Efficient solutions framework for optimal multitask resource assignments for data fusion in wireless sensor networks

Abstract

Motivated by the need to judiciously allocate scarce sensing resources to attain the highest benefit for the applications that sensor networks serve, in this article we develop a flexible solutions methodology for maximizing the overall reward attained, subject to constraints on the resource demands under fairly general reward or demand functions. We map a broad class of related problems for data fusion in wireless sensor networks into an integer programming problem and provide an iterative Lagrangian relaxation technique to solve it. Each iteration step involves solving for a maximum-weight independent set of an appropriately constructed graph, which, in many cases, can be obtained in polynomial time. We apply our methodology to the problem of tracking targets moving over a period of time through a nonhomogeneous, energy-constrained sensor field.With rewards represented by the quality of information attained in tracking, we study its tradeoffs and relationship with energy consumption and periodic measurement taking. We finally illustrate other applications of our framework in sensor networks. © 2014 Association for Computing Machinery.