ESSAIoV: Enhanced Sparrow Search Algorithm-Based Clustering for Internet of Vehicles

The Internet of Things (IoT) has led to the development of a new network paradigm called the Internet of Vehicles (IoV), which has evolved from the traditional Vehicular Ad hoc Networks (VANETs). Vehicle communication becomes incredibly challenging in the IoV network due to high vehicle speed and rapid network topology changes. In a large network setting, the complicated road conditions and the huge number of vehicles place greater demands on IoV communication performance. Vehicle clustering is an effective way to enhance communication capability among vehicles. However, choosing stable cluster heads (CHs) and reducing the number of clusters are challenging while organizing the network by clusters. A clustering algorithm that integrates the Genetic Algorithm and Sparrow Search Algorithm called Enhanced Sparrow Search Algorithm for IoV (ESSAIoV) is proposed. ESSAIoV has the ability to operate in the IoV high mobility nodes scenario and produces optimized clusters for effective communication. It uses a fitness function that combines the benefits of weight-based and mobility-based clustering approaches. The employed fitness function considers mobility metrics along the cluster distance to construct the fewest clusters with stable CHs. The effectiveness of the proposed ESSAIoV algorithm is assessed through simulations. The simulation results show that ESSAIoV outperforms the state-of-the-art algorithms in terms of the number of clusters and the average cluster lifetime and so it enhances the network performance and subsequently reduces the vehicular network's routing cost.