Applying the Generate and Solve Methodology in the Problem of Dynamic Coverage and Connectivity in Wireless Sensor Networks

High power consumption efficiency in wireless sensor networks is always desirable. One way to deal with this issue is using a linear integer programming model based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic component with an exact solver. GS has been recently introduced in the literature in order to solve Problem of Dynamic Coverage and Connectivity in Wireless Sensor Networks, showing promising results. The GS framework includes a metaheuristic engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver. The use of linear integer programming approach is limited to a certain level of complexity that sometimes is not enough for a real size network.