Seek-and-destroy algorithm for optimal resource allocation and security analysis in software-defined vehicular networks

Purpose Software-define vehicular networks (SDVN) assure the direct programmability for controlling the vehicles with improved accuracy and flexibility. In this research, the resource allocation strategy is focused on which the seek-and-destroy algorithm is implemented in the controller in such a way that an effective allocation of the resources is done based on the multi-objective function. Design/methodology/approach The purpose of this study is focuses on the resource allocation algorithm for the SDVN with the security analysis to analyse the effect of the attacks in the network. The genuine nodes in the network are granted access to the communication in the network, for which the factors such as trust, throughput, delay and packet delivery ratio are used and the algorithm used is Seek-and-Destroy optimization. Moreover, the optimal resource allocation is done using the same optimization in such a way that the network lifetime is extended. Findings The security analysis is undergoing in the research using the simulation of the attackers such as selective forwarding attacks, replay attacks, Sybil attacks and wormhole attacks that reveal that the replay attacks and the Sybil attacks are dangerous attacks and in future, there is a requirement for the security model, which ensures the protection against these attacks such that the network lifetime is extended for a prolonged communication. The achievement of the proposed method in the absence of the attacks is 84.8513% for the remaining nodal energy, 95.0535% for packet delivery ratio (PDR), 279.258 ms for transmission delay and 28.9572 kbps for throughput. Originality/value The seek-and-destroy algorithm is one of the swarm intelligence-based optimization designed based on the characteristics of the scroungers and defenders, which is completely novel in the area of optimizations. The diversification and intensification of the algorithm are perfectly balanced, leading to good convergence rates.