Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Simulating networks of wireless sensors
Proceedings of the 33nd conference on Winter simulation
Infrastructure tradeoffs for sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Coding Theory Framework for Target Location in Distributed Sensor Networks
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Scheduling Nodes in Wireless Sensor Networks: A Voronoi Approach
LCN '03 Proceedings of the 28th Annual IEEE International Conference on Local Computer Networks
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An optimal node scheduling for flat wireless sensor networks
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
Hybrid multiobjective approach for designing wireless sensor networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
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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.