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5.27.1 Description
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The ability to quickly locate an individual that is either missing (search and rescue) or is a suspect in an illegal activity (apprehend) is very important for public safety. Statistics show that the quicker a missing person can be found the higher the possibility they can be found in good condition. Similarly for a suspect in an illegal activity, the quicker they can be located the less likely they can hide or commit another illegal activity. These activities can be in both an outdoor environment and indoors.
Leveraging the sensing capability of a 3GPP network and integrating the feature with other 5G capabilities (e.g., metaverse, augmented reality, location, network relay, etc.) can provide a huge advantage to public safety.
In an outdoor example, an elderly person with Alzheimer’s Disease wanders off into the woods and does not know how to return. The longer it takes for public safety to locate this person the more likely they may suffer injuries or medical issues, such as dehydration, cuts, bruises, broken bones, or worse, death. Another example is an individual who robs a bank and escapes into the nearby forest and swamps. The longer it takes to track down and find the individual, the more difficult it becomes and the bigger the risk of them taking hostages, hurting others, or escaping completely.
With the density of base station deployments and with large numbers of 5G enabled UE’s, the 5G coverage includes a large amount of the territory of most countries. These base station signals and the signals of UE’s can also be used to sense the environment for object detection.
Assumptions for this use case:
• Trusted third-party applications for interpreting and presenting the data to public safety is required,
• Devices must be trusted and communicate information togethers; and
• Precision 3-axis location is needed.
An indoor example would be a firefighter entering a building with limited or no visibility using sensing integrated with firefighter heads-up displays/UEs (metaverse/AI/ML) sensing can better allow the firefighters to locate possible people trapped inside and allow them a better view of the rooms as they work through the building.
Indoor 5G coverage can be challenging but leveraging features like UE-to-UE relay, UE-to-Network Relay, UE-to-Network multi-hop relay and UE-to-UE multi-hop relay and allowing the devices to work together can provide good coverage and capability to provide indoor sensing services in this challenging environment.
Assumptions for this use case:
• The use of UE-to-Network Relay and UE-to-UE relay can provide a more reliable connectivity.
• Devices must be trusted and communicate information togethers; and
• Precision 3-axis location is needed.
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5.27.2 Pre-conditions
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1) Operator A’s network supports sensing capability with their base stations and have 3GPP sensing enabled UEs on their network.
2) Local public safety officials have a relationship with Operator A allowing them to access the networks sensing capability and service.
3) Appropriate security and privacy requirements are in place between the operator and the public safety organization.
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5.27.3 Service Flows
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1) Public safety is notified of a need to search for an individual. This could be either a search and rescue, or an apprehend scenario and could involve both indoor and outdoor environments.
2) Operator A’s network is 3GPP sensing enabled and public safety’s UEs are 3GPP sensing enabled.
3) Public safety personnel begin searching for the individual using both traditional methods, UAVs and UE’s with 3GPP sensing capabilities.
4) Depending on coverage there may be a need to leverage indirect network connections.
5) Using the 3GPP sensing data and non-3GPP sensing data public safety can quickly locate the person of interest.
6) The individual is located using the combinations of capabilities.
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5.27.4 Post-conditions
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The individual is located faster than without 3GPP sensing capability. The additional harm that might have occurred to the individual or community is avoided.
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5.27.5 Existing features partly or fully covering the use case functionality
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TBD
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5.27.6 Potential New Requirements needed to support the use case
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[PR.5.27.6-1] The 5G system shall support exposing the information of sensing result (e.g., location, relative location, velocity vectors, relative headings, etc.) to the trusted and secure mission critical applications.
[PR.5.27.6-2] The 5G system shall support mechanisms for combining 3GPP sensing data and non-3GPP sensing data (e.g., body cameras.) depending on location, availability of non-3GPP sensing data, and public safety applications.
[PR.5.27.6-3] The 5G system shall support security and protection of the 3GPP sensing data, non-3GPP sensing data, and sensing results.
[PR.5.27.6-4] The 5G system shall provide a secure sensing service for Mission Critical Services.
[PR.5.27.6-5] The 5G system shall be able to provide the sensing service with the following KPIs:
Table 5.27.6-1 Performance requirements of sensing results for public safety search and rescue or apprehend
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Search and Rescue/Apprehend
Outdoor/Indoor
99
≤ 0.5
≤ 1.0
Pedestrian: ≤1.5
Pedestrian: ≤1.5
3
Horiz: 5
Vert: 5
≤1s
≥10Hz
[≤3]
[≤3]
NOTE: The terms in Table 5.27.6-1 are found in Section 3.1.
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5.28 Use case on Vehicles Sensing for ADAS
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5.28.1 Description
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Advanced Driving Assistance System(ADAS) uses various sensors (Wireless Sensing millimeter wave radar, lidar, monocular / binocular camera and satellite navigation) installed on the vehicle to sense the surrounding environment at any time during the driving process, collect data, identify, detect and track static and dynamic objects, and carry out systematic calculation and analysis in combination with navigation map data, so as to make the driver aware of the possible dangers in advance, and effectively increase the comfort and safety of driving.
Figure 5.28.1-1 ADAS overview
There is an opportunity for 3GPP New Radio (NR) based sensing technologies to be added into ADAS. 5G based wireless sensing service could improve the ADAS reliability and quality.
The ADAS has the map information, the real time location/ trajectory of the car and can assist the car driving, e.g. stop the car for avoiding collision. The car (as 3GPP UE) is equipped with 3GPP NR based sensing technology. When the UE initially accesses the 5G network, the UE is authorized by the 5G network to participate in sensing under the operator’s control. The 3GPP sensing data is from the NR based sensor, and the sensing result is sent to the ADAS system of the car. Collaborating with other non-3GPP sensing devices/technologies, NR based sensing result as input to ADAS could improve the comfort and safety of driving.
The vehicle as a 3GPP UE based sensing sensor is important for automotive use cases, it can operate under network control & complements network-based sensing. Network based sensing alone cannot fully address the automotive use case needs, e.g.:
1. There may be a blockage from the network (base station) to the sensed target.
2. ADAS concerns more on relative positioning other than absolute position. Network based sensing introduces additional errors (due to compounding errors of two separate positions).
3. Automobiles applications may determine sensing priority/performance requirements locally (not visible to gNB).
UE based sensing resources allocations can be under the control of the network (e.g. base station). For UEs inside the coverage of enhanced network for sensing, resources can be directly controlled. Sensing results of the automobiles can be shared via 3GPP connections (Uu or PC5).
It is expected that the 3GPP NR sensing service for ADAS should meet the requirement and level of commercial ADAS radar sensing performance. Based on requirements for existing automotive sensors, the RF based sensing requirements for automotive applications are as the following table.
Table 5.28.1-1 RF based sensing requirements for automotive applications of existing automotive sensors
Parameter
Typical Automotive Radar KPIs
Long Range Radar [10][12][20][50][51][52]
Short Range Radar [10][12][20][52][53][54]
Maximum range
250-300m
(for RCS of 10dBsm with >90%detection probability)
30-100m
Range resolution
10-75cm
5-20cm
Range accuracy
±10 to ±40cm
±2 to 10cm
FOV azimuth
±9-15deg
±60-85deg
Azimuth resolution
1-3deg
3-9deg
Azimuth accuracy
±0.1-0.3deg
±0.3-5deg
Update rate
5 to 20 fps
20 to 50 fps
Max one-way velocity
±50m/s to ±70m/s
±30m/s
Velocity resolution
0.1 –0.6 m/s
0.1 - 0.6 m/s
Velocity accuracy
±0.03m/s to ±0.12 m/s
±0.03m/s to ±0.12 m/s
Although all vehicles on the road are expected to be equipped with NR radio, but not necessarily utilizing them for 100 percent of time. Enabling NR radio based sensing for ADAS can be a better utilization of the capability and radio resources.
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5.28.2 Pre-conditions
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The 3GPP UE in the car has 3GPP subscription and is authorized by the operator to perform sensing.
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5.28.3 Service Flows
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Figure 5.28.3-1 ADAS
1. Tom buys a new car with the latest ADAS equipped.
2. Tom wants to drive the car from home to the company in the morning of a working day. Tom drives from home to the road. The 3GPP NR based sensor in Tom’s car transmits the 3GPP NR signal to the other car(s) in the same road, and receives the reflected signal to detect the distance and speed of the other car(s) to feed to the ADAS in Tom’s car.
3. While the car is driving on the highway, suddenly a car is stopped before the Tom’s car due to an accident, fortunately it is timely detected by the NR based sensor.
4. The NR based sensors send the collision warning to the ADAS, the ADAS stops the car immediately.
5. Finally, Tom’s car avoids a collision accident and leaves the highway safely.
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5.28.4 Post-conditions
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With the safely driving experience provided by ADAS, Tom arrives in the company safely and easily. Tom starts the daily work in the office.
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5.28.5 Existing features partly or fully covering the use case functionality
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There are features of Sidelink positioning for the car moving along the LOS road (e.g., using Sidelink positioning for car ranging on the same road), which requires the participant cars are 3GPP UEs.
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5.28.6 Potential New Requirements needed to support the use case
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[PR 5.28.6-1] The 5G system shall be able to configure and authorize UEs supporting V2X applications to perform sensing.
[PR 5.28.6-2] The 5G system shall be able to collect charging information for UEs supporting V2X applications when performing sensing.
[PR 5.28.6-3] The 5G system shall be able to support the following KPIs:
Table 5.28.6-1 KPIs for Vehicles Sensing for ADAS
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
ADAS
[long range Radar]
Outdoor
[95]
[≤1.3]
NOTE 2
≤0.5
[≤ 0.12]
NOTE 4
N/A
[0.4]
NOTE 5
[≤ 0.6]
NOTE 4
[50]
[≤ 0.2]
[≤ 10]
[<1]
ADAS
[Short range Radar]
Indoor (parking space)/Outdoor
[95]
[≤2.6]
NOTE 3
≤0.5
[≤ 0.12]
NOTE 4
N/A
[0.4]
NOTE 5
[≤ 0.6]
NOTE 4
[20]
[≤ 0.05]
[≤ 10]
[<1]
NOTE 1: The terms in Table 5.28.6-1 are found in Section 3.1.
NOTE 2: Assuming typical max range of 250m, range accuracy of 10cm, azimuth accuracy of ±0.3deg. Positioning accuracy as (Min-Max) within field of view.
NOTE 3: Assuming typical max range of 30m, range accuracy of 2cm, azimuth accuracy of ±5deg.Positioning accuracy as (Min-Max) within field of view.
NOTE 4: Velocity accuracy and resolution is typically reported for the radial velocity (not absolute H/V velocity).
NOTE 5: Range resolution typically reported as 3D ranging distance accuracy.
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5.29 Use case on Coarse Gesture Recognition for Application Navigation and Immersive Interaction
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5.29.1 Description
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As a new way of human-device interface, gesture recognition enables a more intuitive interaction between humans and machines, compared to the conventional text or GUI-based interfaces. Common applications of gesture recognition include touchless control of mobile devices, such as smartphones, laptops, and smart watches. Compared to other use cases, such as sports monitoring or sleep monitoring, gesture recognition requires higher resolution, higher update rate, and lower latency, which makes it more challenging in terms of resource utilization and processing complexity.
Gesture recognition identifies motions and postures of human body parts, such as head, hands, and fingers. As shown in Figure 5.29.1-1, gesture recognition can be applied to various applications such as human motion recognition, keystroke detection, sign language recognition and touchless control.
Gesture/motion/posture recognition
• Human motion recognition
• Keystroke detection
• Touchless control
• Sign language recognition
Figure 5.29.1-1 Gesture Recognition
In this use case, we focus on application of gesture recognition for touchless control and immersive (i.e. XR) application.
For touchless control, the identified gestures are then interpreted to specific behaviours or operations of the device, including locking/unlocking a screen, increasing/decreasing volume, and navigating forward/backward web pages.
For XR application, the position tracking and mapping of the human body is a basic requirement that permeates XR application to provide the immersive experience [55]. Hereby a variety of sensors are integrated in the XR devices to measure the movement of the human body (e.g., head, eye, hand, arm, etc) in order to simulate normal human mimicry. Besides, the hand tracking goes beyond the simulation and enables a natural and intuitive way of the interaction between the human and the machine compared with the use of the physical controller. The gesture recognition and hand tracking becomes a vital function in XR applications, especially when the human and the controlled object stay in physical and virtual world separately.
For both touchless control and XR application, NR-based RF sensing is suitable for gesture recognition because certain RF signals can detect coarse body movements and the RF signals are not susceptible to the ambient illumination condition and occlusions in the environment. In addition, NR-based RF sensing allows for coarse hand tracking in a lower-complexity and economic manner.
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5.29.2 Pre-conditions
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There are two roommates, Jose and Bob, both of whom subscribed to MNO A, which has deployed RAN entity (e.g., an indoor base station) supporting NR-based sensing.
Jose subscribes to the touchless user interface service and his mobile device has NR sensing capability.
Bob subscribes to the immersive interaction service, provided by both MNO A and XR application (e.g. game, sports training) provider AppX on the access rights of the interaction information relevant to Bob’s hands. Bob’s UEs (e.g. smartphone, XR device such as headset) are capable of NR-based sensing and Bob’s XR device also have other sensors (e.g. IMU) embedded on the device.
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5.29.3 Service Flows
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Social Media Navigation service with Gesture Recognition
Step 1: Sitting in this room, Jose is reading through social media posts using his smartphone, to navigate to either the previous or next posts. Jose waves his hand in the air from left to right or from right to left.
Step 2: Jose’s smartphone detects the hand gesture using 5G wireless sensing using the RAN entity, UE or both. The smartphone and RAN entity can send sensing data (with extracted gesture features such as range and Doppler of the detected gesture) to the 5G network.
Step 2b: Alternatively, the Jose’s smartphone detects the hand gesture using sensing signals and process those signals to generate 3GPP sensing data and sensing results. Also, the 3GPP sensing data can be further combined and processed with non-3GPP sensing data at the UE to generate a combined sensing result.
Step 3: 5G network then aggregates and processes the information collected from the UE and RAN entity to detect Jose’s gesture and provides the sensing results to Jose’s smartphone which is shared with the social media application and used for the navigation of the post.
Immersive interaction service with Gesture Recognition
Step 1: Bob launches the XR application in his room, at which moment one avatar is generated to represent him in the virtual world of the XR application, and the immersive interaction service is activated as shown in Fig 5.29.3-1.
Step 2: When Bob sees a basketball flying toward him, he catches the ball and throws it back. The characteristics of the gesture (e.g. range, doppler shift) are detected using NR sensing signals of UEs, the RAN entity or both.
Step 3: 5G network (e.g. 5GC) collects the 3GPP sensing data from UEs, RAN entity or both, process the data and exposes the sensing result (e.g. 3D position, velocity) to XR application. In parallel, the non-3GPP sensing data obtained from the XR device is transmitted to the application platform transparently through UE to 5GC.
Step 3b: Alternatively, 5G network can combine and process the 3GPP sensing data and non-3GPP sensing data obtained from XR device for the same posture, and then expose the combined sensing result(e.g. 3D position, velocity) with the contextual information(e.g. time) to XR application platform.
Step 4: The gesture and hand movement are recognized and the basketball bounces to another direction as a sensing result. The entire course will be presented on Bob’s headset as that the basketball is caught by one hand of Bob’s avatar and thrown back.
Figure 5.29.3-1 Hand Tracking in XR applications
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5.29.4 Post-conditions
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Due to the RF sensing capability in Jose’s mobile device and a nearby RAN entity, Jose’s gestures are detected and used to navigate the social media posts on his phone.
Similarly, due to the RF sensing capability in Bob’s smartphone, XR device and a nearby RAN entity, Bob’s coarse gesture and the motion of his hands will be recognized and tracked correctly. The avatar in XR application will show the correct gesture and execute the correct action triggered by the gesture.
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5.29.5 Existing features partly or fully covering the use case functionality
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None.
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5.29.6 Potential New Requirements needed to support the use case
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[PR 5.29.6-1] The 5G system shall be able to provide sensing with the following KPIs:
Table 5.29.6-1 Performance requirements of sensing results for gesture recognition
Scenario
Sensing service area
Confidence level [%]
Motion rate accuracy
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Gesture recognition
Indoor
95
N/A
0.2
NOTES 4 and 5
0.2
NOTES 4 and 5
0.1
0.1
0.375
NOTES 1 and ,2 and 5
0.3
5 – 50
NOTE 3
≤0.1
≤5
≤5
NOTE 1: Due to the resolution allowed by the KPIs above, the use of pre-defined gestures for determining a user’s basic hand gestures is assumed.
NOTE 2: Assuming bandwith of 400 MHz, Tthe range resolution can beis determined using the formular C/2*B where C is the speed of light, B is the bandwidth (assuming B = 400 MHz which is supported in 5G mmWave networks.)
NOTE 3: The value is derived from TS22.261 [33] clause 7.11 about max allowed end-to-end latency for immersive multi-modal KPIs.
NOTE 4: Positioning accuracy KPIs are based on minimum supported positioning accuracy in 5G systems [TS 22.261]
NOTE 5: By combining non-3GPP sensing data (if available) with 3GPP sensing data, some of these KPIs may be improved.
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5.30 Use case on sensing for automotive manoeuvring and navigation service when not served by RAN
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5.30.1 Description
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Consider the scenario defined in section 5.8, where NR wireless sensing is utilized to assist automotive manoeuvring, i.e. sensing results play an important role in making the manoeuvring decisions. However, in this section the vehicles are not served by RAN when the Sensing activity is expected to occur, where UE is not served by RAN and therefore RAN entities are unable to be involved. UEs performing the sensing measurement process in this scenario have to be able to operate when UE is not served by RAN.
5.30.2 Pre-conditions
Refer to 5.8, where Bob’s vehicle determines the need for sensing service. Bob’s vehicle integrates a UE supporting V2X application i.e., Bob’s vehicle supports V2X. Sensing can be performed by 5G Wireless sensing. Unlike in section 5.8, the vehicles are not served by RAN and perform 5G Wireless sensing without help of the network entities. Bob’s vehicle has been provisioned by his home operator to be able to perform 5G Wireless sensing when not served by RAN. There may be other vehicles or RSU (road side unit) helping the 5G Wireless sensing of the Bob’s vehicle.
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5.30.3 Service Flows
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1. Bob is driving from urban to rural countryside. As his vehicle is operational, in motion, and attempting to assist his driving using ADS, Bob’s vehicle is performing sensing using 5G Wireless sensing.
2.Bob drives his vehicle outside the coverage area of its mobile network. The 5G Wireless sensing continues providing assistance to Bob’s driving.
3. Bob’s vehicle approaches Joe’s vehicle.
4. Bob’s vehicle becomes aware of Joe’s vehicle by Bob’s onboard Sensing receiver(s) receiving 5G Wireless sensing reflected by Joe’s vehicle.
5. Bob’s vehicle applies this new object information to the local ADS function to ensure a safe, collision-free, drive.
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5.30.4 Post-conditions
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Bob’s vehicle is able to drive with high reliability by utilizing 5G sensing service when not served by RAN.
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5.30.5 Existing features partly or fully covering the use case functionality
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None.
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5.30.6 Potential New Requirements needed to support the use case
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[PR 5.30.6-1] The 5G system shall be able to provide mechanisms for an MNO to configure UEs supporting V2X application for 5G Wireless sensing operation when not served by RAN.
[PR 5.30.6-2] Subject to regulation, the 5G system shall enable UEs supporting V2X application to perform 5G Wireless sensing when not served by RAN using the allowed ITS spectrum and unlicensed spectrum.
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5.31 Use case on blind spot detection
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5.31.1 Description
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Blind spot detection reduces the risk of accidents during lane changes by monitoring the dangerous blind spot area [26]. The blind spot area is a typically a moving target area that changes when car moves if we take the road infrastructure as reference point. Currently, the blind spot detection system operates via a variety of external sensors located on a car’s bumpers and wing mirrors, which can detect if a person or vehicle enters your blind spot, notifying you via an audible or visual cue - typically, a warning light located in the car’s wing mirror.
Figure 5.31.1-1: blind spot detection system, a moving target area.
Wireless sensing technology can be utilized to detect obstacles that presents in car’s blind spot area:
• Case I: Base stations on the roadside are already used to provide 5G coverage for communication, and the radio signals that are reflected can be used to sense the blind spot area of a car.
• Case II: Base stations on the roadside are already used to provide 5G coverage for communication, and the radio signals that are received by the UE (i.e. the car is a 3GPP UE, or there is a 3GPP UE such as smartphone on the car) can be used to sense the blind spot area of the car.
Any obstacle that presents in the car’s blind spot area, no matter the obstacle is moving (e.g. car, motorcycle, walking human, animal etc) or static (building, tree), will affect the reflected/received signals. By deriving the characteristics of the affected signals, obstacle can be detected, and danger can be avoided. It is convenient to utilize current deployed 5G network system to achieve this blind spot detection.
This use case is to reuse 5.8 to describe the blind spot detection aspects.
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5.31.2 Pre-conditions
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MNO provides blind spot detection sensing service to different kinds of subscribers:
- Bob’s car is a 5G UE and subscribes to this sensing service. His car has NR-based sensing technology and capabilities such as NR-based sensing capabilities, sensing processing capabilities are also provided to the MNO.
- Juan’s car is not a 5G UE, but his 5G UE (smartphone) subscribes to this sensing service, where an application is installed on the 5G UE.
- Alex’s car is not a 5G UE, an application installed on his car subscribes to this sensing service.
Laura has no subscription to the blind spot detection sensing service.
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5.31.3 Service Flows
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Step 1: Bob, Juan, Alex and Laura are friends and driving together to Alps skiing resort. At 8:00am, Bob starts from Street A, Juan and Alex start from Street B and Laura start cars from Street C. Bob, Juan and Alex trigger the blind spot detection sensing service separately.
Step 2: When received the service request, 5G system discovers and configures sensing transmitter(s) and sensing receiver(s) to track and monitor the moving car’s blind spot area (from sensing transmitter’s perspective), e.g.:
• Bob’s car is a 5G UE and has NR-based sensing capabilities, 5G system configures base station(s) as sensing transmitter and Bob’s car as the sensing receiver. The moving blind spot area is tracked by Bob’s 5G UE.
• Juan’s car and Alex’s car are not 5G UE. 5G system configures base station(s) as sensing transmitter and sensing receiver. Juan’s car’s moving blind spot area and Alex’s car’s moving blind spot area are separately tracked by 5G base station.
Step 3: 3GPP sensing data is collected by sensing receiver and transferred to the network sensing processing entity to derive the sensing result, which is then exposed to the service consumer to fetch out whether there is obstacle presence in the blind spot area, e.g.
• Bob’s car is a 5G V2X UE and has processing capabilities, 5G system authorizes 5G UE as sensing processing entity.
• Juan’s car is not a 5G V2X UE, but Juan has his smartphone (5G UE) carried in car, 5G system authorizes 5G UE as sensing processing entity.
• Alex has no 5G UE on board, 5G network processes the 3GPP sensing data to derive sensing result.
Step 4: A car is moving very fast from Street A to Street B to Street C and presents sequentially in Bob’s, Juan’s Alex’s and Laura’s blind spot area.
• Bob is changing lane, the moving car is detected and Bob safely changed lane.
• Juan and Alex turning left, the moving car is detected and they safely turned right.
• Laura is overtaking a truck, the moving car suddenly presents in the blind spot area, Laura forgets the over-shoulder view and hits the moving car.
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5.31.4 Post-conditions
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Bob, Juan and Alex drive safely to the Alps skiing resort and enjoy their holiday thanks to the blind spot detection sensing service.
Laura is in hospital.
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5.31.5 Existing features partly or fully covering the use case functionality
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None.
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5.31.6 Potential New Requirements needed to support the use case
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[PR 5.31.6-1] The 5G System shall be able to provide sensing service to track a moving target sensing service area.
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5.32 Use case of integrated sensing and positioning in factory hall
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5.32.1 Description
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Autonomous Mobile Robots (AMR)s and automated guided vehicle (AGV) are enabling solutions for a smart factory environment, in which a diversity of logistic tasks are done with an autonomous and efficient implementation, with minimal direct human engagement. In order to achieve a safe and efficient operation to serve a desired goal (e.g., transfer of construction materials with minimal risk, delay and energy consumption), a command center may take over the task of collecting the information of the involved facilities and performing a coordinated planning of the device operations. Nevertheless, a safe and efficient operation of the mobile devices may be only achieved on the condition of an accurate awareness of the environment (e.g., obstacles, humans) and the device positioning/tracking information (e.g., AGV/AMR position and velocity).
In view of the above, the 5G system shall serve the implementation of a smart factory by means of a reliable data connectivity (e.g., the low-latency and reliable AGV/AMR to command center connection) and positioning of the involved AGV/AMR UE devices. Furthermore, the 5G system sensing services can be utilized to augment the environment awareness by means of detecting and locating the non-connected objects (e.g., obstacles such as trash box, or safety-sensitive objects such as human, etc.).
In addition to the detection of the non-connected objects, the 5G system sensing enables a higher positioning and tracking accuracy of a target UE at the 5G system, by means of augmenting the positioning and sensing capabilities [27]. In case of an AMR/AGV, the positioning measurement of a device can be augmented at the 5G system with the 3GPP sensing data obtained from the reflections of the sensing signal from the AMR/AGV physical body, in the interest of a higher environment awareness and positioning accuracy, as well as the additional information obtained via sensing of the AMR/AGVs (e.g., orientation of an AMR). In particular, when both 5G system sensing and positioning services are activated, the same 5G system nodes (e.g., sensing Tx nodes) and 5G system signals (e.g., positioning or sensing signals) can be reused to efficiently generate and process the desired sensing and positioning measurements, see Figure 5.32.1-1 as an example of a joint sensing and positioning of a UE.
Figure 5.32.1-1 The 5G system obtains position estimate of a UE, utilizing 3GPP sensing data of the UE obtained from the 5G wireless sensing service, in combination with the positioning measurement of the UE
The obtained higher positioning accuracy of an AGV/AMR is particularly valuable for coordination of multiple AGVs and AMR with indeterministic movement paths, wherein the situations involving sudden break and/or velocity change may lead to a high delay, damage risk, and interruption energy loss.
5.32.2 Pre-conditions
Multiple AMR/AGVs are deployed in a factory hall belonging to the company X. The AMR/AGVs are coordinated by a command center to perform a collaborative construction task. The command center coordinates the AMR/AGVs’ movements to improve safety and to avoid energy loss (due to an AMR/AGV break) and delay as much as possible.
To facilitate this, the factory hall is equipped with the 5G system sensing services provided by the Mobile Network Operator (MNO) A. Moreover, the AMR/AGVs are equipped with the 5G system communication and positioning modules.
The company X has provided the MNO with the physical type/characteristics of the deployed AMR/AGVs, as well as the installed camera data of the factory hall to assist detection and positioning of the AMR/AGVs via sensing.
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5.32.3 Service Flows
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Step 1: [AMR/AGV is deployed to deliver goods]
AMR/AGV Y is assigned with a task for delivering needed material to a construction site within the factory hall. The AMR/AGV is loaded with the materials and departs from its initial location.
Step 2: [AMR position is obtained via 5G system positioning]
AMR/AGV moves from its initial position towards the construction site. The position information of the AMR/AGV is obtained by the MNO A via the 5G system positioning module of the AMR/AGV and reported to the command center.
The command center determines that the provided positioning accuracy of the MNO A is sufficient since the AMR/AGV currently moves in the low-traffic area of the factory hall. Based on the received positioning information of the AMR/AGV, the command center recommends that AMR/AGV keeps its velocity towards the construction site.
Step 3: [AMR/AGV position is obtained via a joint 5G system sensing and positioning]
As the AMR/AGV moves towards the construction site, more objects (other AMR/AGVs, humans, tools) appear in the vicinity. The command center identifies that the AMR/AGV is now in a high-traffic area and a higher positioning accuracy is desired.
The command center requests the MNO A to activate 5G system sensing service during the 5G system positioning service for positioning of the AMR/AGV UEs.
MNO A activates sensing of the desired AMR/AGV areas to enhance positioning of the AMR/AGV devices. MNO A identifies UE and/or gNBs capable of sensing in the vicinity of the AMR/AGV and starts sensing measurement process jointly with the 5G positioning measurements of the AMR/AGVs. The 5G network obtains 3GPP sensing data of the identified nodes and generates a high accuracy position estimate and/or additional sensing information of the AMR/AGVs, based on the collected 3GPP sensing data in addition to the AMR/AGV’s positioning measurements. The obtained positioning estimate of the AMR/AGV is reported to the command center.
Based on the obtained high-accuracy positioning information of the AMR/AGVs the command center adjusts the velocity of the AMR/AGVs.
Step 4: [AMR reaches the construction site]
The AMR/AGV reaches the construction site and offloads the goods.
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5.32.4 Post-conditions
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Thanks to the 5G system based sensing service enabling an enhanced AMR/AGV positioning and sensing of the environment, the involved AMR/AGVs are coordinated to arrive at their destination with minimal risk and interruption loss.
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5.32.5 Existing feature partly or fully covering use case functionality
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A UE equipped with 5G positioning module may obtain positioning information of the UE based on the 5G positioning services. Moreover, the 5G system sensing services shall support detection and positioning of an object. Nevertheless, interpretation of an object’s position as a UE’s position (e.g., among multiple detected objects), as well as a joint positioning and sensing of a UE device as a physical object by the 5G system (when both 5G system services are available to the UE) has not been enabled by the current requirements.
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5.32.6 Potential New Requirements needed to support the use case
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[PR 5.32.6-1] Based on operator’s policy, the 5G system may provide a mechanism for a trusted third party to provide sensing assistance information about a sensing target.
[PR 5.32.6-2] The 5G system shall be able to provide sensing results with the following KPIs:
Table 5.32.6-1 Performance requirements of the sensing results exposed to the third party
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Indoor Factory
100 m2
99
[≤0.5]
N/A
0.5
N/A
[0.5]
[0.5]
≤100
0.1
N/A
N/A
NOTE: The terms in Table 5.32.6-1 are found in Section 3.1.
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6 Considerations
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6.1 Considerations on confidentiality, integrity and privacy
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6.1.1 General
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When introducing sensing technology, new aspects on confidentiality, integrity, and privacy need to be considered, to ensure that these aspects are considered already when proposing service requirements.
For instance, with sensing technology by-standers can be affected in a completely new way, previously only UEs have been able to be tracked but now sensing capabilities may enable tracing and potentially identification of anything in the environment, including humans that do not carry a UE, or any objects. This has implications for privacy. Obviously humans should have a right to privacy.
For privately owned areas, respective permission is required for sensing operation from such as the homeowner for in-home sensing or the building management for the in-building sensing.
For public areas, such as a public road, park and airport, it is required to obtain the permission of the respective public area management.
It is important to have the user consent before the network uses UEs in providing sensing service. If the sensing results and user ID are brought together for further processing, user consent is also needed.
Of course, factors such as resolution, updating frequency, and type of application influence the security implications.
Requirements to minimize the risk of unwanted usage and awareness of the usage needs to be considered in stage 1. These are captured in the next chapter.
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6.1.2 Potential New Requirements
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A set of general new requirements can be identified:
[PR 6.1.2-1] The 5G system shall limit sending the sensing results only to third party authorized to receive that sensing results.
[PR 6.1.2-2] The 5G system shall support encryption and integrity protection of the sensing result, to protect the data inside the 5G system and when used.
[PR 6.1.2-3] The 5G system shall support appropriate level of sensing for both situations where consent can be obtained from the sensing targets, and where it cannot.
[PR 6.1.2-4] Subject to regulation, the 5G system shall obtain user consent when sensing results and user identification are brought together for further processing.
6.2 Considerations on Regulatory, Mission Critical and other priority services
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6.2.1 General
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The sensing operation in Operator’s network can support commercial services (e.g. use case described in section 5.8 on sensing assisted automotive manoeuvring and navigation). There could be areas where the network resources are limited and prioritization (according to operator’s decision) would be needed among the resources used for sensing service and resources used for other services (e.g. communication service).
In addition, sensing operation could also be used to support services such as MCS (e.g. public safety, Utilities, Railways) and MPS with requirements for priority treatment. The 5G system can provide flexible means for priority treatment to the users of sensing services subject to regional/national regulatory rules and operator policy.
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6.2.2 Potential New Requirements
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[PR 6.2.2-1] Subject to regulation and operator’s policy, 5G system shall provide prioritization among sensing services.
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7 Consolidated potential requirements and KPIs
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7.1 Consolidated functional requirements
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7.1.1 General
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Table 7.1.1-1 General Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 7.1.1-1
The 5G system shall be able to provide 5G wireless sensing service in a sensing service area location using sensing transmitters and sensing receivers.
PR 5.2.6-1
PR 5.9.6-1
PR 5.11.6-1
P.R 5.14.6-1
PR 5.1.6-2
CPR 7.1.1-2
Subject to regulation and operator policy, the 5G network shall be able to activate, configure, and deactivate 5G wireless sensing based on parameters such as location and network conditions (e.g. network load).
P.R 5.14.6-3
P.R 5.13.6-7
PR 5.8.6-6
CPR 7.1.1-3
Subject to user consent, regulation, and operator’s policy, the 5G system shall be able to collect non-3GPP sensing data from authorized non-3GPP sensors and securely provide it to 5G network.
PR.5.4.6-1
PR 5.21.6-2
CPR 7.1.1-4
The 5G system shall support continuity for 5G wireless sensing service (e.g. for sensing a moving object).
PR 5.23.6 -1
PR 5.18.6.1
CPR 7.1.1-5
Subject to operator’s policy, the 5G System shall be able to provide the 5G wireless sensing service in case of roaming.
PR 5.24.6-1
CPR 7.1.1-6
Subject to user consent, regulation, and operator’s policy, the 5G system should support the combination of the 3GPP sensing data and non-3GPP sensing data to derive a combined sensing result.
PR 5.21.6-3
PR 5.4.6-4
PR 5.27.6-2
CPR 7.1.1-7
Subject to regulation and operator’s policy, 5G network shall provide prioritization among 5G wireless sensing services (e.g. prioritizing between communication and sensing services).
PR 6.2.2-1
CPR 7.1.1-8
The 5G system shall be able to enable UEs without 5G coverage to use unlicensed spectrum to provide 5G wireless sensing service.
PR 5.25.6-3
PR 5.1.6-5
PR 5.25.6-2
CPR 7.1.1-9
Subject to regulation, the 5G system shall enable UEs supporting V2X application to perform 5G Wireless sensing when not served by RAN using the allowed ITS spectrum and unlicensed spectrum.
PR 5.30.6-2
CPR 7.1.1-10
The 5G system shall be able to provide sensing service to detect, identify and/or track one or more objects (e.g., UAVs, birds) and their environment.
Note: There is a need to clarify “identify” during normative phase.
PR 5.12.6-2
PR 5.10.6-1
CPR 7.1.1-11
Based on operator’s policies, operator’s control and regulation, the 5G system shall be able to collect 3GPP sensing data from sensing receivers for processing.
PR 5.2.6-4
PR. 5.3.6-1
PR. 5.6.6-1
PR. 5.7.6-1
PR 5.8.6-2
PR.5.1.6-4
PR 5.9.6-3
PR 5.11.6-2
PR 5.13.6-3
PR 5.17.6-4
PR 5.2.6-5
PR. 5.3.6-2
PR 5.13.6-1
PR 5.21.6-1
CPR 7.1.1-12
Subject to operator’s policy, the 5G system may be able to use sensing assistance information to derive the sensing result.
PR 5.22.6-2
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7.1.2 Configuration and authorization
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Table 7.1.2-1 Configuration and authorization Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 7.1.2-1
The 5G system shall be able to provide mechanisms for an MNO to configure UEs supporting V2X application for 5G Wireless sensing service when not served by RAN.
PR 5.30.6-1
CPR 7.1.2-2
Subject to regulation and operator’s policies, the 5G network shall be able to configure and/or authorize or revoke authorization of sensing service, sensing transmitter(s) and sensing receiver(s) for 5G wireless sensing service.
NOTE: Such configuration and authorization can be based on sensing transmitter or sensing receiver location, specific time, sensing duration, sensing accuracy, target sensing geographical area, establishing of communication to transfer sensing data, etc.
PR 5.2.6-3
PR 5.15.6-1
PR 5.9.6-2
PR 5.20.6-2
PR 5.8.6-1
PR 5.8.6-4
PR 5.28.6-1
PR 5.1.6-1
PR 5.5.6-3
PR 5.11.6-4
P.R 5.13.6-8
P.R 5.10.6-2
PR 5.17.6-3
PR 5.24.6-2
CPR 7.1.2-3
Based on location, the 5G network shall be able to ensure that sensing transmitters and sensing receivers use licensed spectrum only in network coverage and under the full control of the operator who provides the coverage.
NOTE 1: The above requirement does not apply for public safety and V2X networks with dedicated spectrum, where 5G wireless sensing can be allowed out of coverage or in partial coverage as well.
PR 5.25.6-1
PR 5.20.6-1
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7.1.3 Network exposure
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Table 7.1.3-1 – Network exposure Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 7.1.3-1
Subject to operator’s policy, the 5G network shall be able to provide secure means to report sensing result to a trusted third-party requesting information about a target object when specific requested conditions are met.
NOTE: These conditions could be e.g. the target object distance from the restricted area border or entering restricted area.
PR 5.13.6-6
CPR 7.1.3-2
Subject to operator’s policy, the 5G network shall be able to provide secure means to enable trusted third-party to request discovering a sensing group in the proximity of the UE that is requesting a 5G wireless sensing service from application server.
PR 5.19.6-2
CPR 7.1.3-3
Subject to operator’s policy and regulation, the 5G network shall provide secure means for a trusted third-party to request 5G wireless sensing service based on specific parameters (e.g. refresh rate, period of time, sensing KPIs, geographical location) and to receive the corresponding sensing results.
PR 5.11.6-3
PR 5.13.6-4
PR 5.12.6-3
PR 5.12.6-5
PR 5.12.6-4
PR 5.5.6-1
PR 5.5.6-2
PR 5.25.6-4
PR 5.2.6-6
PR 5.9.6-4
PR 5.7.6-2
PR 5.15.6-2
PR 5.14.6-2
PR 5.10.6-3
PR 5.13.6-5
PR 5.22.6-1
PR 5.27.6-1
PR 5.1.6-3
PR 5.4.6-2
PR 5.4.6-3
CPR 7.1.3-4
Subject to operator’s policy, the 5G system shall be able to provide secure means for a trusted third-party to receive sensing results with contextual information.
PR 5.8.6-5
PR 5.3.6-3
CPR 7.1.3-5
Subject to user’s consent, regulation and operator’s policy, the 5G network may provide secure means to expose to a trusted third-party the combined sensing result derived from the joint processing of the 3GPP sensing data and non-3GPP sensing data.
PR 5.21.6-4
CPR 7.1.3-6
Subject to operator’s policy, the 5G network may provide secure means for the operator to expose information towards trusted third-party on whether a given sensing service is available and the estimated quality of the given service for a certain geographic area and time.
PR 5.2.6-8
PR 5.10.6-5
PR 5.18.6-3
CPR 7.1.3-7
Subject to operator’s policy, the 5G network may enable secure means for a trusted third party to provide sensing assistance information.
PR 5.32.6-1
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7.1.4 Security
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Table 7.1.4-1 Security Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 7.1.4-1
The 5G system shall provide a mechanism to protect identifiable information that can be derived from the 3GPP sensing data from eavesdropping.
PR 5.16.6-1
CPR 7.1.4-2
The 5G system shall limit the exposure of the sensing results only to third party authorized to receive that sensing results.
PR 6.1.2-1
CPR 7.1.4-3
The 5G system shall support encryption, integrity protection, privacy of the 3GPP sensing data, non-3GPP sensing data and sensing results, to protect the data inside the 5G system.
PR 5.27.6-4
PR 5.27.6-3
PR 5.23.6 -2
PR 6.1.2-2
CPR 7.1.4-4
The 5G system shall support appropriate sensing KPIs of 5G wireless sensing for both situations where consent can be obtained from the sensing targets, and where it cannot.
PR 6.1.2-3
CPR 7.1.4-5
Subject to regulation and user’s consent, the 5G network may associate sensing results and identity of the user together for further processing for a sensing target that has a UE and the UE is subscribed in the same network.
PR 6.1.2-4
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7.1.5 Charging
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Table 7.1.5-1 Charging Consolidated Requirements
CPR #
Consolidated Potential Requirement
Original PR #
Comment
CPR 7.1.5-1
The 5G system shall be able to support charging for the 5G wireless sensing service (e.g. considering sensing KPIs, duration).
PR 5.20.6-4
PR 5.2.6-7
PR 5.28.6-2
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7.2 Consolidated potential KPIs of sensing results
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Scenario
Sensing service category
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency
[ms]
Refreshing rate
[s]
Missed detection
[%]
False alarm
[%]
Example Services
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Object detection and tracking
1 (use cases 5.1; 5.13 – level1)
Object to be detected indoor: Human, object to be detected outdoor: UAV
95
10
10
N/A
N/A
10
NOTE 2
5
NOTE 3
1000
1
5
2
intruder detection in smart home,
UAV intrusion detection
2 (use cases 5.13 – level2, 5.6, 5.14)
Object to be detected outdoor:
Human, UAV
95
2
5
1
N/A
1
NOTE 2
1
NOTE 3
1000
0.2
0.1 to 5
5
UAV flight route intrusion detection,
intruder detection in surroundings of smart home, tourist spot monitoring
3 (use cases 5.2, 5.7, 5.10, 5.11, 5.12, 5.23)
Factory (100m2), crossroad, highway, railway [air]
NOTE 4
Object to be detected: Animal, Human, UAV, Vehicle
95
1
1
1
NOTE 5
1
1
NOTE 5
NOTE 8
1 x 1 NOTE 9
100
NOTE 6
1000
NOTE 10
5000 for detection in highway
0.05 to 1
NOTE 11
2
2
pedestrian/animal intrusion detection on a highway/railway,
sensing at crossroads with/without obstacle,
UAV flight trajectory tracing
UAV collision avoidance,
AMR collision avoidance in smart factories
4 (use cases 5.20, 5.22, 5.25, 5.27, 5.32)
factor and public safetyy, indoor/outdoor
Object to be detected: Animal, Human, UAV, AGV/AMR, Vehicle
99 for public safety, otherwise, 95
0.5
0.5
Pedestrian: 1.5,
Vehicle: 15, otherwise: 0.1
Pedestrian: 1.5, otherwise: N/A
0.5m
factories: 0.5 x 0.5
100 to ~5000
0.1
1
3
Parking Space Determination,
UAVs/vehicles/pedestrians detection near Smart Grid equipment (NOTE 7),
immersive experience based on sensing,
integrated sensing and positioning in factory hall, public safety search and rescue or apprehend
5 (use cases 5.28)
ADAS
Object to be detected: Vehicle
95
short-range radar:r 2.6
Long range radar:1.3
0.5
0.
N/A
0.4
0.6
Short range radar: 20;
Long range radar:50
Short range radar: 0.05;
Long range radar: 0.2
10
1
ADAS
Environment monitoring
6 (use cases 5.3 and 5.5.)
Rainfall monitoring and flooding
NOTE 14
Object to be detected: Rain
95
10
0.2
NOTE 15
N/A
N/A
N/A
N/A
60000
1<10min,
0.1 to ~5
3
rainfall monitoring,
flooding monitoring
Motion monitoring
7 (use cases 5.15, 5.24)
Indoor human motion -sleep monitoring NOTE 12, sports monitoring NOTE 13,
95
N/A
N/A
N/A
N/A
N/A
N/A
60000
60
5
5
sleep monitoring,
sports monitoring
8 (use case 5.29)
Hand gesture recognition
95
0.2
0.2
0.1
0.1
0.375
0.3
5 to ~50
0.1
5
5
Hand gesture recognition
NOTE 1: The terms in Table 7.2-1 are found in Section 3.1.
NOTE 2: To detect the UAV existance (e.g., for intrusion detection), the sensing resolution of distance is 10m [25].
NOTE 3: To detect the UAV existence, the sensing resolution of velocity is 10m/s [25].
NOTE 4: The typical size (Length x Width x Height) of UAV is 1.6m x 1.5m x 0.7m, the typical size of pedestrian is 0.5m x 0.5m x 1.75m, and the typical size of engineering vehicle is 7.5m x 2.5m x 3.5 m.
NOTE 5: The KPI values for UAVs are sourced from [25] and [40] and for factories are sourced from [47].
NOTE 6: The value 100 ms is sourced from [28] and is valid for sensing at crossroads.
NOTE 7: The safe distance between pedestrian/vehicle and transmission station/line is 0.7m/0.95m [46]. The size of the park of Smart Grid depends on the real environment.
NOTE 8: To track the UAV flying (e.g., for collision detection and warning), the sensing resolution of distance is 1m [25].
NOTE 9: To track the UAV flying, the sensing resolution of velocity is 1m/s [25].
NOTE 10: To realize 1m granularity tracking, when the velocity resolution is 1 m/s, the maximum corresponding sensing service latency is 1s.
NOTE 11: Commercially available Detect and Avoid (DAA) radar systems for small Unmanned Aircraft Systems (UAS) have an approximate 1Hz scan rate [40].
NOTE 12: Additional KPI on human motion rate accuracy of 2 times/min (0.033 Hz).
NOTE 13: Additional KPI on human motion rate accuracy of 3 times/min (0.05Hz) and 4 times/min (0.07 Hz)
NOTE 14: Rainfall estimation accuracy is1 mm/h[39] and describes the closeness of the measured rainfall estimation to its true rainfall value.
NOTE 15: This value is for the water level. Description related to NOTE in clause 5.5.1 suggests 0.01 m. [≤0.2] is derived from the water level where people feel difficulty in walking.
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8 Conclusion and recommendations
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This TR analyses a number of use cases for integrated sensing and communication enabled by the 5G system. The potential new requirements for each use case are compiled into a set of potential consolidated requirements, including functional requirements and performance requirements, wherein a set of KPIs are defined. Clause 7 contains consolidated potential requirements and KPIs for 5G wireless sensing service. It is recommended that these be considered for normative phase. Annex A (informative): Change history Change history Date Meeting Tdoc CR Rev Cat Subject/Comment New version 5.2022 SA1#98e S1-221249 - - - Initial Skeleton 0.0.0 5.2022 SA1#98e - - - Output of approved pCRs from SA1 #98e Includes: S1-221250; S1-221251; S1-221252 0.1.0 9.2022 SA1#99e - - - Output of approved pCRs from SA1 #99e. Includes S1-222300; S1-222301; S1-222302; S1-222303; S1-222304; S1-222305; S1-222306; S1-222307; S1-222308; S1-222309; S1-222310; S1-222311; S1-222312; S1-222313; S1-222314; S1-222315; S1-222316; S1-222317; S1-222318; S1-222319; S1-222320; S1-222321; S1-222322 0.2.0 11.2022 SA1#100 - - - Output of approved pCRs from SA1#100. Includes: S1-223333; S1-223484; S1-223485; S1-223061; S1-223716; S1-223577; S1-223494; S1-223495; S1-223496; S1-223578; S1-223498; S1-223579; S1-223580; S1-223701; S1-223690; S1-223730; S1-223731; S1-223590; S1-223592; S1-223488; S1-223604; S1-223606; S1-223607 0.3.0 03.2023 SA1#101 - - - Output of approved pCRs from SA1#101. Includes: S1-230600; S1-230601; S1-230549; S1-230692; S1-230693; S1-230808; S1-230798; S1-230639; S1-230696; S1-230558; S1-230539; S1-230697; S1-230647; S1-230648; S1-230538; S1-230121; S1-230177; S1-230547; S1-230649; S1-230541; S1-230626; S1-230698; S1-230754; S1-230755; S1-230653 0.4.0 03/2023 SA#99 SP-230219 - - - Clean-up by MCC for presentation to SA 1.0.0 05/2023 SA1#102 - - - Output of approved pCRs from SA1#102. Includes: S1-231421; S1-231306; S1-231759; S1-231742; S1-231478; S1-231481; S1-231482; S1-231428; S1-231483; S1-231135; S1-231681; S1-231432; S1-231307; S1-231237; S1-231375; S1-231449; S1-231274; S1-231450; S1-231437; S1-231793; S1-231811; S1-231794 1.1.0 06/2023 SA#100 SP-230506 - - - Clean-up by MCC for approval by SA 2.0.0 06/2023 SA#100 SP-230506 - - - Raised to v.19.0.0 by MCC following approval by SA 19.0.0 2023-09 SA#101 SP-231013 0014 F Removing editor s notes 19.1.0 2023-09 SA#101 SP-231013 0009 1 F Update of definitions 19.1.0 2023-09 SA#101 SP-231013 0001 2 B Adding new contents for clause 8 Conclusions and recommendations 19.1.0 2023-09 SA#101 SP-231013 0005 2 F Updates the definition of sensing assistance information 19.1.0 2023-09 SA#101 SP-231013 0003 4 C CR on Use Case on Coarse Gesture Recognition for Application Navigation and Immersive Interaction 19.1.0 2023-09 SA#101 SP-231013 0016 1 B Adding CPRs in the consolidated functional requirements section 19.1.0 2023-09 SA#101 SP-231013 0013 3 F Updates on consolidated KPI tables 19.1.0 2023-09 SA#101 SP-231013 0002 5 F Modification of the consolidated functional requirements section 19.1.0 2023-12 SA#102 SP-231397 0017 1 F Correction on consolidated KPI table for sensing 19.2.0 2023-12 SA#102 SP-231397 0019 4 F Update sensing consolidated KPI table 19.2.0 2023-12 SA#102 SP-231397 0018 1 D Adding security title 19.2.0 2024-02 - - - - - Re-introducing missing figure 5.19.1-1 19.2.1 2024-03 SA#103 SP-240202 0021 D Editorial clean-up of TR 22.837 section 7 19.3.0 2024-06 SA#104 SP-240795 0022 2 D Removal of trademark and product name from Sensing TR 19.4.0
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1 Scope
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The present document provides Stage 1 potential 5G service requirements for ambient power-enabled Internet of Things (i.e., Ambient IoT). In the context of the present document, an Ambient power-enabled IoT device is an IoT device powered by energy harvesting, being either battery-less or with limited energy storage capability (e.g., using a capacitor) and the energy is provided through the harvesting of radio waves, light, motion, heat, or any other suitable power source. An ambient IoT device is expected to have lower complexity, smaller size and reduced capabilities and lower power consumption than previously defined 3GPP IoT devices (e.g., NB-IoT/eMTC devices). Ambient IoT devices can be maintenance free and can have long life span (e.g., more than 10 years).
The aspects addressed in the present document include:
• Study use cases of ambient power-enabled IoT and identify potential service requirements, including:
▪ Security aspects, e.g., authentication and authorization, etc.
▪ Network selection, access control, connection, mobility and identification management
▪ Charging (e.g., per data volume, per message)
▪ Aspects related to stakeholder models (e.g., involving interactions in PLMNs, NPNs or other parties)
▪ Positioning
▪ Aspects on device life cycle management related to 3GPP system.
• Study traffic scenarios, device constraints (e.g., power consumption) and identify potential performance requirements and KPIs
• Gap analysis between the identified requirements for ambient power-enabled IoT and what is already defined by existing 3GPP requirements.
Note: How Ambient IoT device performs energy harvesting is out of scope of this technical report.
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2 References
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The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or non‑specific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
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[7] 3GPP TS 22.278: "Service requirements for the Evolved Packet System (EPS)".
[8] 3GPP TS 22.261: "Service requirements for the 5G system".
[9] 3GPP TS 22.011: “Service accessibility”.
[10] 5G for smart manufacturing: https://www.gsma.com/iot/wp-content/uploads/2020/04/2020-04_GSMA_SmartManufacturing_Insights_On_How_5G_IoT_Can_Transform_Industry.pdf
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[34] 3GPP TR 23.501 release-17 - https://www.3gpp.org/ftp/Specs/archive/23_series/23.501/23501-h50.zip
[35] 3GPP TR 38.300 release-17 - https://www.3gpp.org/ftp/Specs/archive/38_series/38.300/38300-h10.zip
[36] https://www.mokosmart.com/use-iot-fire-detector-sensor/
[37] https://www.5gamericas.org/wpcontent/uploads/2019/07/5G_Americas_URLLLC_White_Paper_Final__updateJW.pdf
[38] TS 22.261 https://www.3gpp.org/ftp/Specs/archive/22_series/22.261/22261-j00.zip
[39] TR 22.866: "enhanced Relays for Energy Efficiency and Extensive Coverage".
[40] Global RFID Sensor Market - Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2021-2031, Transparency Market Research, June 2021
[41] Sensors: Technologies and Global Markets (https://www.bccresearch.com/market-research/instrumentation-and-sensors/sensors-technologies-markets-report.html),BCCResearch
[42] Berckmans, D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Tech. Off. Int. Epiz. 2014, 33, 189–196.
[43] Precision Agriculture for Crop and Livestock Farming - Brief Review, Animals 2021
[44] Journal of Dairy Science - Comparison of 2 systems of pasture allocation on milking intervals and total daily milk yield of dairy cows in a pasture-based automatic milking system, American Dairy Science Association, 2013
[45] Grazing and automation, Proceedings 4th Meeting European Grassland Federation WG “Grazing”, 2015
[46] Grazing Methods, UK Agriculture Food and Environment, https://grazer.ca.uky.edu/content/grazing-methods-which-one-you#:~:text=Strip%20Grazing%20%E2%80%93%20This%20technique%20involves,a%20new%20allocation%20of%20forage.
[47] Nora O’Donovan, https://www.independent.ie/business/farming/dairy/dairy- advice/putting-a-size-on-paddocks-30447793.html
[48] A. van den Pol-van Dasselaar, P. W. Blokland, et al, Beweidbare oppervlakte en weidegang op melkveebedrijven in Nederland (Pasture area and grazing on dairy farms in the Netherlands), Livestock Research Wageningen, 2015
[49] Laura Paine, A Summary of Dairy Grazing Practices in Wisconsin
[50] R. J. Rendell, Designing Paddocks on an Irrigated Dairy Farm, Victoria, Australia
[51] The EU pig meat sector, European Parliament
[52] http://www.bestgenetics.com.cn/EN/PigFarm1
[53] Lisette. E. van der Zande, et al, Individual detection and tracking of group housed pigs in their home pen using computer vision, Front. Anim. Sci. 2:669312. doi: 10.3389/fanim.2021.669312, April 2021
[54] Carol Souza de Silva, Wageningen University, the impact of Pen Size and Stocking Density on behaviour and welfare of growing pigs
[55] Xue Li, Xia Xiong, et al, Effects of stocking density on growth performance, blood parameters and immunity of growing pigs, December, 2020
[56] https://www.concreteconstruction.net/projects/infrastructure/so-many-manholes-so-little-time_o#:~:text=U.S.%20EPA%20estimates%20the%20number,from%20100%20to%20500%20feet.
[57] http://news.cjn.cn/24hour/wh24/201101/t1276749.htm
[58] https://www.dibblecorp.com/insights/manhole-infrastructure-assessments/
[59] https://www.francebleu.fr/infos/faits-divers-justice/un-homme-retrouve-mort-dans-une-bouche-d-egout-a-saint-nazaire-1666456765
[60] https://guancha.gmw.cn/2022-05/16/content_35739227.htm
[61] Intelligent manhole cover national standard, GB/T 41401, 2022, the Ministry of Housing and Urban-Rural Development of the People's Republic of China
[62] MEMS gauge pressure sensor NSPGD1 Novosense, liquid level measurement, https://www.novosns.com/Public/Uploads/uploadfile/files/20220726/-897.pdf
[63] Tilt sensor, Clinometer LE-60, http://www.tuoluoyi.com/upfile/201610/2016100853701905.pdf
[64] https://www.analog.com/media/en/technical-documentation/data-sheets/adxl375.pdf
[65] Sung-Pil CHANG, Jinkyo CHOO, Values of Bridge in the Formation of Cities
[66] https://www.thepaper.cn/newsDetail_forward_4643273
[67] https://news.sina.cn/gn/2021-12-19/detail-ikyamrmy9954352.d.html
[68] https://www.nytimes.com/2020/12/22/world/europe/genoa-bridge-collapse.html
[69] https://www.theguardian.com/world/2022/oct/31/india-bridge-collapse-death-toll-rises-rescue-efforts-continue
[70] Role of the Local Government in Monitoring and Maintenance Bridges, IAPA Proceedings Conference, 2019
[71] https://elastisense.com/structural-health-monitoring-bridges
[72] GB/T 39559.2-2020 National Specification for operational monitoring of urban rail transit facilities – Part 2: Bridge
[73] Tilt sensor, Clinometer LE-60, http://www.tuoluoyi.com/upfile/201610/2016100853701905.pdf
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[75] Big data analysis for bridges health monitoring, Tongji University, 2017
[76] Beijing Dongsha bridge safe operation monitoring, http://www.ccea.zju.edu.cn/_upload/article/files/64/e6/a824c7d04932af200750fbd9e690/fb0a5e9c-a1e7-47b8-bdb4-82b584d1c52f.pdf
[77] 3GPP TS 23.502: "Procedures for the 5G System (5GS)"
[78] https://www.rfidjournal.com/esls-are-becoming-more-affordable-for-retailers
[79] https://www.newswire.ca/news-releases/sobeys-pilots-smart-cart-the-first-intelligent-grocery-shopping-cart-803013190.html
[80] https://en.wikipedia.org/wiki/Electronic_paper
[81] F. Palacio, et al. (2006). "Active RFID tag with sensing capabilities and low power consumption".
[82] T. O. John, H. C. Ukwuoma, S. Danjuma and M. Ibrahim, "Energy consumption in wireless sensor network", Energy, vol. 7, no. 8, 2016.
[83] Tuna, G., and V. C. Gungor. "Energy harvesting and battery technologies for powering wireless sensor networks." Industrial Wireless Sensor Networks. Woodhead Publishing, 2016. 25-38.
[84] M. Zeinali and J. Thompson, "Impact of Compression and Small Cell Deployment on NB-IoT Devices Coverage and Energy Consumption with a Realistic Simulation Model", Sensors. no. 19, 2021.
[85] Average size of a warehouse (https://www.nobroker.in/forum/what-is-the-average-size-of-a-warehouse/)
[86] K. Tang et al., "A 75.3 pJ/b Ultra-Low Power MEMS-Based FSK Transmitter in ISM-915 MHz Band for Pico-IoT Applications," 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, pp. 1-4, doi: 10.1109/ISCAS51556.2021.9401715.
[87] J. Bae and H. Yoo, "A low energy injection-locked FSK transceiver with frequency-to-amplitude conversion for body sensor applications," 2010 Symposium on VLSI Circuits, 2010, pp. 133-134, doi: 10.1109/VLSIC.2010.5560325.
[88] Letizia Appolloni and Daniela D’Alessandro, “Housing Spaces in Nine European Countries: A Comparison of Dimensional Requirements”, International Journal of Environmental Research and Public Health, April 2021
[89] https://www.vectorsolutions.com/resources/blogs/safe-stacking-guidelines-for-warehouses/
[89] What is the “2 Hour Rule” with leaving food out, https://ask.usda.gov/s/article/What-is-the-2-Hour-Rule-with-leaving-food-out
[90] Cold chain guidelines, https://www.afgc.org.au/industry-resources/cold-chain-guidelines
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3 Definitions, symbols and abbreviations
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3.1 Definitions
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For the purposes of the present document, the terms and definitions given in 3GPP TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in 3GPP TR 21.905 [1].
Ambient IoT device: An ambient power-enabled Internet of Things device is an IoT device powered by energy harvesting, being either battery-less or with limited energy storage capability (e.g., using a capacitor).
Store-and-forward communication: in the context of this study, store-and-forward communication is an operation mode of a 5G system where a single UE can collect and store information from an Ambient IoT device before forwarding this information to the network.
Ambient IoT Direct network communication: represents communication between the Ambient IoT device and 5G network with no UE conveying information between the Ambient IoT device and the 5G network.
Ambient IoT Indirect network communication: represents communication between the Ambient IoT device and the 5G network where there is an Ambient IoT capable UE helping in conveying information between the Ambient IoT device and the 5G network.
Ambient IoT device to UE direct communication: represents communication between an Ambient IoT device and an Ambient capable UE with no network entity in the middle.
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3.2 Abbreviations
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For the purposes of the present document, the abbreviations given in 3GPP TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in 3GPP TR 21.905 [1].
LPWA Low Power Wide Area
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4 Overview
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In today’s IoT networks, the IoT devices are usually powered by conventional batteries with a limited lifespan. The usage of conventional batteries has influenced the way these IoT devices are deployed and used. The astronomical growth of IoT network together with the deployment of huge numbers of IoT devices, has pushed up the maintenance costs, including both labor and battery costs. Large numbers of conventional batteries have been disposed of every year and only a small part of them can be efficiently recycled. In some extreme environmental conditions, maintaining the operation of IoT devices and replacing the batteries can be quite challenging. In this regard, battery-free IoT devices are proposed and it as they have the potential to improve the network performance and sustainability, and expand the application scenarios. By removing the conventional battery, the device size and cost can be significantly reduced, thus paving the way to a variety of new applications.
In the 5G era, various LPWA technologies such as eMTC, NB-IoT andRedCap. have been developed to fulfil the increasing demand from verticals. These LPWA technologies have achieved low cost, low power and massive connections and can meet requirements of many applications. However, there are still many use cases and applications that cannot be addressed. For example, conventional battery-powered device cannot be deployed in extreme environmental conditions (e.g. high pressure, extremely high/low temperature, humid environment). Also, the use of conventional battery devices can be limited where maintenance-free devices are required (e.g. where the devices are inaccessible and it is not possible to replace the device battery). Finally, ultra-low complexity, very small device size/form factor (e.g. thickness of mm), longer life cycle, etc. are required for mass market use cases.
Ambient power enabled IoT is a promising technology to fulfil the unmet market requirements stated above. An Ambient power-enabled IoT device is an IoT device powered by energy harvesting, being either battery-less or with limited energy storage capability (e.g. using a capacitor) and the energy is provided through the harvesting of radio waves, light, motion, heat, or any other suitable power source. This study targets at describing the use cases and potential requirements in support of ambient IoT devices with lower complexity, smaller size, reduced capabilities and lower power consumption than previously defined 3GPP IoT devices (e.g. NB-IoT/eMTC devices) with an emphasis on improving network efficiency. Ambient IoT devices can be maintenance free and can have long life span (e.g. more than 10 years).
Therefore, a new kind of IoT service for the verticals will be enabled by combining ambient power-enabled IoT with cellular networks, vastly benefitting the 3GPP ecosystem.
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5 Use cases
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5.1 Use case on Ambient IoT on automated warehousing
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5.1.1 Description
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The automated warehouse inventory scenario includes multiple stages, as shown in the figure below, which are divided into verification and unloading, gate-in inventory, inventory, gate-out inventory and check & loading. Along with the transfer, storage and inventory of goods, a large amount of warehousing information will be generated. This information generally has the characteristics of frequent data read operations and large data volumes. Ambient IoT devices are attached to items of different values and usage, such as pallet containers and individual product, and relevant communication equipment is deployed. Through the information interaction between communication equipment and tags, accurate and rapid inventory and efficient management of storage information can be realized in each stage.
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Figure 5.1.1-1: Automated warehouse inventory
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5.1.2 Pre-conditions
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The 5G network equipment used for inventory is deployed in the warehouse according to the needs of automated warehouse inventory scenario:
- Ambient IoT devices containing contain the assigned information, which can be read and written by the 5G network, get attached to different warehouse items, such as pallet containers, storage racks, forklift and individual product;
- The warehouse management platform is a trusted 3rd party and has subscribed the Ambient IoT services and could have interaction with the 5G network with necessary inventory information;
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5.1.3 Service Flows
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When some goods in one batch are delivered to a gate of the warehouse and are ready for the gate-in operation, the management platform selects a gate inventory mode.
There are two gate inventory modes supported by both the management platform and the 5G system.
• Manual-Triggered Mode: The inventory task is triggered by the command sent from the management platform, so the 5G system simply execute the command (e.g., start the inventory task).
• Auto-Triggered Mode: The inventory management system set the gate inventory mode to “Auto-Triggered”, the 5G network send discovery signal periodically to discover Ambient IoT devices within the gate area; if at least one Ambient IoT device is discovered, then the 5G system start inventory task automatically.
The goods arrive near the door of the warehouse, 5G network will perform gate inventory procedure according to the trigger from the management platform.
1. Ambient IoT devices establish communication with the 5G network, and send their own identity and corresponding goods information to the 5G network.
2. The 5G network sends the obtained information to the management platform;
3. The management platform generates a list of gate-in inventory results according to the inventory data and necessary information received from Ambient IoT devices by the continually inventory operation.
4. When the goods are placed on shelves in the warehouse and have been stored for a certain period of time, the management platform starts the indoor inventory task periodically for double check of the goods (total goods or per different batch/group), and generates a list of tags to be inventoried, sends the list to the 5G network;
5. The 5G network receives the list and sends large-scale/specified inventory signals;
6. The Ambient IoT device in the 5G network coverage establish communication with the network, and the 5G network interacts with the corresponding devices according to the inventory requirements to obtain goods information;
7. The 5G network sends the acquired goods information to the management platform;
8. The management platform summarizes the received information and generates a list of inventory results.
9. When the goods are ready for shipping and arrive the gate area, similar to gate-in operation, 5G network perform gate inventory procedure according to the platform's selection, interact with Ambient IoT devices and send the obtained information to the management platform.
10. The management platform generates a list of gate-out inventory results.
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93a47931cc679002202cfe56afd8b056
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22.840
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5.1.4 Post-conditions
|
With the support of 5G system, automated warehousing could be realized to improve the efficiency of goods management. If the inventory result list is consistent with the purchase/shipment list, the administrator can obtain the inventory result list, and update the inventory list in the management platform; If the result is inconsistent with the purchase or shipment order, the administrator would receive a notification/warning.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.1.5 Existing features partly or fully covering the use case functionality
|
In previous releases, SA1 has finished several studies about IoT topic to introduce SA1 requirements in TS 22.011[9], TS 22.278[7], TS 22.368[6] and TS 22.261[8] to address requirement for IoT business about device lifetime, power consumption, data transmission and communication mechanism.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.1.6 Potential New Requirements needed to support the use case
|
[P.R.5.1.6-001] The 5G system shall be able to support communication with Ambient IoT device which is battery-less or with limited energy storage (e.g., capacitor).
[P.R.5.1.6-002] The 5G system shall support to provide collected information from Ambient IoT devices to the trusted 3rd party.
[P.R.5.1.6-003] The 5G system shall support suitable security mechanisms for Ambient IoT devices, including encryption and data integrity.
[P.R.5.1.6-004] The 5G system shall be able to support the authentication and authorization mechanisms of Ambient_IoT devices.
[P.R.5.1.6-005] The 5G system shall be able to manage (e.g. provide service parameters, activate, deactivate) multiple Ambient IoT devices in bulk.
[P.R.5.1.6-006] The 5G system shall be able to provide Ambient IoT service with the following KPIs
Table 5.1.6-1: KPI Table of Ambient IoT for automated warehousing
Scenario
Max. allowed end-to-end latency
Communication Service Availability
Reliability
User-experienced data rate
Message Size
Device density
Communication Range
Service area dimension
Device speed
Transfer interval
Positioning service latency
Positioning service availability
Positioning Accuracy
Automated warehousing
1s (note 3)
99%
NA
<100/128bits/s (note 4)
96/128 bits (note 1)
[NA]
30m indoors
NA
5~10km/h
NA
NA
NA
2~3 m (note 2)
Note 1: Message size refers to the Ambient IoT device identifier used for goods identification in this use case;
Note 2: Three-dimensional positioning (both horizontal and vertical) is considered;
Note 3: End to end latency refers to the time taken for an Ambient IoT device to transmit the message;
Note 4: User-experienced data rate is calculated as the message size (96/128bits) transmitted within 1s time period;
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.2 Use case on medical instruments inventory management and positioning
| |
93a47931cc679002202cfe56afd8b056
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22.840
|
5.2.1 Description
|
More and more medical instruments are utilized in hospital. They always need to be cleaned and sterilized, and shall withstand certain conditions e.g., high temperature, high pressure or humidity. Traditional inventory management for the medical instrument is usually operated manually, which is inefficient and even in some cases, causes serious accident e.g., lost or invalidity. To improve safe and efficient utilization of the medical instrument, remote medical instrument inventory and online maintenance are being developed.
For the remote inventory and online maintenance, the medical instrument is needed to be supplied with Ambient IoT device. Considering the working condition of the medical instrument, this kind of Ambient IoT device should be battery-less or with limited energy storage capability, maintenance-free and should have long service life time. Through 5G network and the IoT device, the medical instrument exchange inventory management and positioning information with medical instrument management platform.
Following is an example of service flow to illustrate an inventory and positioning operation for medical instruments.
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93a47931cc679002202cfe56afd8b056
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22.840
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5.2.2 Pre-Conditions
|
Network operator UU deploys a new service “Ambient IoT” through its 5G system. Hospital Z is subscribed to the new inventory management service for its orthopaedic instruments (e.g. orthopaedic knives, orthopaedic scissors, orthopaedic forceps, orthopaedic hooks, orthopaedic needles, orthopaedic scrapers, orthopaedic cones, orthopaedic drills, orthopaedic saws, orthopaedic chisels, orthopaedic files / shovels, orthopaedic active instruments, etc)
A number of Ambient-IoT devices recording different orthopaedic instrument information are stuck on these orthopaedic instruments. They are usually indoor stored in the instrument warehouse or medical instrument storage room or special storage cabinet.
The Ambient-IoT device is battery free or with limited energy storage capability.
These Ambient-IoT devices attached in the orthopaedic instrument are with very simple capability and not applications installed on them.
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93a47931cc679002202cfe56afd8b056
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22.840
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5.2.3 Service Flows
|
Belle is a nurse of Hospital Z. She has the authorization to remotely manage orthopaedic instrument through the inventory management platform of the hospital. She can operate this work in the hospital or out of the hospital.
1. Belle wants to acquire the inventory information of orthopaedic forceps. She uses her phone to send inventory request to the inventory management platform of the hospital. The platform requires the 5G network to collect inventory information of orthopaedic forceps in predefined duration, e.g., 10 seconds.
2. The 5G network then sends signals to the Ambient-IoT devices in Hospital Z. After receiving the signal, the Ambient IoT devices are active and can receive messages from 5G network.
3. The 5G network can help to transmit the “read” command from the inventory management platform transparently or to translate the command to ask the Ambient-IoT devices attached on the orthopedic forceps to report its status information which can be the serial number of the orthopaedic forceps (16 bits), usage status (2 bits), usage records (128 bits), years of use (6 bits), number of usage (18 bits), maintained status (2 bits), being stored at the predefined or indicated physical addresses of the Ambient IoT device.
4. Ambient-IoT devices on the orthopaedic forceps report the inventory information stored at the corresponding physical address to the inventory management platform of the hospital via 5G network.
5. The inventory management platform sends the inventory information to Belle's phone.
6. After receives the status list of the orthopaedic forceps, Belle selects a pair of orthopaedic forceps from the list of orthopaedic instruments and clicks on “positioning request”. The request is passed to the inventory management platform. The inventory management platform asks 5G network to collect the position of the selected orthopaedic forceps.
7. The selected orthopaedic forceps is now being transported in the hospital handcart from Hospital Outpatient building to Hospital Inpatient building. It’s moving speed is less than 6km/h.
8. The 5G network calculates the position of the selected orthopaedic forceps after receives response of the Ambient-IoT device attached on the orthopaedic forceps.
9. The 5G network then delivers the position information to the inventory management platform.
10. The platform responds the position information to Belle’s phone.Thus, Belle acquires the position of the selected orthopaedic forceps on her phone.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.2.4 Post-Conditions
|
Hospital Z utilizes Ambient-IoT service to support the remote inventory management of medical instrument. Belle can read the information of medical instrument. She can also find a medical instrument through the positioning information provided by Ambient IoT service.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.2.5 Existing features partly or fully covering the use case functionality
|
None
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.2.6 Potential New Requirements needed to support the use case
|
[PR 5.2.6-001] The 5G system shall be able to communicate with an Ambient-IoT device.
[PR 5.2.6-002] The 5G system shall be able to provide group communication for a group of Ambient-IoT devices.
[PR 5.2.6-003] The 5G system shall be able to provide a mechanism to expose the information collected from an Ambient-IoT device to a trusted 3rd party.
[PR 5.2.6-004] The 5G system shall be able to support positioning for an Ambient-IoT device.
[PR 5.2.6-005] The 5G system shall be able to provide communication service with KPIs listed in Table 5.2.6-1 for the Ambient IoT device/s.
Table 5.2.6-1: KPIs for use case of Medical Instrument Inventory management
Scenario
Max. allowed end-to-end latency
Communication Service Availability
Reliability
User-experienced data rate
Message Size
Device density
Communication Range
Service area dimension
Device speed
Transfer interval
Positioning service latency
Positioning service availability
Positioning Accuracy
Medical instrument inventory management and positioning
Several seconds
99%
NA
<2kbit/s
(note 1)
176bit
≥1000/km2
(note 2)
50m indoor
200m outdoor
NA
Stationary or walking speed
<6 km/h
NA
NA
NA
3 m to 5 m indoor
Note 1: User experienced data rate is calculated based on inventory information (176 bits) within time period of e.g. 100 ms;
Note 2: It refers typical medical instrument density condition in Chinese hospital.
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93a47931cc679002202cfe56afd8b056
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22.840
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5.3 Use Case on Ambient IoT devices in substations in smart grids
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.3.1 Description
|
With around 80 million kilometres of transmission and distribution lines worldwide, electricity networks are the backbone of secure and reliable power systems. As stated in the World Energy Outlook 2020 [2], significant investment takes place in new network capacity between 2019 and 2030 as a result of growing demand for electricity, the addition of new renewable generation capacity and the need to develop smart grids. The expansion of electricity networks to 2030 is about 80% more than over the past decade. Around 30% of the increase in transmission lines and 20% of the increase in distribution network lines are attributable to the increase of renewables. Over the next ten years, around 16 million km of existing distribution lines and 1.5 million km of transmission lines need to be replaced or digitalised, together with switching equipment, transformers, meters and other crucial components. In regions with older power systems, such as the United States and the European Union, roughly one-fifth of current networks need to be replaced or digitalised; this corresponds to 2.7 million km in the United States and 3.7 million km in the European Union. More than 60% of global line replacements and new lines are in emerging market and developing economies, with China alone accounting for a third of what is needed (over 7 million km).
Smart grids with wide use of IoT devices have a vital role to play in supporting the penetration of variable renewables electricity sources. IoT offers a wide number of applications in the energy sector, i.e., in energy supply, transmission and distribution, and demand [3]. In particular substations are a significant part of the electrical power grid. Through these stations, the voltage level is converted from high voltage to low voltage using (transformer). The substation transfers power to distribution stations by the transmission lines (see (a) in Figure 5.3.1-1). Monitoring electrical substations are necessary to detect faults and treat them, because if left unattended, it may lead to electrical problems and cause long-term consequences. These problems not only cause energy losses but also lead to electrical outages and losses in expensive equipment, in addition to injuries and accidents such as fire. Therefore, monitoring of substations and their equipment is important to ensure safety, protection, and stability in the electric power networks. Different types of sensors (e.g. temperature and humidity sensors) can be used in the outdoor ultra-high voltage substation (see (b) in Figure 5.3.1-1) to detect the anomaly and trigger predictive maintenance. In addition, various sensors can be used in other use cases in the power transmission and distribution networks (see (c) to (e) in figure F.3.1-1) for remote monitoring and protection purposes.
Overview
Outdoor ultra-high voltage substation
(Possible issues such as poor contact can cause faulty service.)
Indoor/outdoor shielding cabinet
(Problems such as poor wire connection can cause short circuit or even fire.)
Underground transmission and distribution lines
(Possible issues include that the underground cables can be cut or bitten by rats, and can be damaged due to the faulty drainage system.)
Aerial transmission and distribution lines
(Possible issues include that the aerial cables can be vibrated or displaced, and difficult to provide power sources for sensors to monitor these parameters.)
Figure 5.3.1-1: Transmission and distribution networks in smart grids
For these use cases, the data acquisition process is typically not latency-critical, but a large number of sensors have to be efficiently connected, especially considering many of these sensors have limited power source and relatively frequent data transmission (every 5-15 minutes) is expected in some cases (e.g., sensor data is collected once per several seconds for critical equipment monitoring and protection). Moreover, lifespans of the field IoT devices are expected to be one decade or longer, which is one of the main differences compared with consumer products. Many production systems are subject to regulatory approvals (e.g., safety certification), changes to a running production system have often to be avoided. Often sensors are deployed in locations that are inaccessible, where physical replacement would be unduly expensive. Research continues to develop efficient communication techniques to meet the requirements, among which Ambient IoT [4] is very promising to enable wireless communication with minimum energy consumption. The Ambient IoT devices typically are battery-less or with limited energy storage capability, and obtain energy from the environment. The communication power consumption of such Ambient IoT devices are expected to be a few hundred μW [80] [81] [82] [83]. Moreover, communication service availability with sufficient 5G coverage is important especially for remote monitoring of the critical equipment.
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93a47931cc679002202cfe56afd8b056
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22.840
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5.3.2 Pre-conditions
|
In this use case GreenGrid has service level agreement with GreenMobile to deploy 5G network to enable the communication of these Ambient IoT devices with the network. As part of the service level agreement, GreenMobile provides energy efficient communication and management services to GreenGrid including:
- interfacing with GreenGrid’s grid monitoring and management platform;
- ensuring the lifespan of the Ambient IoT devices of 10-15 years;
- providing energy efficient device management for the Ambient IoT devices based on the instructions from the grid monitoring and management platform;
- providing energy efficient operation (e.g. inventory, read, write) the Ambient IoT devices based on the instructions from the grid monitoring and management platform;
- providing sufficient positioning information of the Ambient IoT devices;
- providing energy efficient security mechanisms for the communication between Ambient IoT devices and the network.
GreenGrid has installed wireless sensors, one form of Ambient IoT devices, in the outdoor ultra-high voltage substations of their power transmission and distribution networks to monitor the corresponding parameters to detect malfunctioning and broken elements in the surrounding environment. This environment is typically monitored using various types of sensors such as temperature sensors, humidity sensors, pressure sensors and vibration sensors. Remote monitoring along with a classification of the anomaly can help with predictive maintenance. For example, the sensor data (e.g. temperature, humidity) can be used to detect high-temperature problems (which is a typical indication of poor connection), excessive humidity (which could be due to floods), etc.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.3.3 Service Flows
|
1. The 5G core network receives the request from the application function (in this case GreenGrid’s grid monitoring and management platform) to operate on the Ambient IoT devices in a certain area. The 5G core network starts to operate on these devices accordingly. Once detecting the signals from the 5G network these Ambient IoT devices can respond to the command.
2. These Ambient IoT devices send the identification information to the 5G core network and complete the authentication procedure.
3. The Ambient IoT devices (wireless sensors) measure the environmental parameters, such as temperature, humidity, pressure and vibration.
For temperature, humidity and pressure measurement, typical sampling rate is 10 Hz with sample size of 32 bits, thus the data generation per Ambient IoT device is about 320 bit/s. For vibration measurement, typical sampling rate is 10 Hz with sample size of 96 bits, thus the data generation per Ambient IoT device is about 960 bit/s.
4. The 5G core network, based on the requests issued by the application function, performs operations such as "inventory", "read" and "write" on the Ambient IoT devices correspondingly. "Inventory" operation is to read the Ambient IoT device identifier. "Read" operation is to read sensor data. "Write" operation can be used to configure the Ambient IoT device.
5. The 5G core network then sends the results of the operations to the application function. The application function includes diagnostic functions that analyze the sensor data and detect the anomaly and trigger predictive maintenance when necessary. Typically, the sensor data is collected once per several seconds.
6. The 5G system is also expected to provide positioning service for these Ambient IoT devices, which can be for device management purpose (e.g., to automatically identify the locations of the Ambient IoT devices) or for security reason (e.g., to make sure the devices have not been removed). The positioning for these purposes is not expected to be performed frequently.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.3.4 Post-conditions
|
The 5G system enables efficient communication, with enhanced security and tens of meter-level positioning accuracy, for the Ambient IoT devices installed in the power transmission and distribution networks for remote monitoring and protection purposes.
|
93a47931cc679002202cfe56afd8b056
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22.840
|
5.3.5 Existing features partly or fully covering the use case functionality
|
Service requirements for MTC (Machine-Type Communications) have been captured in TS 22.368 [6] since release 10, which specifies the service requirements for network improvements. In addition to the common service requirements, specific service requirements have also been defined corresponding to the following MTC Features:
- Low Mobility;
- Time Controlled;
- Small Data Transmissions;
- Infrequent Mobile Terminated;
- MTC Monitoring;
- Secure Connection;
- Group Based MTC Features:
- Group Based Policing;
- Group Based Addressing.
Resource efficiency is one of the key service requirements for IoT, for which there are a few requirements specified in 3GPP TS 22.278 [7] and 3GPP TS 22.261 [8]. The target IoT scenarios have been described as
As sensors and monitoring UEs are deployed more extensively, the need to support UEs that send data packages ranging in size from a small status update in a few bits to streaming video increases. A similar need exists for smart phones with widely varying amounts of data. Specifically, to support short data bursts, the network should be able to operate in a mode where there is no need for a lengthy and high overhead signalling procedure before and after small amounts of data are sent. The system will, as a result, avoid both a negative impact to battery life for the UE and wasting signalling resources.
The related service requirements are kept at a relatively general level, e.g.
The 5G system shall minimize control and user plane resource usage for data transfer from send only UEs.
The 5G system shall minimize control and user plane resource usage for stationary UEs (e.g. lower signalling to user data resource usage ratio).
The 5G system shall minimize control and user plane resource usage for transfer of infrequent small data units.
The 5G system shall optimize the resource use of the control plane and/or user plane for transfer of small data units.
Additional consideration needs to be given in support of Ambient IoT devices that are battery-less or with limited energy storage capability, which present new challenges to the 5G system.
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93a47931cc679002202cfe56afd8b056
|
22.840
|
5.3.6 Potential New Requirements needed to support the use case
|
[PR.5.3.6-001] The 5G system shall support energy efficient communication mechanisms (i.e. minimizing the overall and peak device communication power consumption) for Ambient IoT devices.
[PR 5.3.6-002] The 5G system shall be able to support energy efficient security mechanisms for Ambient IoT devices, including authentication, encryption and data integrity.
[PR 5.3.6-003] The 5G system shall support a mechanism to interface a 3rd party application to manage and operate on the Ambient IoT devices.
[PR.5.3.6-004] The 5G system shall be able to collect charging information for using Ambient IoT services on per Ambient IoT device basis (e.g., total number of communications per charging period).
[PR 5.3.6-005] The 5G system shall be able to collect charging information per application for using Ambient IoT services (e.g., total number of Ambient IoT devices per charging period).
[PR 5.3.6-006] The 5G system shall provide the network connection to address the KPIs for the use of Ambient IoT devices in substations in smart grids, see table 5.3.6-1.
Table 5.3.6-1: Potential key performance requirements for the use of Ambient IoT devices in substations in smart grids
Scenario
Max. allowed end-to-end latency
Communication Service Availability
Reliability
User-experienced data rate
Message Size
Device density
Communication Range
Service area dimension
Device speed
Transfer interval
Positioning service latency
Positioning service availability
Positioning Accuracy
Remote monitoring of transmission and distribution networks in smart grids
1 s (note 4)
99%
NA
< 1kbit/s
(note 5)
Typically
< 100 bytes
(note 1)
< 10,000 /km2
(note 3)
Outdoor: typically 50-200 meters
[several km2 up to 100 000 km2]
(note 2)
Stationary
5-15 min
NA
NA
several 10 m
NOTE 1: Electronic Product Code standard [5], this size is the payload size.
NOTE 2: The service are refers to the overall size of transmission and distribution networks. Typically, the size of the individual substations varies from 100m x 200m to 500m x 600m.
NOTE 3: The device density is calculated based on an individual substation, where typically several hundreds of Ambient IoT devices are required to monitor the environmental parameters.
NOTE 4: This is calculated based on assumption that the sensor data are collected once per several seconds.
NOTE 5: For temperature, humidity and pressure measurement, typical sampling rate is 10 Hz with sample size of 32 bits, thus the data generation per Ambient IoT device is about 320 bit/s. For vibration measurement, typical sampling rate is 10 Hz with sample size of 96 bits, thus the data generation per Ambient IoT device is about 960 bit/s.
|
93a47931cc679002202cfe56afd8b056
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22.840
|
5.4 Use case on supporting Ambient IoT in Non-Public Network for logistics
| |
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.4.1 Description
|
The logistic chain is composed of different processes, such as warehouse inbound and outbound, etc. During the inbound, warehousing inventory needs to be done in order to track whether all the goods are inventoried. After the outbound, the cargo needs to be tracked to ensure that corresponding goods are moving to the right destination. This tracking that happens whenever the cargo moves across a specific area (e.g., equip with portals). The warehousing inventory and cargo tracking need the support of Ambient IoT in non-public network.
In the case of warehousing inventory, there are tons of goods waiting to be inventoried. A pallet is used for carrying goods (each pallet for maximumly 100 goods) and each pallet passes through a base station (e.g., integrated with a scanner) which can scan all the goods on the pallet and complete the inventory for those goods. The time interval for two neighboring pallets to pass through the base station is usually short (e.g., less than 3min). With the introduction of Ambient IoT devices, the logistics service provider can have its own NPN and perform the warehousing inventory using the NPN. All goods can communicate directly with the base station or send the data to the base station indirectly through a relay.
In the case of cargo tracking, A wagon carries many pallets with goods (more than 10 pallet per wagon) and travels to the destination. In order to track the goods within the wagon, several toll gates are set up among the route of the wagon. Whenever the wagon passes through the toll gate, the data of all goods are required to be sent to the toll gate which can then forward the data to the proper application server for tracking. The service provider of logistics may set up the tracking equipment on certain toll gate to track the data of goods carried by wagons. The efficiency for tracking the goods carried by wagon will be dramatically increased.
In the two processes described, the Ambient IoT devices used can be either battery-less or with limited energy storage capability (i.e., using a capacitor).
Figure 5.4.1-1: Ambient IoT in non-public network for logistics
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93a47931cc679002202cfe56afd8b056
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22.840
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5.4.2 Pre-conditions
|
The service provider has service agreement with the Network Operator. The service agreement includes the provisioning of NPN to the service provider.
The service provider set up its own NPN for managing the Ambient IoT devices in logistics.
The use case of logistics in 5.4 can be split into 2 key processes, which are warehousing inventory and cargo tracking.
Process A: Cargo warehousing inventory
A service provider of logistics has its NPN with the support of Network Operator for the access of Ambient IoT devices (e.g., tags) for tracking the good within each pallet (each pallet for more than 150 goods). Alternately, the service provider set up its own NPN for the purposes. The service provider of logistics uses licensed band or unlicensed band for accessing the NPN.
All the Ambient IoT devices conduct onboarding and provisioning for NPN credentials. Ambient IoT device registers with the onboarding NPN and obtains the NPN credential with low energy cost.
All the Ambient IoT devices register with the NPN.
Process B: cargo tracking
A service provider of logistics has its NPN with the support of Network Operator for tracking the Ambient IoT devices (e.g., tags) carried by wagon. Alternately, the service provider set up its own NPN for the purposes. The NPN is set up on the toll gate along the road for tracking the cargo.
All the Ambient IoT devices conduct onboarding and provisioning for NPN credentials. Ambient IoT device registers with the onboarding NPN and obtains the NPN credential with low energy cost.
|
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.4.3 Service Flows
|
Process A: Cargo warehousing inventory
1. When each pallet passes through the base station, all the Ambient IoT devices within the pallet complete the inventory by echoing the request for inventory from the base station.
2. After a short internal of time (less then 3min), the base station inventories the goods carried by the next pallet.
Process B: cargo tracking
1. All the Ambient IoT devices register with the NPN when the wagon carrying the cargo passes through the toll gate.
2. All the Ambient IoT devices within the wagon sends the response to the base station on the toll gate upon the receipt of the request from the base station.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.4.4 Post-conditions
|
Process A: Cargo warehousing inventory
1. Inventory information is obtained by the service provider, who can proceed with the warehouse outbound.
Process B: cargo tracking
1. By receiving the tracking information of the cargo, the service provider knows that corresponding goods are moving to the right destination.
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93a47931cc679002202cfe56afd8b056
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22.840
|
5.4.5 Existing features partly or fully covering the use case functionality
|
TS 22.261 has following requirements:
- The 5G system shall support operator-controlled alternative authentication methods (i.e., alternative to AKA) with different types of credentials for network access for IoT devices in isolated deployment scenarios (e.g., for industrial automation).
- The 5G system shall support the capability to operate in licensed and/or unlicensed bands.
Existing specification support alternative authentication method with different types of credentials for network access for IoT devices. For Ambient IoT device, the alternative authentication method should be light-weight in order make sure that the cost of power consumption caused by the authentication is strictly controlled.
Existing specification support the capability for 5G system to operate in licensed and/or unlicensed band. For Ambient IoT device, it should also be able to connect to the 5G network using licensed and/or unlicensed band. Service provider may decide to use licensed band or unlicensed band based on the situation of local spectrum and the SLA between Service provider and other parties (e.g., Network Operator, industry partner, etc.). Ambient IoT device operating in unlicensed band should have the same performance (e.g., transmission range) with the one operating in licensed band.
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93a47931cc679002202cfe56afd8b056
|
22.840
|
5.4.6 Potential New Requirements needed to support the use case
|
[PR.5.4.6-001] 5G system shall support network access for Ambient IoT devices while considering the constraint power consumption.
Note: The above requirement applies to both NPN and PLMN.
|
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.5 Use case on intralogistics in automobile manufacturing
| |
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.5.1 Description
|
The automobile manufacturing industries are constantly looking for ways to increase productivity by improving inventory accuracy and material flows. Therefore, intralogistics for production facilities in automobile factories have been targeting at these goals by achieving timely visibility of inventories (a.k.a. precise materials scheduling) to achieve optimum efficiency in production. Specifically, it involves stocking, dispatching, and sorting. It is no surprise that there are competing technologies for supporting small-scale inventory, which at times would require human involvement. With the advent of Industry 4.0, manufacturing requires a much higher automatic intralogistics performance [10]. This means more key areas inside large manufacturing facilities would require highly-efficient and automated inventory of materials and parts. Ambient power-enabled IoT (Ambient IoT) service provided by 5G can be expected to meet the demanding needs by providing communication to Ambient IoT devices with good performance, with the Ambient IoT solely dependent on harvested ambient energy, being maintenance-and-battery-less or with limited energy storage capability, extremely small, thin, and of low complexity, very light-weight, and with a long lifespan. For this use case, communication service availability with sufficient 5G network coverage in the service area is important. Additionally, the 5G system can provide Ambient IoT devices with positioning services, as full automation would require AGVs or forklifts to fetch materials at accurate locations within the large storage areas. Moreover, the 5G system provides intrinsic core network functions to manage and authenticate Ambient IoT connections, enabling operators to build more interesting cases together with their business partners.
Automobile company A uses standardized load containers to realize flow of materials. The load containers are purchased, owned and managed by Company A. With its business growing, Company A is integrating the latest and advanced production and logistics management systems to ensure the production facilities are modernized for Industry 4.0. In quest of this, intralogistics at Company A is expected to automatically identify and track individual goods and materials throughout production facilities: not only at the dock where materials enter the production facilities, but also in the large floor-level storage areas, further in the sorting areas and down at production lines. Similar to the norm of the automobile manufacturing industry, a typical production facility of Company A covers a total area of around 600,000 square meters. Company A uses unique identifiers (e.g. 96 bit or 240 bit EPC codes) to distinctly identify load containers.
Fig. 5.5.1-1: Intralogistics in automobile manufacturing
Prior to deciding on a future-proof solution, a comprehensive analysis (e.g., ROI) is carried out by Company A to verify Ambient IoT’s suitability for the long-term business objectives. By virtue of many of Ambient IoT’s attractive wireless communication characteristics, Company A came to the conclusion of a positive case. To effectively meet their intralogistics demand, it entails installing around 1300 stationary “readers” strategically per typical production facility (600,000 square meters), where in total around 800,000 Ambient IoT devices would be physically present at the same time. To keep track of the production process, normally 20 Ambient IoT devices need to be read per second per “reader” (e.g., fast-moving AGVs passing a gate). After verifying with operator O, Company A adopts Ambient IoT by having a service contract with the operator, which is commissioned to design and deploy 5G network coverage within Company A’s automobile production facilities and then to accordingly enable and manage Ambient IoT service. Wherever needed in the production facilities, sufficient network coverage is assumed.
It is in the huge floor storage area where the loaded containers are kept, accurate positioning service is needed by AGVs for pick-up, once certain materials or parts are needed by a production line. At Company A, the floor storage area is divided into blocks with the dimension of 18m by 18m, in each of which a stationary “reader” is deployed. Each block is further divided into grids of the size slightly larger than a load container, whose length is around 1.5m. To achieve uninterrupted material flow, Company A decide to use the space of two grids for parking load containers holding the same materials. In this way, as a load container holding certain material at a given grid is carried away by an AGV, the same material loaded in another container would always be available at the grid just next to it.
The personnel Sylvain at Company A attaches each of the load containers with an Ambient IoT device, where a unique code is written in its microchip. Ambient IoT devices are very thin non-battery powered or with limited energy storage capability IoT devices, which instead function using an energy harvesting mechanism that produces a limited amount of power. A typical energy harvesting device can produce up to a few hundred microwatts [11] (e.g., less than 250 micro-Watts). In the production facilities of Company A, an Ambient IoT device can be connected to the 5G network and the communication is enabled.
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93a47931cc679002202cfe56afd8b056
|
22.840
|
5.5.2 Pre-conditions
|
An Ambient IoT device can obtain energy by collecting energy sources such as solar and radio waves. Each load container is attached with an Ambient IoT device for supporting intralogistics. Ambient IoT devices have the capability of storing information needed by the inventory process. Base stations installed inside car manufacturing facilities provide network coverage. The communication service availability is achieved by providing seamless 5G network coverage inside the service areas. The 5G system delivers application data to the intralogistics management system of the car manufacturer.
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93a47931cc679002202cfe56afd8b056
|
22.840
|
5.5.3 Service Flows
|
1. Company A uses Ambient IoT devices attached to their load containers to support automated intralogistics process for improved productivity. They send load containers to various suppliers in order to bring back ordered goods, materials and parts.
2. By returning to Company A’s automobile manufacturing facilities, large amount of load containers first enter the dock area. There the 5G network transmits signals intending to start inventory process. Since the Ambient IoT devices harvest power from the environment, once detecting the signals from the 5G network the Ambient IoT devices can respond by starting random access.
3. 5G network (i.e., core network) authenticates and authorizes Ambient IoT devices.
4. Based on the requests issued by the application function, the 5G network performs the automatic inventory operation (read out pre-written unique IDs in Ambient IoT devices) of the large number of incoming load containers entering the dock area fast and efficiently. The read-out information is sent by 5GC to Company A’s inventory system (connected to ERP/APS), where the inventoried information is updated or saved.
5. Once registered in the inventory system, load containers are stored in the floor storage areas. The load containers (still loaded with various materials and parts) can be inventoried by Company A either periodically or when requested.
6. As automobile manufacturing process continues (almost non-stop), a picking list is automatically generated according to the production plan managed by the APS (Advanced Planning and Scheduling) system.
7. According to the information in the inventory system, AGVs (or forklifts) with their respective picking lists are sent to the floor storage areas to pick up the corresponding load containers (holding different materials or parts). As the floor storage area is extremely huge, Company A utilize the 5G network positioning service for AGVs to quickly fetch the target load containers holding the exact materials requested in the pick-up list. Since the floor storage area is divided into 18m by 18m blocks with each block being further divided into grids of 1.5m by 1.5m, the 5G system always provides the AGVs with the accurate positioning information of the target load containers.
8. When AGVs find the needed load containers in the floor storage area, convey them further to the sorting areas. It is in the sorting areas that different materials and parts brought in by AGVs are grouped in accordance with precise manufacturing schedules to be followed at the production lines.
9. At the production lines, materials and parts as logically grouped (by sorting machines at the sorting areas) are eventually fed into production. The empty load containers will be brought to the recycling area. These empty load containers await to be sent to suppliers at the next schedule determined by ERP/APS.
10. In all the key areas (floor storage areas, sorting areas, production lines and empty container recycling area) inside the manufacturing facility, the load containers need to be inventoried efficiently, timely, fast, accurately. When AGVs enter these areas, step 4 is repeated and the precise material scheduling is updated in the inventory system.
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93a47931cc679002202cfe56afd8b056
|
22.840
|
5.5.4 Post-conditions
|
Thanks to the Ambient IoT service provided by the 5G system, automobile manufacturing can enjoy automatic intralogistics, largely improve the efficiency and productivity.
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93a47931cc679002202cfe56afd8b056
|
22.840
|
5.5.5 Existing features partly or fully covering the use case functionality
|
SA1 has performed various studies on IoT in previous releases, where related normative stage 1 requirements are introduced in TS 22.011 [9], TS 22.278 [7], TS 22.368 [6], and TS 22.261 [8].
TS 22.011 introduces access control for MTC, examples of periodic network selection attempts are:
For UEs only supporting any of the following, or a combination of, NB-IoT, GERAN EC-GSM-IoT [18], and Category M1[13] of E-UTRAN enhanced-MTC, the UE shall interpret the interval value to be between 2 and 240 hours, with a step size of 2 hours between 2 and 80 hours and a step size of 4 hours between 80 and 240 hours.
In the absence of a permitted value in the SIM/USIM, or the SIM/USIM is phase 1 and therefore does not contain the datafield, then a default value of 60 minutes, shall be used by the UE except for those UEs only supporting any of the following, or a combination of: NB-IoT, GERAN EC-GSM-IoT [18], and Category M1 [17] of E-UTRAN enhanced-MTC. For those UEs a default value of 72 hours shall be used.
NOTE: Use of values less than 60 minutes may result in excessive UE battery drain.
TS 22.368 addresses features of MTC communication and service requirements related to MTC device triggering, addressing, identifiers, low mobility, small data transmission, infrequent MT communication, security, remote MTC device management, group-based MTC features including policing and addressing, etc. Example requirements are:
The system shall provide mechanisms to lower power consumption of MTC Devices.
The system shall provide mechanisms for the network operator to efficiently manage numbers and identifiers related to MTC Subscribers.
TS 22.261 captures some important service requirements for IoT, e.g.
The 5G system shall support a secure mechanism for a home operator to remotely provision the 3GPP credentials of a uniquely identifiable and verifiably secure IoT device.
The 5G system shall support a secure mechanism for the network operator of an NPN to remotely provision the non-3GPP identities and credentials of a uniquely identifiable and verifiably secure IoT device.
An IoT device which is able to access a 5G PLMN in direct network connection mode using a 3GPP RAT shall have a 3GPP subscription.
The 5G system shall allow the operator to identify a UE as an IoT device based on UE characteristics (e.g. identified by an equipment identifier or a range of equipment identifiers) or subscription or the combination of both.
An IoT device which is able to connect to a UE in direct device connection mode shall have a 3GPP subscription, if the IoT device needs to be identifiable by the core network (e.g. for IoT device management purposes or to use indirect network connection mode).
The 5G system shall support operator-controlled alternative authentication methods (i.e. alternative to AKA) with different types of credentials for network access for IoT devices in isolated deployment scenarios (e.g. for industrial automation).
The 5G system shall support a suitable framework (e.g. EAP) allowing alternative (e.g. to AKA) authentication methods with non-3GPP identities and credentials to be used for UE network access authentication in non-public networks.
NOTE: Non-public networks can use 3GPP authentication methods, identities, and credentials for a UE to access network. Non-public networks are also allowed to utilize non-AKA based authentication methods such as provided by the EAP framework, for which the credentials can be stored in the ME.
The 5G system shall enable an NPN to be able to request a third-party service provider to perform NPN access network authentication of a UE based on non-3GPP identities and credentials supplied by the third-party service provider.
In these specifications, albeit the service requirements addressing traits for IoT in terms of low device power consumption, small and infrequent data transmissions, long service lifetime, and resource efficiently, the IoT devices considered in 3GPP have been assumed to be powered by at least batteries up till now. To enable extremely small, thin, light-weight, battery-less or with limited energy storage capability, or even disposable Ambient IoT devices that provide basic IoT data transaction at appropriate performance level suitable for the target scenarios, new challenges to the 5G system are foreseen and need to be addressed.
|
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.5.6 Potential New Requirements needed to support the use case
|
[PR 5.5.6-001] The 5G system shall support communication for an Ambient IoT device which is battery-less or with limited energy storage capability.
[PR 5.5.6-002] The 5G system shall support collection of charging information based on different charging policies for Ambient-IoT type of communication, i.e., total number of communication (e.g. data payload) per charging period, or total number of Ambient IoT devices per charging period.
[PR 5.5.6-003] The 5G system shall provide the network connection to address the KPIs for the use of Ambient IoT devices for intralogistics in automobile manufacturing, see table 5.3.6.1-1.
Table 5.5.6-1: Potential key performance requirements for the use of Ambient IoT devices for intralogistics in automobile manufacturing
Scenario
Max. allowed end-to-end latency
Communication Service Availability
Reliability
User-experienced data rate
Message Size
Device density
Communication Range
Service area dimension
Device speed
Transfer interval
Positioning service latency
Positioning service availability
Positioning Accuracy
Automatic Intralogistics in automobile manufacturing
10 s
(note 1)
99%
NA
<1 kbit/s
(note 2)
96 bits
(note 3)
<1,5 Million/km2 (note 4)
<30 meters
Indoors
600 000 m2
(note 5)
Up to 5 km/h
NA
NA
NA
3 m
NOTE 1: This value corresponds to peak reading rate of 100 tags per second. The average tag reading rate is lower.
NOTE 2: This value is calculated as the instant data rate for transmitting 96 bits within 100 ms time period. The need for data transmission is infrequent.
NOTE 3: EPC Tag Data standard [5], the length of the EPC number ranges from 96 bits to 496 bits. For intralogistics, EPC length of 96 bits is the most common EPC lengths to satisfy the use case.
NOTE 4: Daily around 1 million units of materials are used in the manufacturing area, but they are not used at the same time.
NOTE 5: A typical car manufacturing plant takes up to 600 000 m2 in surface.
|
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.6 Use case on Ambient IoT sensors in smart homes
| |
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.6.1 Description
|
Monitoring tasks in smart home scenarios can be roughly divided into two types:
• Monitoring of room environment. By deploying specific sensors that enable communication services, people can acquire real-time monitoring data (e.g. temperature, humidity) of their room environment no matter where they are.
• Monitoring of emergency situations. By deploying specific sensors that enable communication services, risk factors such as gas and smoke can be detected in the air and alarmed timely. When the sensor detects that the gas concentration in the home exceeds the threshold, it usually activates a very strong audio signal to alarm people at home. However, in case of people are out of home (e.g. while at work, or on shopping), it cannot reach people on this situation. So, it should be necessary to notify the family members through their phones as well.
In contrast with conventional battery-based sensors, Ambient IoT sensors can obtain and/or store energy from the environment, such as light, heat, wind, and radio waves, which can be converted to useable electrical energy [12]. Different ambient energy harvesting technologies have their own advantages and disadvantages, suitable for use in different environments. Energy harvesting technologies are not in the scope of the study.
Ambient IoT sensors can support diverse monitoring applications in smart home scenario with following advantages:
• Remove the demand for batteries, i.e. reduce the energy consumption of charging.
• Reduce the cost of maintenance, i.e. avoid human intervention for recharging or replacing.
• Increase device durability, i.e. devices can work continuously without charging and/or replacing the battery.
• Increase device portability, i.e., the locations of devices are no longer limited by electric cables or wires.
The Ambient IoT sensors can also consume very small amounts of energy. For example, the photovoltaic (PV) energy harvesting power density could provide 10µW~4mW, while the sensing power consumption of a gas sensor is only 1.2µW [13].
Considering the above advantages, Ambient IoT sensors can further improve the development of smart home applications.
|
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.6.2 Pre-Conditions
|
Tom’s family lives in Home A. In Home A, there is a gas sensor in the kitchen. It’s an Ambient IoT sensor, and the application server can obtain its sensor data through the network.
The gas sensor detects data continuously and allows the application server to request its data (e.g., a 32 bits packet) periodically. The application server stores the data and a pre-set methane concentration threshold (e.g., 5%).
|
93a47931cc679002202cfe56afd8b056
|
22.840
|
5.6.3 Service Flows
|
1. The gas sensor monitors the methane concentration in the kitchen in real-time.
2. The application server acquires the sensor data and finds the data reaching half of the threshold (e.g., 2.5%), then it will update a shorter period to request data.
3. If the data exceeds the threshold, the application server can send an alarm message through the network.
4. The application server will distribute the alarm message to Tom’s family through the network. If Tom’s family members do not respond, the alarm message will be sent again after a preset time (e.g., 1 min).
5. Tom’s family members receive the alarm message on their phones. Tom notices the alarm message and clicks on “Confirm” button in the app.
6. The application server receives the “Confirm” response and stops to send alarm messages to their phones until set time (e.g., 10min) passes and the data still exceeds the threshold.
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