Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation

This paper proposed a distributed genetic algorithm (DGA) to solve the energy efficient coverage (EEC) problem in the wireless sensor networks (WSN). Due to the fact that the EEC problem is Non-deterministic Polynomial-Complete (NPC) and time-consuming, it is wise to use a nature-inspired meta-heuristic DGA approach to tackle this problem. The novelties and advantages in designing our approach and in modeling the EEC problems are as the following two aspects. Firstly, in the algorithm design, we realized DGA in the multi-processor distributed environment, where a set of processors run distributed to evaluate the fitness values in parallel to reduce the computational cost. Secondly, when we evaluate a chromosome, different from the traditional model of EEC problem in WSN that only calculates the number of disjoint sets, we proposed a hierarchical fitness evaluation and constructed a two-level fitness function to count the number of disjoint sets and the coverage performance of all the disjoint sets. Therefore, not only do we have the innovations in algorithm, but also have the contributions on the model of EEC problem in WSN. The experimental results show that our proposed DGA performs better than other state-of-the-art approaches in maximizing the number of disjoin sets.