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sequential_decision_algorithms_for_as-you-go_deployment_of_wireless_relay_network_along_a_line

“Sequential decision algorithms for as-you-go deployment of wireless relay network along a line”

Dr. Arpan Chattopadhyay, USC

Wednesday, March 1, 2017 2:00 – 3:00PM EEB 248

Abstract: We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, starting from a sink node (e.g., a base-station), and proceeds towards a source node (e.g., a sensor) which is at an a priori unknown location. At equally spaced locations, he makes link quality measurements to the previous relay, and deploys relays at some of these locations, with the aim to connect the source to the sink by a multihop wireless path. In this paper, we consider two approaches for impromptu deployment: (i) the deployment agent can only move forward (which we call a pure as-you-go approach), and (ii) the deployment agent can make measurements over several consecutive steps before selecting a placement location among them (the explore-forward approach). We consider a very light traffic regime, and formulate the problem as a Markov decision process, where the trade-off is among the power used by the nodes, the outage probabilities in the links, and the number of relays placed per unit distance. We obtain the structures of the optimal policies for the pure as-you-go approach as well as for the explore-forward approach. We also consider natural heuristic algorithms, for comparison. Numerical examples show that the explore-forward approach significantly outperforms the pure as- you-go approach in terms of network cost. Next, we propose learning algorithms for the explore-forward approach and the pure as-you-go approach, based on single and two timescale Stochastic Approximation, which asymptotically converge to the set of optimal policies, without using any knowledge of the radio propagation model. We demonstrate numerically that the learning algorithms can converge (as deployment progresses) to the set of optimal policies reasonably fast and, hence, can be practical model-free algorithms for deployment over large regions. Finally, we demonstrate the end-to-end traffic carrying capability of such networks via field deployment.

Bio: 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 in the year 2010 and 2015, respectively. Then he worked as a postdoc in the group DYOGENE of INRIA/ENS Paris. He joined EE department, USC as a postdoc from November 2016. His host is Prof. Urbashi Mitra. His research interests include optimization, learning and control of wireless networks and cyber-physical systems.

sequential_decision_algorithms_for_as-you-go_deployment_of_wireless_relay_network_along_a_line.txt · Last modified: 2017/02/27 13:52 by ashutosh_nayyar