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The secret key is stored in an ideal tamper-proof device so that a vehicle can implement a secure authentication with the road-side units (RSUs) and other drivers. However, some adversaries can capture the secret key by physical attacks. To resist physical attacks, we propose a physical-preserving authentication based on a physical unclonable function for vehicular ad hoc networks. In the proposed scheme, a physical unclonable function is deployed on the vehicle and the RSU to provide a challenge–response mechanism. A secret key is only generated by the challenge–response mechanism when it is needed, which eliminates the need to store a long-term secret key. As a result, this prevents secret keys from being captured by adversaries, improving system security. In addition, route planning is introduced into the proposed scheme so that a vehicle can obtain the authentication key of RSUs on its route before vehicle-to-infrastructure authentication, which greatly speeds up the authentication when the vehicle enters the RSUs' coverage. Furthermore, a detailed analysis demonstrates that the proposed scheme achieves security objectives in vehicular ad hoc networks. Ultimately, when contrasted with similar schemes, the performance assessment demonstrates that our proposed scheme surpasses others in terms of computational overhead, communication overhead and packet loss rate. [ABSTRACT FROM AUTHOR]

In target-oriented multi-agent tasks, agents collaboratively achieve goals defined by specific objects, or targets, in their environment. The key to success is the effective coordination between agents and these targets, especially in dynamic environments where targets may shift. Agents must adeptly adjust to these changes and re-evaluate their target interactions. Inefficient coordination can lead to resource waste, extended task times, and lower overall performance. Addressing this challenge, we introduce the regulatory hierarchical multi-agent coordination (RHMC), a hierarchical reinforcement learning approach. RHMC divides the coordination task into two levels: a high-level policy, assigning targets based on environmental state, and a low-level policy, executing basic actions guided by individual target assignments and observations. Stabilizing RHMC's high-level policy is crucial for effective learning. This stability is achieved by reward regularization, reducing reliance on the dynamic low-level policy. Such regularization ensures the high-level policy remains focused on broad coordination, not overly dependent on specific agent actions. By minimizing low-level policy dependence, RHMC adapts more seamlessly to environmental changes, boosting learning efficiency. Testing demonstrates RHMC's superiority over existing methods in global reward and learning efficiency, highlighting its effectiveness in multi-agent coordination. [ABSTRACT FROM AUTHOR]

Frequent accidents have occurred in coal mine enterprises; therefore, raising the technological level of coal mine safety monitoring systems is an urgent problem. Wireless sensor networks (WSN), as a new field of research, have broad application prospects. This paper proposes a Web of Things- (WoT-) based remote monitoring system that takes full advantage of wireless sensor networks in combination with the CAN bus communication technique that abstracts the underground sensor data and capabilities into WoT resources to offer services using representational state transfer (REST) style. We also present three different implemented scenarios for WoT-based remote monitoring systems for coal mine safety, for which the system performance has been measured and analyzed. Finally, we describe our conclusions and future work.