Abstract

In this article, we study the coverage problem for heterogeneous wireless sensor networks where different targets need to be covered (sensed) by different types of wireless sensors running at possibly different sampling rates as well as different initial energy reserve. The objective is to maximize network lifetime while fulfilling diversified coverage constraints, i.e., different targets may require different sensing quality in terms of the number of transducers, sampling rate, sensing data rate, etc. The problem is particularly challenging since we need to consider both connectivity and routing requirements. To conquer this combinational complexity, we formulate a lifetime maximization problem, which is general and allows unprecedented diversity in coverage requirements, sampling rates, transmission energy consumption models, communication ranges, and target sensing ranges. Furthermore, to efficiently solve the optimization problem, we propose a column generation based approach, where a column corresponding to a feasible solution; our idea is to find a column with steepest ascent in lifetime, based on which we iteratively search for the solution of the maximum lifetime problem. To speed up the convergence rate, we generate an initial solution through a novel random selection algorithm. Through extensive simulations, we systematically study the effect of sampling rates, transmission energy consumption models, communication ranges, and sensing ranges on the lifetime. Several interesting insights have been revealed.

Keywords:

target coverage; lifetime optimization; heterogeneous wireless sensor networks