Optimizing the Spatio-temporal Distribution of Cyber-Physical Systems for Environment Abstraction

Cyber-physical systems (CPS) bridge the virtual cyber world with the real physical world. For representing a physical environment in cyber, CPS devices / nodes are assigned to collect data in a region of interest. In practice, the nodes seldom fully cover the region due to the restriction of quantity and cost. Hence, the sampled data are usually inadequate to describe the holistic environment. Recent researches mainly focus on the interpolation methods to generate an approximating model from the raw data. However, in this paper, we propose to study the spatio-temporal distribution of CPS nodes in order to obtain the crucial data for optimal environment abstraction. There are two target problems. First, when the environment changes little over time, what is the optimal spatial distribution of stationary nodes based on historical data? Second, when the environment is time-varying, what is the adaptive spatio-temporal distribution of mobile nodes? We show the NP hardness of the former problem and propose an approximation algorithm. For the latter problem, we develop a cooperative movement algorithm on nodes for achieving a curvature-weighted distribution pattern. A trace driven simulation based on real data of GreenOrbs project evaluates the performance of the proposed approaches.