Stochastic Steepest-Descent Optimization of Multiple-Objective Mobile Sensor Coverage

—We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the scheduling decision is effected by a simple constant-time coin toss operation only. We apply our method to the scheduling of a mobile sensor’s coverage time among a set of points of interest (PoIs). The coverage algorithm is guided by a Markov chain wherein the sensor at PoI i decides to go to the next PoI j with transition probability pij. We use steepest descent to compute the transition probabilities for optimal tradeoff between two performance goals concerning the distributions of per-PoI coverage times and exposure times, respectively. We also discuss how other important goals such as energy efficiency and entropy of the coverage schedule can be addressed. For computational efficiency, we show how to optimally adapt the step size in steepest descent to achieve fast convergence. However, we found that...

• Type: Conference
• Year: 2010
• Where: ICDCS
• Authors: Chris Y. T. Ma, David K. Y. Yau, Nung Kwan Yip, Nageswara S. V. Rao, Jiming Chen
• Keywords: Distributed And Parallel Computing, ICDCS 2010, Optimal Control Parameters, Steepest Descent, Steepest Descent Method