Fault tolerant deployment and topology control in wireless networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Power optimization in fault-tolerant topology control algorithms for wireless multi-hop networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Topology control in wireless ad hoc and sensor networks
ACM Computing Surveys (CSUR)
Energy-aware topology control for wireless sensor networks using memetic algorithms
Computer Communications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Lifetime maximization of sensor networks under connectivity and k-coverage constraints
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Optimal deployment of large wireless sensor networks
IEEE Transactions on Information Theory
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
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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. Recently, 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.