Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

Abstract

In VANETs (Vehicular Ad-hoc Networks), the number of vehicles increased continuously, leading to significant traffic problems like traffic congestion, a feasible path, and associated events like accidents. Though, the Intelligent Transportation System (ITS) providing excellent services, such as safety applications and emergency warnings. However, ITS has limitations regarding traffic management tasks, scalability, and flexibility because of the enormous number of vehicles. Therefore, extending the traditional VANET architecture is indeed a must. Thus, in the recent period, the design of the SD-VANETs (Software-Defined Networking defined VANETs) has gained significant interest and made VANET more intelligent. The SD-VANET architecture can handle the aforesaid VANET challenges. The centralized (logically) SDN architecture is programmable and also has global information about the VANET architecture. Therefore, it can effortlessly handle scalability, traffic management, and traffic congestion issues. The traffic congestion problem leads to longer trip times, decreases the vehicles' speed, and prolong average endto-end delay. Though, somewhere, some routes in the network are available with capacity, which can minimize the congestion problem and its characteristics. Therefore, we proposed heuristic algorithms called Congestion-Free Path (CFP) and Optimize CFP (OCFP), in SD-VANET architecture. The proposed algorithms address the traffic congestion issue and also provide a feasible path (less end-to-end delay) for a vehicle in VANET. We used the NS-3 simulator to evaluate the performance of the proposed algorithms, and for generating a real scenario of VANET traffic; we use the SUMO module. The results show that the proposed algorithms decrease road traffic congestion drastically compared to exiting approaches.

An Optimization Framework for Data Collection in Software Defined Vehicular Networks

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Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

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Ontological Model for Cohesive Smart Health Services Management

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