Pioneering advanced security solutions for reinforcement learning-based adaptive key rotation in Zigbee networks.

In the rapidly evolving landscape of Internet of Things (IoT), Zigbee networks have emerged as a critical component for enabling wireless communication in a variety of applications. Despite their widespread adoption, Zigbee networks face significant security challenges, particularly in key management and network resilience against cyber attacks like distributed denial of service (DDoS). Traditional key rotation strategies often fall short in dynamically adapting to the ever-changing network conditions, leading to vulnerabilities in network security and efficiency. To address these challenges, this paper proposes a novel approach by implementing a reinforcement learning (RL) model for adaptive key rotation in Zigbee networks. We developed and tested this model against traditional periodic, anomaly detection-based, heuristic-based, and static key rotation methods in a simulated Zigbee network environment. Our comprehensive evaluation over a 30-day period focused on key performance metrics such as network efficiency, response to DDoS attacks, network resilience under various simulated attacks, latency, and packet loss in fluctuating traffic conditions. The results indicate that the RL model significantly outperforms traditional methods, demonstrating improved network efficiency, higher intrusion detection rates, faster response times, and superior resource management. The study underscores the potential of using artificial intelligence (AI)-driven, adaptive strategies for enhancing network security in IoT environments, paving the way for more robust and intelligent Zigbee network security solutions.

Optimization of cobalt-based MOFs for super-capacitor electrode materials of new energy vehicle.

Super-capacitors (SCs), as new energy conversion storage elements, have attracted much attention, but there is still a research gap in the design of electrode materials. In this study, the optimization scheme of Metal-Organic Frameworks (MOFs) and cobalt-based MOF composites as electrode materials for SCs in new energy vehicles is explored, and a series of experiments are conducted to evaluate their performance. Scanning Electron Microscope (SEM) images reveal that the cobalt-based MOF composites have a surface morphology of particles with uniform distribution. The electrochemical performance test results show that the specific capacitance of the cobalt-based MOF composites is much higher than the sum of the two individual electrode materials and presents a remarkable increase with the scanning rate. Additionally, in the constant current charge-discharge test, cobalt-based MOF composites exhibit the longest charge-discharge time and good symmetry. Electrolyte particle contact tests for samples at different preparation temperatures display that high-temperature samples have better structural stability and electrolyte ion contact. In Cyclic Voltammetry (CV) and Galvanostatic Charge Discharge (GCD) tests, the 250 °C sample demonstrates the best electrochemical properties and the highest specific capacitance (269 F/g). Moreover, as the current density increases, the specific capacitance of the 600 °C sample decreases at a lower rate, showing stronger stability. However, the use of cobalt-based MOF materials may pose environmental and safety risks, such as the environmental impact of cobalt resource mining, instability under high-temperature conditions, and the possible production of hazardous substances. Therefore, these factors need to be fully considered when designing and using SCs to ensure the environmental friendliness and safety of cobalt-based MOFs. These results provide an important reference for selecting and optimizing electrode materials for SCs in new energy vehicles. Furthermore, this study offers research suggestions for improving new energy materials, filling the research gaps in related fields, and supporting the further development of SC technology.