Applicability of Computing-Aware Traffic Steering to Intelligent Transportation Systems
draft-jeong-cats-its-use-cases-05
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| Last updated | 2025-11-25 | ||
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draft-jeong-cats-its-use-cases-05
Computing-Aware Traffic Steering Working Group J. Jeong, Ed.
Internet-Draft B. Mugabarigira
Intended status: Informational Sungkyunkwan University
Expires: 29 May 2026 25 November 2025
Applicability of Computing-Aware Traffic Steering to Intelligent
Transportation Systems
draft-jeong-cats-its-use-cases-05
Abstract
This document describes the applicability of Computing-Aware Traffic
Steering (CATS) to Intelligent Transportation Systems (ITS). CATS
provides the steering of packets of a traffic flow for a specific
service request toward the corresponding service instance at an edge
computing server at a service site. CATS are applicable for
Computing-Aware ITS including (i) Context-Aware Navigation Protocol
(CNP) for Terrestrial Vehicles and Unmanned Aerial Vehicles (UAV),
(ii) Edge-Assisted Cluster-Based MAC Protocol (ECMAC) for Software-
Defined Vehicles, and (iii) Self-Adaptive Interactive Navigation Tool
(SAINT) for Cloud-Based Navigation Services, and (iv) Cloud-Based
Drone Navigation (CBDN) for Efficient Drone Battery Charging.
Status of This Memo
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provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 29 May 2026.
Copyright Notice
Copyright (c) 2025 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Provisions Relating to IETF Documents (https://trustee.ietf.org/
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Vehicular Network Architecture . . . . . . . . . . . . . . . 3
4. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4.1. Context-Aware Navigation Protocol . . . . . . . . . . . . 5
4.2. Edge-Assisted Cluster-Based MAC Protocol . . . . . . . . 6
4.3. Self-Adaptive Interactive Navigation Tool for Cloud-Based
Navigation . . . . . . . . . . . . . . . . . . . . . . . 9
4.4. Cloud-Based Drone Navigation (CBDN) for Efficient Battery
Charging in Drone Networks . . . . . . . . . . . . . . . 11
5. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 13
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14
7. Security Considerations . . . . . . . . . . . . . . . . . . . 14
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.1. Normative References . . . . . . . . . . . . . . . . . . 14
8.2. Informative References . . . . . . . . . . . . . . . . . 14
Appendix A. Changes from draft-jeong-cats-its-use-cases-04 . . . 16
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 16
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 17
1. Introduction
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
corresponding service instance at an edge server at a service site
[I-D.ietf-cats-usecases-requirements][I-D.ietf-cats-framework].
This document proposes four use cases about the applicability of CATS
for Computing-Aware Intelligent Transportation Systems (ITS). They
are (i) Context-Aware Navigation Protocol for Terrestrial Vehicles
and Unmanned Aerial Vehicles (UAV) [CNP-Vehicle] [CNP-UAV], (ii)
Edge-Assisted Cluster-Based MAC Protocol for Software-Defined
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Vehicles (SDV) [ECMAC], (iii) Self-Adaptive Interactive Navigation
Tool (SAINT) for Cloud-Based Navigation Services [SAINT], and (iv)
Cloud-Based Drone Navigation (CBDN) for Efficient Drone Battery
Charging [CBDN].
2. Terminology
This document uses the terminology described in
[I-D.ietf-cats-usecases-requirements] and [I-D.ietf-cats-framework].
In addition, the following terms are defined below:
* Context-Aware Navigation Protocol (CNP): It is an application
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 [CNP-Vehicle][CNP-UAV].
* Edge-Assisted Cluster-Based MAC Protocol (ECMAC): It is a Media
Access Control (MAC) protocol that enables Software-Defined
Vehicles (SDV) to communicate with each other using Software-
Defined Vehicular Networks with edge computing servers [ECMAC].
* Self-Adaptive Interactive Navigation Tool (SAINT): It is an
application protocol that guides terrestrial vehicles to navigate
efficiently towards their destination through the interaction
between the vehicles and the vehicular cloud for navigation
services [SAINT].
* Cloud-Based Drone Navigation (CBDN): It is an application protocol
for efficient drone battery charging in drone networks by finding
globally coordinated drone routes that minimize the total traffic
delay in a drone network while reducing the overall Quick Battery-
Charging Machine (QCM) congestion level [CBDN].
3. Vehicular Network Architecture
This section explains a vehicular network architecture for vehicles
in 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 [AUTOSAR], Eclipes
SDV [Eclipse-SDV], and COVESA [COVESA]. These SDVs need to
communicate with each other to avoid collisions or accidents.
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Figure 1 shows a Vehicular Network Architecture for Software-Defined
Vehicles (SDV) such as terrestrial vehicles and Unmanned Aerial
Vehicles (UAV). This vehicular network architecture is based on the
vehicular network architecture for IPv6 Wireless Access in Vehicular
Environments (IPWAVE) in [RFC9365].
Vehicular Cloud
*******************************************
* *
* +------------------+ *
* | Cloud Controller | *
* +------------------+ *
* ^ *
* | *
* v *
*******************************************
^ Edge-Cloud1 ^ Edge-Cloud2 ^ Edge-Cloud3
| +------------+ | +------------+ | +------------+
| |Edge-Server1| | |Edge-Server2| | |Edge-Server3|
| +------------+ | +------------+ | +------------+
| ^ | ^ | ^
| | | | | |
v V v V v V
+---------+ +---------+ +---------+
| gNB1 |<------->| gNB2 |<------>| gNB3 |
+---------+ +---------+ +---------+
^ ^ ^
: : :
+-----------------+ +-----------------+ +-----------------+
| : 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
Figure 1: Vehicular Network Architecture for Software-Defined
Vehicles
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4. Use Cases
This section explains four use cases about the applicability of CATS
to Computing-Aware ITS.
4.1. Context-Aware Navigation Protocol
A connected network of automated vehicles on roads can increase the
driving safety of driverless vehicles (i.e., autonomous vehicles).
The critical level of dangerous situations on the road while driving
can be increased by the speed, orientation, and traffic density of
the vehicles involved. Therefore, there is a need for a maneuvering
mechanism that handles both the current driving vehicle and the
oncoming vehicles headed toward an emergency zone (e.g., road hazard
and road accident spot).
Edge Cloud
+-------------+
| Edge-Server1|
+-------------+
^ ^
| |
V V
+---------+ +---------+ +---------+
| gNB1 |<---->| gNB2 |<---...--->| gNBn |
+---------+ +---------+ +---------+
^ ^ ^
: : :
+-------------------+ +----------+ +-----------------------------+
| : V2I | | : V2I | | : V2I |
| V2V v | | v | | v |
|+----+<-->+----+ | | +----+ | |+----+V2V +----+ V2V+----+ |
|| CM1|==> | CH1|==>| | | CH2|==>| || CM1|<-->| CHn|<-->| CM2|==>|
|+----+ +----+ | | +----+ | |+----+==> +----+==> +----+ |
| ^ | | ^ | ... | ^ |
| | V2V | | | V2V | | | V2V |
| v | | v | | v |
| +----+ | | +----+ | | +----+ |
| | CM2|==>| | | CM1|==>| | | CM3|==> |
| +----+ | | +----+ | | +----+ |
+-------------------+ +----------+ +-----------------------------+
Cluster1 Cluster2 Clustern
<----> Wired Link <....> Wireless Link ===> Moving Direction
Figure 2: The Illustration of Context-Aware Navigator Protocol
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Context-Aware Navigation Protocol (CNP) enhances the safety of
vehicles driving in urban roads [CNP-Vehicle][CNP-UAV]. Firstly, CNP
includes a collision avoidance module that builds on both vehicular
networks and on-board sensors to track vehicles' behaviors, and this
module analyzes the driving risks to determine the necessary
maneuvers in dangerous situations. Secondly, CNP establishes a
collision mitigation strategy that limits the severity of collision
damages in hazardous road during non-maneuverable scenarios. Through
a theoretical analysis as well as extensive simulations, the
effectiveness of CNP is shown in terms of the reduction of both
communication overhead and the risk of road collisions.
To use CNP, vehicles need to report their mobility information (e.g.,
vehicle identifier, destination, current position, direction, and
speed) to a central cloud or an edge cloud for a CNP-based vehicle
collision avoidance service as shown in Figure 2. Service instances
at either the edge cloud or the central cloud need to work for the
vehicles. The packets with the mobility information per vehicle need
to steered to an appropriate service instance for CNP. The service
instance needs to provide a appropriate maneuver direction to each
vehicle moving on the roadway.
Since the vehicle is moving along the roadway, to serve the vehicle
for collision avoidance, a new service instance needs to be selected
for it, considering the network delay between the vehicle and service
instance and also computing resources for the service instance. For
the service instances to continue to compute the maneuvers smoothly,
they need to exchange the mobility information as context while the
vehicles are moving and change their service instance over time.
That is, the context migration should be supported in the CATS
infrastructure having the central clouds and the edge clouds to
foster service instances.
4.2. Edge-Assisted Cluster-Based MAC Protocol
Vehicular networks have emerged as a promising means to mitigate
safety hazards in modern transportation systems. On highways,
emergency situations associated with vehicles necessitate a reliable
Media Access Control (MAC) protocol that can provide timely warnings
of possible vehicle collisions.
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An Edge-Assisted Cluster-Based MAC Protocol (ECMAC) is a vehicular
MAC protocol for reliable and fast packet dissemination in software-
defined vehicular networks [ECMAC]. To reduce the control messaging
overhead for clustering, ECMAC separates the cluster control plane
(i.e., managing cluster formation) from the data plane (i.e., actual
data transmission and forwarding) by using a software-defined network
controller in a cellular network edge server as illustrated in
Figure 3.
For transmitting packets effectively and efficiently, ECMAC tries to
channel interference minimization among adjacent clusters by using a
joint optimization of channel assignment and a time slot scheduling.
The joint optimization consists of two phases such as the channel
assignment phase and the time slot allocation phase. In the first
phase for the channel assignment, ECMAC allocates different wireless
channels to the adjacent channels by minimizing the total inter-
cluster interference by reusing the available channels. In the
second phase for the time slot allocation, ECMAC uses a time-division
multiple access (TDMA) schedule algorithm to guarantee a high
reliability and a low latency. The TDMA schedule in ECMAC is
determined by a joint optimization process in the cellular edge,
which is formulated as a binary integer linear programming problem
and solved by a heuristic approach based on the divide-and-conquer
paradigm. This joint optimization process minimizes the signal
interference by jointly considering channel assignment and time slot
allocation, thereby ensuring reliable communication. Through
extensive simulations, the effectiveness of ECMAC is demonstrated a
higher delivery ratio of emergency packets than the legacy data
delivery approaches.
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Vehicular Cloud
*******************************
* Edge Cloud *
* +--------------------------+ *
* | Edge-Server1 | *
* | +--------------------+ | *
* | |Cluster Formation, | | *
* | |Channel Assignment, | | *
* | |Time Slot Allocation| | *
* | +--------------------+ | *
* +--------------------------+ *
********************************
^ ^
| |
V V
+---------+ +---------+
| gNB1 |<-----...----->| gNBm |
+---------+ +---------+
^ ^
: :
+-------------------+ +-----------------------------+
| : V2I | | : V2I |
| v | | v |
|+----+V2V +----+ | |+----+ V2V+----+V2V +----+ |
|| CM1|<-->| CH1|==>| || CM1|<-->| CHn|<-->| CM2|==>|
|+----+==> +----+ | |+----+==> +----+==> +----+ |
| ^ | ... | ^ |
| | V2V | | | V2V |
| v | | v |
| +----+ | | +----+ |
| | CM2|==>| | | CM3|==> |
| +----+ | | +----+ |
+-------------------+ +-----------------------------+
Cluster1 Clustern
<----> Wired Link <....> Wireless Link ===> Moving Direction
Figure 3: The Illustration of Edge-Assisted Clusterer-Based MAC
Protocol
In ECMAC, the cellular network edge server can be implemented as a
service instance in the CATS infrastructure. In the same way with
CNP, service instances need to efficiently perform the context
migration (e.g., mobility information and cluster membership) of
vehicles so that they can continue to form clusters of vehicles,
allocate wireless channels to the vehicles, and assign time slots to
the vehicles over time.
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4.3. Self-Adaptive Interactive Navigation Tool for Cloud-Based
Navigation
Efficient navigation services are important in Intelligent
Transportation Systems because they allow vehicles to move towards
destinations quickly. For this efficient navigation, vehicles need
to interact with a central cloud or an edge cloud in real time.
Self-Adaptive Interactive Navigation Tool (SAINT) is a cloud-based
navigation guidance system for vehicular traffic optimization in road
networks [SAINT]. The legacy navigation systems guide vehicles to
take their navigation paths with real-time traffic statistics in road
maps without considering the navigation paths of other vehicles.
This uncoordinated navigation planning may incur traffic congestion
in certain areas in the road networks.
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Vehicular Cloud
*************************************
* Edge Cloud *
* +-----------------------------------+ *
* | Edge Server Edge Emergency | *
* | Center | *
* | +-------------+ +---------------+ | *
* | |Road Traffic | |Road Emergency | | *
* | |Information | |Notification | | *
* | +-------------+ +---------------+ | *
* +-----------------------------------+ *
*************************************
^ ^ ^
| | |
V V V
+---------+ +---------+ +---------+
| gNB1 |<----->| gNB2 |<------>| gNB3 |
+---------+ +---------+ +---------+
^ ^ ^
: : :
: : :
+-----------------+ +-----------------+ +-----------------+
| : V2I | | : V2I | | : V2I |
| v | | v | | v |
| +--------+ | | +--------+ | | +--------+ |
| | SDV1 |===>| | | SDV2 |===>| | | SDV3 |===>|
| +--------+<........>+--------+ | | +--------+ |
| ^ V2V ^ | | ^ |
| : V2V | | : V2V | | : V2V |
| v | | v | | v |
| +--------+ | | +--------+ | | +--------+ |
| | SDV4 |===> | | | SDV5 |===>| | | SDV6 |==>|
| +--------+ | | +--------+ | | +--------+ |
+-----------------+ +-----------------+ +-----------------+
Subnet1 Subnet2 Subnet3
(Prefix1) (Prefix2) (Prefix3)
<----> Wired Link <....> Wireless Link ===> Moving Direction
Figure 4: The Illustration of Self-Adaptive Interactive Navigation
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On the other hand, SAINT uses a virtual metric called congestion
contribution that estimates traffic congestion in each road segment
in the current time and near-future time by considering the planned
navigation paths of the vehices in the target road network. SAINT
guides each vehicle to have a certain-level detour in order to make
the whole road network have spread vehicular traffic and lessen
possible traffic congestion in certain road segments or
intersections.
For this cooperative navigation in SAINT, while vehicles are moving
along the roadways, they need to send their periodic navigation
queries and their mobility information to appropriate service
instances in a central cloud or an edge cloud in the CATS
infrastructure. The service instances need to process their
navigation queries and reply to them with good navigation paths,
considersing the road-wide traffic optimization as depicted in
Figure 4. Due to the movement of the vehicles, the switching from a
service instance to another service instance should be performed
efficiently, considering the network delay between the service
instance and each vehicle and the computing resources of the service
instance.
SAINT can support the efficient delivery of emergency vehicles such
as ambulance and fire engine to a road accident spot by the
management of a congestion contribution matrix in a target road
network [SAINTplus]. It can not only guide vehicles within the
accident spot, but also can detour vehicles approaching the accident
spot. This version of SAINT is called SAINT+.
4.4. Cloud-Based Drone Navigation (CBDN) for Efficient Battery Charging
in Drone Networks
The growing popularity of Unmanned Aerial Vehicles (UAV) comes with a
need to charge their battery at Quick Battery-Charging Machines
(QCMs) due to their limited battery capacity. Without drone
coordination, a drone's choice for its QCM may lead to congestion
resulting from multiple drones selecting the same QCM, thus
increasing the drones' battery-charging delay due to the queueing day
at the QCM. This battery-charging delay leads to a long travel delay
for each drone at the QCM. A Cloud-Based Drone Navigation (CBDN)
efficiently determines drone routes to minimize the overall QCM
congestion level for all QCMs in a target drone network [CBDN]. It
finds globally coordinated drone routes that minimize the total
travel delay in a drone network by reducing the overall QCM
congestion level.
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Vehicular Cloud
*******************************
* Edge Cloud *
* +--------------------------+ *
* | Edge-Server1 | *
* | +--------------------+ | *
* | |Drone Registration, | | *
* | |Drone Route Finding,| | *
* | |QCM-Selection Scheme| | *
* | +--------------------+ | *
* +--------------------------+ *
********************************
^ ^ ^
| | |
V V V
+---------+ +---------+ +---------+
| gNB1 |<--->| gNB2 |<--->| gNB3 |
+---------+ +---------+ +---------+
^ ^ ^
: : :
+-----------------+ +-----------------+ +-----------------+
| : V2I | | : V2I | | : V2I |
| v | | v | | v |
| +--------+ | | +--------+ | | +--------+ |
| | UAV1 |===>| | | UAV2 |===>| | | UAV3 |===>|
| +--------+<........>+--------+ | | +--------+ |
| ^ V2V ^ | | ^ |
| : V2V | | : V2I | | : V2I |
| v | | v | | v |
| +--------+ | | +--------+ | | +--------+ |
| | UAV4 |===> | | | QCM1 | | | | QCM2 | |
| +--------+ | | +--------+ | | +--------+ |
+-----------------+ +-----------------+ +-----------------+
Subnet1 Subnet2 Subnet3
(Prefix1) (Prefix2) (Prefix3)
<----> Wired Link <....> Wireless Link ===> Moving Direction
Figure 5: The Illustration of Cloud-Based Drone Navigation
An edge cloud in the CATS infrastructure with computing and storage
resources need to compute the trajectories of the drones (i.e., drone
routes), along with their average speeds, source positions, and
destination positions, as well as the battery charing loads at the
QCMs. The wireless communications between drones and infrastrucure
nodes (e.g., edge server) can be either 5G and beyond 5G or wireless
LAN, as illustated in Figure 5. Drones interact with the edge server
to compute navigation paths regarding the drone network-wide traffic
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optimization of all drones in the drone network. To decrease battery
consumption, the drones only once report their mobility information
(i.e., current position, destination, direction, and speed) to the
edge computing decice to acquire their navigation paths.
Upon the commencement of the drone service, each drone reports its
mobility information to the edge server. A drone's QCM reservation
for battery charging acquires the most efficient shortest path
regarding the drone-network-wide traffic optimization of all the
drones in the drone network. For this drone-network-wide traffic
optimization, a drone sends its mobility information to the edge
server before its departure, and the edge server computes an optimal
navigation path to the drone and notifies the drone of the path in
run time.
5. Requirements
This section specifies the requirements for the applicability of CATS
to ITS use cases in Section 4.
* R1: Dynamic mapping between a required service and a service
instance. Both network delay and computing delay are considered
over time.
* R2: Run-time context migration of vehicles between edge servers
(i.e., service instances). Each vehicle's context (e.g., mobility
information, communications parameters (e.g., channel, time slot))
is transferred to an appropriate service instance along with its
movement over time.
* R3: Proactive load balancing among service instances considering
the required Quality of Service (QoS) and Quality of Experience
(QoE) for vehicles. The trajectories of vehicles are considered
for such load balancing.
* R4: Dynamic clustering of geographically adjacent vehicles.
Clusters of vehicles are dynamically reconstructed over time.
* R5: Dynamic network configuration for vehicles and network
forwarding entities (e.g., base stations and switches/routers).
In wireless networks, network resources (e.g., channel and time
slot) per vehicle are dynamically configured by base stations. In
wired networks, a network slice from a base station to a service
instance are dynamically adjusted for each vehicle.
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* R6: Differentiated packet scheduling for service types. Packets
of real-time services (e.g., autonomous driving) and packets of
non-real-time services (e.g., infotainment) are handled
differently.
6. IANA Considerations
This document does not require any IANA actions.
7. Security Considerations
The same security considerations for Computing-Aware Traffic Steering
(CATS) are applicable to the use cases for the Computing-Aware ITS
[I-D.ietf-cats-usecases-requirements] [I-D.ietf-cats-framework].
8. References
8.1. Normative References
[RFC9365] Jeong, J., Ed., "IPv6 Wireless Access in Vehicular
Environments (IPWAVE): Problem Statement and Use Cases",
RFC 9365, DOI 10.17487/RFC9365, March 2023,
<https://www.rfc-editor.org/info/rfc9365>.
8.2. Informative References
[I-D.ietf-cats-usecases-requirements]
Yao, K., Contreras, L. M., Shi, H., Zhang, S., and Q. An,
"Computing-Aware Traffic Steering (CATS) Problem
Statement, Use Cases, and Requirements", Work in Progress,
Internet-Draft, draft-ietf-cats-usecases-requirements-09,
19 November 2025, <https://datatracker.ietf.org/doc/html/
draft-ietf-cats-usecases-requirements-09>.
[I-D.ietf-cats-framework]
Li, C., Du, Z., Boucadair, M., Contreras, L. M., and J.
Drake, "A Framework for Computing-Aware Traffic Steering
(CATS)", Work in Progress, Internet-Draft, draft-ietf-
cats-framework-19, 20 November 2025,
<https://datatracker.ietf.org/doc/html/draft-ietf-cats-
framework-19>.
[AUTOSAR] "AUTOSAR Adaptive Platform", Available:
https://www.autosar.org/standards/adaptive-platform, March
2024.
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[Eclipse-SDV]
"Eclipse Software Defined Vehicle Working Group Charter",
Available: https://www.eclipse.org/org/workinggroups/sdv-
charter.php, March 2024.
[COVESA] "Connected Vehicle Systems Alliance",
Available: https://covesa.global/, March 2024.
[CNP-Vehicle]
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Internet-Draft Computing-Aware ITS Applicability November 2025
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Appendix A. Changes from draft-jeong-cats-its-use-cases-04
The following changes are made from draft-jeong-cats-its-use-cases-
04:
* This version updates the figures in this draft by changing the IP-
RSUs to gNBs to suite the 5G and beyong communication
architecture.
Acknowledgments
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 and RS-
2022-II221015).
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).
Contributors
This document is made by the group effort of CATS WG, greatly
benefiting from inputs and texts by Peng Liu (China Mobile), Yong-
Geun Hong (Daejeon University), and Joosang Youn (Dong-Eui
University). The authors sincerely appreciate their contributions.
The following are coauthors of this document:
Juwon Hong
Department of Computer Science & Engineering
Sungkyunkwan University
2066 Seobu-Ro, Jangan-Gu
Suwon
Gyeonggi-Do
16419
Republic of Korea
Phone: +82 31 299 4106
Email: hongju2024@skku.edu
URI: http://iotlab.skku.edu/people-Joo-Won-Hong.php
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Internet-Draft Computing-Aware ITS Applicability November 2025
Yiwen Shen
Department of Computer Science & Engineering
Sungkyunkwan University
2066 Seobu-Ro, Jangan-Gu
Suwon
Gyeonggi-Do
16419
Republic of Korea
Phone: +82 31 299 4106
Email: chrisshen@skku.edu
URI: https://chrisshen.github.io/
Authors' Addresses
Jaehoon Paul Jeong (editor)
Department of Computer Science & Engineering
Sungkyunkwan University
2066 Seobu-Ro, Jangan-Gu
Suwon
Gyeonggi-Do
16419
Republic of Korea
Phone: +82 31 299 4957
Email: pauljeong@skku.edu
URI: http://iotlab.skku.edu/people-jaehoon-jeong.php
Bien Aime Mugabarigira
Department of Electrical & Computer Engineering
Sungkyunkwan University
2066 Seobu-Ro, Jangan-Gu
Suwon
Gyeonggi-Do
16419
Republic of Korea
Phone: +82 10 5964 8794
Email: bienaime@skku.edu
URI: http://iotlab.skku.edu/people-Bien-Aime.php
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