Metaheuristic Algorithm with Deep Learning Based Secured Lifetime Maximization in Vehicular Adhoc Networks

Wireless Sensor Networks (WSN)s along with Vehicular Adhoc Networks (VANETs) form two vital parts of contemporary smart systems because they help collect information and enable real-time response and decision-making in multiple applications. The effectiveness of these networks remains restricted due to their fluid topology designs as well as network security dangers and operational performance boundaries. The evaluation discusses artificial intelligence methods linked with metaheuristic approaches which optimize secure lifetimes for VANETs and WSNs. Energy consumption strategies and resource allocation as well as routing methods are reviewed using multiple metaheuristic algorithms. The research examines combination architectures of deep learning systems which handle real-time operational safety and efficiency while utilizing metaheuristic optimization for network optimization. A thorough evaluation of current methods shows their benefits together with their weaknesses as well as possible next steps for research. Research results showcase that the combination improves both WSNs and VANET network performance in terms of security and reliability.