Reliability-Driven End–End–Edge Collaboration for Energy Minimization in Large-Scale Cyber-Physical Systems

In recent years, cyber-physical systems (CPS) have been widely deployed in industrial manufacturing fields and our daily living domains. End–end–edge collaboration, coupling mobile edge computing and device-to-device communication, is a promising computation paradigm to meet the stringent real-time demands of large-scale CPS applications. However, energy and reliability concerns should be carefully addressed in end–end–edge collaboration-empowered large-scale CPS due to the limited energy supply and inherent openness characteristic of end devices. In this article, we explore the reliability-driven energy optimization of end–end–edge collaborated large-scale CPS applications. We develop a reliability-driven end–end–edge collaboration approach to deal with the energy minimization problem. Our approach first designs a clustering method to quantify differentiated energy demands by analyzing the energy dissipation composition of heterogeneous applications. Afterward, our approach leverages incremental control and swarm intelligence-based techniques to obtain energy-efficient reliability-guaranteed task offloading solutions for differentiated application clusters. Experimental results reveal that our approach achieves 51.48% energy savings compared with peer algorithms.