Clustering‐based routing protocol using gray wolf optimization and technique for order of preference by similarity to ideal solution algorithms in the vehicular ad hoc networks

• Published in: Concurrency and computation Vol. 34; no. 23
• Main Authors: Kheradmand, Behbod, Ghaffari, Ali, Soleimanian Gharehchopogh, Farhad, Masdari, Mohammad
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 25.10.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Clustering; Data transmission; gray wolf optimization; Heuristic methods; Mobile ad hoc networks; Network latency; Network topologies; Optimization; Particle swarm optimization; Passenger safety; Performance measurement; routing; Routing (telecommunications); Similarity; TOPSIS; vehicular ad hoc network
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.7209

Summary In a vehicular ad‐hoc network (VANET), each vehicle is equipped with an on‐board unit to communicate vehicle to vehicle or vehicle to fixed infrastructure. VANET technology is offered to provide many facilities to passengers and drivers, including safety, entertainment, mobile commerce, driver assistance, and emergency alarms. VANET has unique features such as high‐speed node mobility and network topology dynamics. These special features cause many problems such as increased transmission delays and packet loss. On the other hand, providing a good routing plan for VANET is a critical issue. Therefore, this article proposes a cluster‐based routing using in‐vehicle meta‐heuristic algorithms (CRMHA‐VANET) which has two phases. In the first stage, the vehicles are clustered and the most suitable cluster head (CH) is selected using the gray wolf optimization algorithm (GWO). In the next step, the next suitable CH is selected for data transmission in direct paths using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the proposed method is analyzed through several criteria such as package delivery rate, end‐to‐end delay and throughput. CRMHA‐VANET results in a 10% to 25% improvement over all performance metrics, that is, packet delivery rate, latency, and throughput, over CRBP (clustering routing based on PSO [particle swarm optimization]), WCV (weight based clustering for VANET), and AODV‐CD methods.