Communication in VANETs is vulnerable to various types of security attacks since it is constructed based on an open wireless connection. Therefore, a lightweight authentication (LIAU) scheme for vehicle-to-vehicle communication is proposed in this paper. The LIAU scheme requires hash operations and uses cryptographic concepts to transfer messages between vehicles, in order to maintain the required security. Moreover, we made the LIAU scheme lightweight by introducing a small number of variable parameters in order to reduce the storage space. Performance analysis shows that the LIAU scheme is able to resist various types of security attacks and it performs well in terms of communication cost and operation time.

Vehicular ad hoc networks (VANETs) have attracted significant attention in academia insofar as they can provide reliable and secure communication between vehicles. It is thus essential to ensure security and preserve privacy. In this paper, we propose mix-context-based pseudonym changing privacy-preserving authentication (MPCPA). MPCPA introduces privacy protection through a mutual authentication mechanism to prevent attack-vehicles from sneaking into a VANET system. Moreover, it preserves the integrity of transmitted messages with an anonymous authentication mechanism. In addition, MPCPA adopts a mix-context-based pseudonym changing strategy to prevent vehicle tracking. A performance analysis demonstrates that MPCPA incurs low computational costs and offers a privacy-preserving scheme that is more secure than existing authentication schemes.

The rapid development of the social Internet of Things (IoT) enhances interconnectivity but also raises significant network security challenges, particularly from botnet attacks that disrupt system stability. Addressing this issue requires effective strategies to control botnet propagation in social IoT environments. This study develops a social IoT botnet propagation model incorporating social factors to analyze their influences on its propagation dynamics. Based on this, a social IoT botnet propagation control framework is constructed, formulating an optimization problem using Markov games. To solve the optimization problem, we propose social-dynamics deep recurrent Q-network (SD-DRQN), a novel deep reinforcement learning algorithm that integrates long short-term memory (LSTM) layers to improve learning in dynamic social IoT environments. Experimental results validate the performance of the proposed SD-DRQN across various social IoT scenarios, including complex real-world topologies. The algorithm demonstrates faster convergence, superior generalization, and practical applicability, making it an effective solution for botnet propagation control in real-world social IoT deployments.

In the Social Internet of Things (SIoT) environment, malware propagation is attracting more and more attention due to increasing damages. Markov chain models have been used to predict epidemic behavior qualitatively and quantitatively, but most of them model random propagation as a basic multiplicative factor. In this paper, we propose an epidemic model Susceptible-Infected without command-Infected with command (SII’), and derive an exact Markov chain for SIoT malware propagation. We also employ a Markov chain for an SIoT malware mitigation system that groups random devices alongside those with detected infections during the malware eradication process. This mitigation mechanism operates at the network scale, addressing the risks associated with large-scale SIoT deployments through a strategic, yet assertive, approach of widespread disconnections. Such a system effectively drives down the basic reproduction number to less than 1, preventing malware from gaining dominance over the network–all accomplished without modifying the recovery rate. We conducted experimental simulations of the proposed model’s dynamic predictions, and the experimental results show that the use of an exact Markov chain model better matches the benchmark results of our proposed model and also verifies the different effects of group-based mitigation in different SIoT contexts.

In order to alleviate energy consumption of wireless sensor networks (WSNs), energy consumption optimization-based clustering routing (ECOR) is proposed in this paper. In ECOR, network is gridding by hexagon. And there is only a cluster head (CH) in each hexagon, which makes the distribution of CHs more even. Residual energy of nodes and distance from the centroid of hexagon are used to elect CHs. For any a CH, dynamic time slot allocation strategy is adopted to allocate time slot for its cluster members. According to status of cluster members, the duration of time slot is dynamically adjusted. In intercluster communication, the Dijkstra algorithm is used to construct the shortest path between CHs in order to shorten the distance of transmitting data. Simulation results show that the ECOR algorithm outperforms the improved-Low Energy Adaptive Clustering Hierarchy (I-LEACH) algorithm in terms of distribution of CHs and energy consumption.