Leveraging Biologically Inspired Models for Cyber-Physical Systems Analysis Open Access
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Cyber-physical systems (CPS) are systems composed of distributed sensors, physical actuators and controlling computers that are interconnected through a computer network. Notable examples include: electric utility “smart grids” that can sense and optimize power distribution, transportation systems, and healthcare and medical systems. As an emerging area of research, CPS engineering combines and extends the more mature disciplines of computing, control theory and communications engineering. As CPS complexity increases, system level trade studies become more challenging due to the combined interaction of the computing, network communications, and physical sensor and actuator elements. Although fundamentally different, complex CPS and biological systems share common attributes that suggest the use of similar modeling approaches. This work investigates the utility of employing modeling techniques developed for the analysis of biological systems for the system level trade study analysis of a distributed robotic wireless sensor network CPS. A multi-objective optimization study, using a genetic algorithm based optimizer, was conducted on four slightly different versions of a dynamic model of a simple distributed robotic sensor system to assess the effect of incorporating a biological mathematical model on the quality of the Pareto fronts generated by the optimizer. An improvement in Pareto front quality was found after incorporating a biological mathematical model.