Intelligent autonomous cyber-physical systems and applications

This special issue aims to bring out recent advances in cyber-physical systems (CPS) and its applications. CPS is a new emerging paradigm with widespread applications such as intelligent manufacturing, smart grid, smart manufacturing, etc. Usually, CPS applications are complex, and it is often difficult to build and manage in a real-time environment. Currently, energy management remains to be a critical issue, especially with automobile industries. To effectively deal with energy optimisation problems across smart scheduling systems, a Multiple Fuzzy Aggravated Energy Scheduling Approach (MFAESA) is proposed to incorporate fuzzy algorithms to deal with energy loss problems. This algorithm searches for the network idle time and optimises the energy usage of IoT efficiently assisted automobile industries. This increases performance measures and reduces system execution time. Further, this approach is highly accurate and protects energy loss across the IoT network in a more optimised way. Supply chain management is one of the most prominent applications of CPS. This special issue also focuses on exploring the most accurate and fault-tolerant solutions for CPS assisted supply chain management systems. Devices, including target hardware, software, and operating environment, are more susceptible to vulnerable operations when functioning across the IoT systems. A linear approximation based fuzzy model is used to identify defective components in the supply chain management systems. The use of roughest approximation techniques eliminates the defects in the identification of faulty components and eliminates ambiguity measures. In addition, this approach helps to measure faults across the dynamic modules of the system. Currently, information management across physical networks remains to be a significant issue. Especially with cybersecurity assisted IoT systems. This special issue presents a deep reinforcement learning-based solution to deal with enterprise information management and its integration with intelligent physical systems. It efficiently