Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Multi-metric Geographic Routing for Vehicular Ad hoc Networks

Maintaining durable connectivity during data forwarding in Vehicular Ad hoc Networks has witnessed significant attention in the past few decades with the aim of supporting most modern applications of Intelligent Transportation Systems (ITS). Various techniques for next hop vehicle selection have been suggested in the literature. Most of these techniques are based on selection of next hop vehicles from fixed forwarding region with two or three metrics including speed, distance and direction, and avoid many other parameters of urban environments. In this context, this paper proposes a Multi-metric Geographic Routing (M-GEDIR) technique for next hop selection. It selects next hop vehicles from dynamic forwarding regions, and considers major parameters of urban environments including, received signal strength, future position of vehicles, and critical area vehicles at the border of transmission range, apart from speed, distance and direction. The performance of M-GEDIR is evaluated carrying out simulations on realistic vehicular traffic environments. In the comparative performance evaluation, analysis of results highlight the benefit of the proposed geographic routing as compared to the state-of-the-art routing protocols

Lightweight Simulation of Hybrid Aerial- and Ground-based Vehicular Communication Networks

Cooperating small-scale Unmanned Aerial Vehicles (UAVs) will open up new application fields within next-generation Intelligent Transportation Sytems (ITSs), e.g., airborne near field delivery. In order to allow the exploitation of the potentials of hybrid vehicular scenarios, reliable and efficient bidirectional communication has to be guaranteed in highly dynamic environments. For addressing these novel challenges, we present a lightweight framework for integrated simulation of aerial and ground-based vehicular networks. Mobility and communication are natively brought together using a shared codebase coupling approach, which catalyzes the development of novel context-aware optimization methods that exploit interdependencies between both domains. In a proof-of-concept evaluation, we analyze the exploitation of UAVs as local aerial sensors as well as aerial base stations. In addition, we compare the performance of Long Term Evolution (LTE) and Cellular Vehicle-to-Everything (C-V2X) for connecting the ground- and air-based vehicles

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Performance Evaluation of Routing Protocols for Vehicle Re-Routing in ITS-based Vehicular Networks

This study aims to assess the performance of routing protocols in Intelligent Transportation System (ITS)-based vehicular networks, specifically in accident and highway scenarios. The effective management of traffic flow in these situations is crucial for ensuring the safety and smooth operation of vehicular networks. Therefore, it is imperative to evaluate routing protocols to identify the most suitable one for these scenarios. The evaluation considers various commonly used routing protocols in vehicular networks, including Ad hoc On-Demand Distance Vector (AODV), Ad hoc On-Demand Multipath Distance Vector (AOMDV), and Destination-Sequenced Distance Vector (DSDV). The evaluation is based on several performance metrics, such as packet delivery ratio, end-to-end delay, network throughput, normalized routing load, and routing overhead. These metrics provide insights into the effectiveness and efficiency of the routing protocols in handling re-routing in accident and highway scenarios. The research is divided into two modules, Module I and Module II, to evaluate the effectiveness of routing protocols in these distinct scenarios using the NS2 simulation tool. The simulation results are analyzed and compared to determine the performance of the routing protocols in each module. The findings indicate that AODV consistently achieves the highest throughput, packet delivery ratio, and lowest end-to-end delay, routing overhead, and normalized routing load, followed by AOMDV and then DSDV. The findings of this study contribute to the understanding of the strengths and weaknesses of different routing protocols in accident and highway scenarios. This knowledge can assist in the development of more efficient and reliable routing protocols for vehicular networks

A Multi-hop Mobile Networking Test-bed for Telematics

An onboard vehicle-to-vehicle multi-hop wireless networking system has been developed to test the realworld performance of telematics applications. The system targets emergency and safety messaging, traffic updates, audio/video streaming and commercial announcements. The test-bed includes a Differential GPS receiver, an IEEE 802.11a radio card modified to emulate the DSRC standard, a 1xRTT cellular-data connection, an onboard computer and audio-visual equipment. Vehicles exchange data directly or via intermediate vehicles using a multi-hop routing protocol. The focus of the test-bed is to (a) evaluate the feasibility of high-speed inter-vehicular networking, (b) characterize 5.8GHz signal propagation within a dynamic mobile ad hoc environment, and (c) develop routing protocols for highly mobile networks. The test-bed has been deployed across five vehicles and tested over 400 miles on the road