Abstract:Summary
Many cyber‐attach schemes and coding models established by algebra tools are build to address the problem of security of cyber‐pysical systems (CPS). As an important field of algebra computing, Boolean Polynomial System Solving (PoSSo) problem plays a very important role in many algebra applications. In this article, we propose an efficient Parallel Boolean Characteristic Set method (PBCS) under the high‐performance computing environment to improve the efficiency of solving Boolean polynomial systems. … Show more
“…Cyberattack schemes and coding models build by algebra tools are usually used to address the security problem of CPS. The authors presented an efficient parallel Boolean characteristic set method 3 to improve the efficiency of solving Boolean polynomial system problems in CPS.…”
“…Cyberattack schemes and coding models build by algebra tools are usually used to address the security problem of CPS. The authors presented an efficient parallel Boolean characteristic set method 3 to improve the efficiency of solving Boolean polynomial system problems in CPS.…”
“…Cyber‐physical systems (CPS) are a kind of systems that deeply intertwine physical objects and software components through gathering smart sensing, computation, control and networking techniques 1,2 . In recent years, the advance in information technology has dynamically fueled the deployment of numerous emerging CPS applications such as autonomous automobile systems, healthcare monitoring, and process control systems.…”
In recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.
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