Abstract
We propose an online, multiobjective optimization (MO) algorithm to efficiently schedule the nodes of a wireless sensor network (WSN) and to achieve maximum lifetime. Instead of dealing with traditional grid or uniform coverage, we focus on the differentiated or probabilistic coverage where different regions require different levels of sensing. The MO algorithm helps to attain a better tradeoff among energy consumption, lifetime, and coverage. The algorithm can be run every time a node failure occurs due to power failure of the node battery so that it may reschedule the network. This scheduling is modeled as a combinatorial, multiobjective, and constrained optimization problem with energy and noncoverage as the two objectives. The basic evolutionary multiobjective optimizer used is known as decomposition-based multiobjective evolutionary algorithm (MOEA/D) which is modified by integrating the concept of fuzzy Pareto dominance. The performance of the resulting algorithm, which is called MOEA/DFD, is compared with the performance of the original MOEA/D, which is another very well known MO algorithm called nondominated sorting genetic algorithm (NSGA-II), and an IBM optimization software package called CPLEX. In all the tests, MOEA/DFD is observed to outperform all other algorithms.