Optimum positioning of base stations for cellular radio networks
Wireless Networks
Message-optimal connected dominating sets in mobile ad hoc networks
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Approximating minimum size weakly-connected dominating sets for clustering mobile ad hoc networks
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolving neural networks through augmenting topologies
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Differentiated surveillance for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Energy-efficient surveillance system using wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
A Simulated Annealing Algorithm for Energy-Efficient Sensor Network Design
WIOPT '05 Proceedings of the Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Power-aware Node Deployment in Wireless Sensor Networks
SUTC '06 Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC'06) - Volume 01
Stochastic coverage in heterogeneous sensor networks
ACM Transactions on Sensor Networks (TOSN)
Adaptive design optimization of wireless sensor networks using genetic algorithms
Computer Networks: The International Journal of Computer and Telecommunications Networking
Modeling and simulating coverage in sensor networks
Computer Communications
Search-Oriented Deployment Strategies for Wireless Sensor Networks
ISORC '07 Proceedings of the 10th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing
Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks
ITNG '07 Proceedings of the International Conference on Information Technology
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Energy-efficient coverage problems in wireless ad-hoc sensor networks
Computer Communications
Energy-quality tradeoffs for target tracking in wireless sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
The design space of wireless sensor networks
IEEE Wireless Communications
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Communications Magazine
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Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks' compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.