V2X Misbehavior and Collective Perception Service: Considerations for Standardization

Ansari, Mohammad Raashid; Monteuuis, Jean-Philippe; Petit, Johann; Chen, Cong

doi:10.1109/cscn53733.2021.9686156

Cited by 7 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, CAM messages contain information about location and the kinematic state of the transmitter. However, as not every object has V2X communication capabilities (for example, pedestrians, obstacles, animals or non-V2X vehicles) or even having it they can be out of a certain vehicle range, ETSI has defined the Collective Perception Service to complement the Context Awareness Service [39]. CPM messages enable to share information about other road users or obstacles, so that the vehicles can extend their awareness even further that their sensing capabilities.…”

Section: A Traffic Modelmentioning

confidence: 99%

Goal-Oriented Source Coding and Filtering for Vehicular Communications

Gimenez-Guzman,

Leyva-Mayorga,

Popovski

2024

IEEE Internet Things J.

View full text Add to dashboard Cite

Vehicle-to-Everything (V2X) networks will constitute a prominent application in future generations of cellular networks, definitely transforming our conception of transportation systems. A major challenge in V2X networks is the vast amount of data generated by the large number of sensors in the vehicles, which saturates the wireless links. As it is not possible to meet the throughput, timing, and reliability requirements for the total bulk of generated data, one needs to filter out data based on the actual communication goal. In this paper we present an architecture and diverse options to implement filtering and source coding for goal-oriented vehicular communications. We illustrate how filtering and source coding contribute to meeting the strict delay requirements while maintaining energy-efficient operation. Our results show that goal-oriented communications, performed as the combination of Bloom filtering and goal-oriented source coding, can greatly contribute not only to reduce the energy consumption by up to 30% but also to decrease delay, which in turn increases the supported amount of delay-sensitive traffic by up to 819.2%.

show abstract

Section: A Traffic Modelmentioning

confidence: 99%

Goal-Oriented Source Coding and Filtering for Vehicular Communications

Gimenez-Guzman,

Leyva-Mayorga,

Popovski

2024

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Attackers may target CPMs and send false information that jeopardizes safety. Current standardization efforts do not yet include vehicle misbehavior specifications for advanced V2X services, such as collaborative perception (CP) [126].…”

Section: Security and Privacy Preservationmentioning

confidence: 99%

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

et al. 2022

View full text Add to dashboard Cite

Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

show abstract