Lifetime maximization and resource management in wireless sensor networks

A primary concern in the operation of cooperative wireless sensor networks is the issue of energy efficiency and lifetime maximization. This thesis addresses the problem of lifetime maximization under unequal and time-varying channel conditions and subject to a distortion constraints. The standard method for solving such dynamic stochastic problems is dynamic programming, which is a discrete method that must heavily quantize all quantities, is exponentially complex with respect to the number of states, and requires global information exchange among sensors at each iteration. Our goal in this thesis is to develop a practical, low-complexity solution which does not have the downfalls of the previous methods.;Three signalling schemes are considered in the context of joint estimation: orthogonal channels, beamforming, and shared channel with no phase information. In the final chapter the problem of binary detection in sensor networks is studied.;In the case of orthogonal channels, the distortion constraint is relaxed. We propose a simplified method via a decomposition approximation: The SNR requirement at the destination is "divided" between sensors according to their battery powers and radio link statistics, and then each of the sensors' operating power is carefully controlled over time to maximize the lifetime as well as maintain a certain required SNR at the receiver.;For the other two channel configurations, we consider the special case in which channels coefficients are independently, identically distributed. We argue that under these conditions a power scheduling scheme that minimizes the power consumption over any transmission period, maximizes the expected lifetime of the network. A closed from optimal solution is obtained for shared channels with phase information. In the case of shared channel with no phase information, the original problem is reduced to a standard linear programming problem and a closed form solution is obtained.;We also consider the problem of binary hypothesis detection in wireless sensor networks under power constraint. The objective is to solve the resource allocation problem for this distributed detection problem and find a suitable operating point for the network. We use the Neyman-Pearson (NP) criteria and design suitable transmission schemes and a fusion center detector, so that subject to transmission power constraints, detection probability is optimized. It is shown that the corresponding optimization problem is convex, optimality conditions are derived, and solutions for transmit powers as well as overall detector are obtained. Numerical simulations verify our results.