Power Optimal Connectivity and Coverage in Wireless Sensor Networks Metin Inanc Malik Magdon-Ismail Bülent Yener Simulations Stochastic processes/Queueing theory Mathematical programming/Optimization This work addresses the problem of minimizing power consumption in each sensor node locally while ensuring two global (i.e., network wide) properties: (i) communication connectivity, and (ii) sensing coverage. A sensor node saves energy by suspending its sensing and communication activities according to a Markovian stochastic process. It is shown that a power level to induce a coverage radius w(n)/n is sufficient for connectivity provided that w(n) is a function approaching to infinity. The paper presents a Markov model and its solution for steady state distributions to determine the operation of a single node. Given the steady state probabilities, we construct a non-linear optimization problem to minimize the power consumption. Simulation studies to examine the collective behavior of large number of sensor nodes produce results that are predicted by the analytical model. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY cs-03-06
Power Optimal Connectivity and Coverage in Wireless Sensor Networks
Metin Inanc
Malik Magdon-Ismail
Bülent Yener
Simulations
Stochastic processes/Queueing theory
Mathematical programming/Optimization
This work addresses the problem of minimizing power consumption in each sensor node locally while ensuring two global (i.e., network wide) properties: (i) communication connectivity, and (ii) sensing coverage. A sensor node saves energy by suspending its sensing and communication activities according to a Markovian stochastic process. It is shown that a power level to induce a coverage radius w(n)/n is sufficient for connectivity provided that w(n) is a function approaching to infinity. The paper presents a Markov model and its solution for steady state distributions to determine the operation of a single node. Given the steady state probabilities, we construct a non-linear optimization problem to minimize the power consumption. Simulation studies to examine the collective behavior of large number of sensor nodes produce results that are predicted by the analytical model.
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY
cs-03-06