Motivated by the internet-of-things and sensor networks for cyber-physical systems, the problem of dynamic sensor activation for the tracking of a time-varying process is examined. The tradeoff is between energy efficiency, which decreases with the number of active sensors, and fidelity, which increases with the number of active sensors. The problem of minimizing the time- averaged mean-squared error over infinite horizon is examined under a constraint on the mean number of active sensors. The proposed methods artfully combine three key ingredients: Gibbs sampling, stochastic approximation for learning, and modifications to consensus algorithms to create a high performance, energy efficient tracking mechanisms with active sensor selection. The following progression of scenarios are considered: centralized tracking of an i.i.d. process, distributed tracking of an i.i.d. process and finally distributed tracking of a Markov chain. For i.i.d. process with known distribution, convergence to the global optimal solution with high probability is proved. The main challenge of the i.i.d. case is that the process has a distribution parameterized by a known or unknown parameter which must be learned. The key theoretical results prove that the proposed algorithm for the centralized tracking problem converges to a local optimum for the i.i.d process case with unknown parametric distribution; numerical results suggest that global optimality is in factachieved in some cases. If time permits, distributed tracking algorithms for the i.i.d process and a Markov chain, based on consensus, Kalman-consensus filtering and stochastic approximation, will also be discussed.
Arpan Chattopadhyay obtained his B.E. in Electronics and Telecommunication Engineering from Jadavpur University, Kolkata, India in the year 2008, and M.E. and Ph.D in Telecommunication Engineering from Indian Institute of Science, Bangalore, India in the year 2010 and 2015, respectively. He is currently working in the Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles as a postdoctoral researcher. Previously he worked as a postdoc in INRIA/ENS Paris. His research interests include various design, security, control and learning problems in wireless networks, internet-of-things and cyber-physical systems