Multiobjective K-Connected Deployment and Power Assignment in WSNs Using Constraint Handling

The K-connected deployment and power assignment problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing both the network coverage and lifetime under K-connectivity constraints, in a single run. It is shown that the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem specific knowledge for increasing its efficiency. Since the K-connected DPAP requires constraint handling, several techniques are investigated and compared, including a DPAP-specific repair heuristic (RH) that transforms an infeasible network design into a feasible one and maintains the MOEA/D's efficiency simultaneously. This is achieved by alternating between two repair strategies, which favor one objective each. Simulation results have shown that the MOEA/D-RH performs better than the popular constrained NSGA-II in several network instances.