LAUSR

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and ‘security compromise’ are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and ‘number of packets sent’, which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).