Vehicle sensor networks (VSNs) are ushering in a promising future by enabling more intelligent transportation systems and providing a more efficient driving experience. However, because of their inherent openness, VSNs are subject to a large number of potential security threats. Although various authentication schemes have been proposed for addressing security problems, they are not suitable for VSN applications because of their high computation and communication costs. Chuang and Lee have developed a trust-extended authentication mechanism (TEAM) for vehicle-to-vehicle communication using a transitive trust relationship, which they claim can resist various attacks. However, it fails to counter internal attacks because of the utilization of a shared secret key. In this paper, to eliminate the vulnerability of TEAM, an enhanced privacy-preserving authentication scheme for VSNs is constructed. The security of our proposed scheme is proven under the random oracle model based on the assumption of the computational Diffie–Hellman problem.

This article investigates the high-reliable data transmission for multihop and multichannel wireless sensor networks (WSNs), which jointly optimizes the channel allocation and channel access mechanisms. We propose a novel wireless paradigm empowered by mobile-edge computing (MEC) and deep reinforcement learning (DRL) to improve the data process ability of WSNs and formulate the joint resource allocation problem for reliability maximization as a partially observable Markov decision process (POMDP). Meanwhile, we introduce the distributed decision-making (DDM) framework to decouple channel optimization into two subproblems: 1) channel allocation and 2) channel access. Correspondingly, we present an asynchronous channel allocation algorithm for multiagent scenario and enable the neighbor cooperation to tackle the nonstationary problem, which can significantly improve the network convergence speed. Besides, we present a collision-free channel access algorithm including three submodules that can simultaneously eliminate vanishing nodes, hidden terminal, and exposed terminal problems in large-scale WSNs. Simulation results demonstrate that the proposed algorithm significantly improves network performance in terms of convergence, throughput, collision, and packet delivery ratio (PDR).