A dynamic multiobjective hybrid approach for designing Wireless Sensor Networks

The increase in the demand for wireless sensor networks (WSNs) has intensified studies which aim to obtain energy-efficient solutions, since the energy storage limitation is critical in those systems. However, there are other aspects which usually must be ensured in order to provide an efficient design of WSNs, such as area coverage and network connectivity. This paper proposes a multiobjective hybrid approach for solving the dynamic coverage and connectivity problem (DCCP) in flat WSN subjected to node failures. It combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a genetic algorithm, with a local online algorithm (LoA), which is intended to restore the network coverage when one or more failures occur. The proposed approach is compared with an integer linear programming (ILP) based approach and a similar mono-objective approach with regard to coverage, energy consumption and residual energy of the solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can obtain good solutions with a considerably smaller computational cost than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.