Department of Computer Science and Engineering

Sungkyunkwan University 2066 Seobu-Ro, Jangan-Gu Suwon Gyeonggi-Do 16419 Republic of Korea +82 31 299 4957 +82 31 290 7996 pauljeong@skku.edu http://iotlab.skku.edu/people-jaehoon-jeong.php

Routing Area Computing-Aware Traffic Steering Working Group Internet-Draft This document proposes use cases for Computing-Aware Intelligent Transportation Systems (ITS). Computing-Aware Traffic Steering (CATS) provides the steering of packets of a traffic flow for a specific service request toward the corresponging service instance at an edge computing server at a service site. The use cases for Computing-Aware ITS include Context-Aware Navigation for Terrestrial Vehicles and Unmanned Aerial Vehicles (UVA) and Edge-Assisted Cluster-Based MAC Protocol for Software-Defined Vehicles.

Nowadays, various networked services are provided by leveraging edge computing infrastructure. Either a closest or a lightest edge computing server (simply called an edge server) can be selected to serve a request service. In this trend, Computing-Aware Traffic Steering (CATS) is standardized to provide the steering of packets of a traffic flow for a specific service request toward the corresponging service instance at an edge server at a service site. This document proposes two use cases for Computing-Aware Intelligent Transportation Systems (ITS). They are (i) Context-Aware Navigation Protocol for Terrestrial Vehicles and Unmanned Aerial Vehicles (UVA) and (ii) Edge-Assisted Cluster-Based MAC Protocol for Software-Defined Vehicles (SDV) .

This document uses the terminology described in , and . In addition, the following terms are defined below: Context-Aware Navigation Protocol (CNP): It is a protocol that enables either terrestrial vehicles (i.e., ground vehicles) or Unmanned Aerial Vehicles (UAV) to move in road networks or fly in the sky to maneuver safely without collisions, respectively . Edge-Assisted Cluster-Based MAC Protocol (ECMAC): It is a protocol that enables Software-Defined Vehicles (SDV) to communicate with each other using Software-Defined Vehicular Networks with edge computing servers .

This section explains a vehicular network architecture for vehicles and three use cases for for Computing-Aware ITS.

Software-Defined Vehicles (SDV) include terrestrial vehicles and Unmanned Aerial Vehicles (UAV). The standardization and implementation of SDVs are performed by AUTOSAR , Eclipes SDV , and COVESA . These SDVs need to communicate with each other to avoid collisions or accidents. shows a Vehicular Network Architecture for Software-Defined Vehicles (SDV) such as terrestrial vehicles and Unmanned Aerial Vehicles (UAV).

A Vehicular Network Architecture for Software-Defined Vehicles | IP-RSU2 |<------>| IP-RSU3 | +---------+ +---------+ +---------+ ^ ^ ^ : : : +-----------------+ +-----------------+ +-----------------+ | : V2I | | : V2I | | : V2I | | v | | v | | v | +--------+ | +--------+ | | +--------+ | | +--------+ | | SDV1 |===> | SDV2 |===>| | | SDV3 |===>| | | SDV4 |===>| +--------+<...>+--------+<........>+--------+ | | +--------+ | V2V ^ V2V ^ | | ^ | | : V2V | | : V2V | | : V2V | | v | | v | | v | | +--------+ | | +--------+ | | +--------+ | | | SDV5 |===> | | | SDV6 |===>| | | SDV7 |==>| | +--------+ | | +--------+ | | +--------+ | +-----------------+ +-----------------+ +-----------------+ Subnet1 Subnet2 Subnet3 (Prefix1) (Prefix2) (Prefix3) <----> Wired Link <....> Wireless Link ===> Moving Direction ]]>

Context-Aware Navigation Protocol is developed to provide the safe navigation (e.g., maneuver on the ground or in the sky) with Software-Defined Vehicles (SDV) such as electrical vehicles, autonomous vehicles, Unmanned Aerial Vehicle (UAV), and urban Air Mobility (UAM) . A connected network of such SDVs (e.g., autonomous vehicles and drones) on road networks can facilitate the safe driving on the ground or the safe flying in the sky. while driving on the roadways or skyways, many dangerous situations for SDVs may occur by the speed, orientation, and traffic density of the SDVs involved. Thus, there is a necessity for an automatic maneuvering mechanism for SDVs that handles both the current driving SDV and the oncoming SDVs heading toward an emergency spot (e.g., road hazard and road accident place). CNP is a realization of such an automatic maneuvering mechanism that SDVs collaborate with each other with the help of edge computing infrastructure through wireless communications such as 5G Vehicle-to-Everything Communication (i.e., 5G V2X). SDVs observe their road environments and other SDVs' behaviors with their on-board sensors like cameras and LiDAR. SDVs share these sensing data with an edge server in the edge computing infrastructure. The sensing data of SDVs need to be forwarded to an appropriate edge server in terms of network status and computing resource status. Such an edge server needs to be selected in order to provide SDVs with timely guidance for safe driving. The edge server conducts a maneuver control with the mobility information of SDVs and road environments, interacting with the SDVs and road infrastructure entities (e.g., traffic lights and raod ramps) in real time. CNP also provides a collision mitigation scheme with the SDVs so that the SDVs may experience minimum collision damages in hazardous roadways (or skyways) during non-maneuverable scenarios. CNP uses cluster formation where a cluster head is selected among adjacent SDVs to give guidance to its cluster members for safe maneuvering. The selection of such a cluster head is performed by the edge server that has the mobility information (e.g., speed, current position, and direction) of the SDVs. For SDVs, CNP based on IPv6 Neighbor Discovery is proposed in . It can work on top of either Dedidated Short-Range Communications (DSRC) like Wireless Access in Vehicular Environments (WAVE) or 5G V2X. Refer to and for the detailed mechanism of CNP for SDVs moving in either roadways or flying in skyways.

Edge-Assisted Cluster-Based MAC Protocol (ECMAC) is a protocol to facilitate the real-time communications among SDVs for safe maneuvering (e.g., driving or flying). ECMAC works on the SDVs with the help of an edge server in Software-Defined Vehicular Networks (SDVN). The edge server collects the mobility information from the SDVs in Vehicular Ad Hoc Networks (VANET) or Flying Ad Hoc Networks (FANET). The SDVs are formed in clusters that have a cluster head and the corresponging cluster members by the edge server in the SDVN. The edge server allocates a wireless channel and time slot scheduling to each cluster. Cluster members in each cluster report their mobility information (e.g., speed, current position, and direction) to its cluster head according to the time slot scheduling for a given wireless channel. The cluster head reports the aggregated mobility information to the edge server. An appropriate edge server needs to be dynamically selected and relaced according to the navigation path of the SDVs. This selection of an edge server should be performed by considering the network status and computing resource status between the edge server and SDVs. Refer to for the detailed mechanism of ECMAC for SDVs moving in roadways.

This document does not require any IANA actions.

The same security considerations for Computing-Aware Traffic Steering (CATS) are applicable to the use cases for the Computing-Aware ITS .

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Ministry of Science and ICT (MSIT) (No. RS-2024-00398199). This work was supported in part by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Ministry of Science and ICT (MSIT) (No. 2022-0-01015, Development of Candidate Element Technology for Intelligent 6G Mobile Core Network). This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government, Ministry of Science and ICT (MSIT) (No. 2023R1A2C2002990).

This document is made by the group effort of CATS WG, greatly benefiting from inputs and texts by (China Mobile), (Daejeon University), and (Dong-Eui University). The authors sincerely appreciate their contributions. The following are coauthors of this document: Department of Electrial & Computer Engineering

Sungkyunkwan University 2066 Seobu-Ro, Jangan-Gu Suwon Gyeonggi-Do 16419 Republic of Korea +82 31 299 4106 bienaime@skku.edu http://iotlab.skku.edu/people-Bien-Aime.php

Department of Computer Science & Engineering

Sungkyunkwan University 2066 Seobu-Ro, Jangan-Gu Suwon Gyeonggi-Do 16419 Republic of Korea +82 31 299 4106 chrisshen@skku.edu http://iotlab.skku.edu/people-chris-shen.php