Elements of information theory
Elements of information theory
Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Building efficient wireless sensor networks with low-level naming
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Wake on wireless: an event driven energy saving strategy for battery operated devices
Proceedings of the 8th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Low-Power Wireless Sensor Networks
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
The number of neighbors needed for connectivity of wireless networks
Wireless Networks
Prediction-based monitoring in sensor networks: taking lessons from MPEG
ACM SIGCOMM Computer Communication Review - Special issue on wireless extensions to the internet
Computer Networks: The International Journal of Computer and Telecommunications Networking
Convergence analysis of genetic algorithms for topology control in MANETs
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
A survey of communication/networking in Smart Grids
Future Generation Computer Systems
Transactions on Computational Science XV
Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms
International Journal of Intelligent Information Technologies
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In this paper we propose a reduced-complexity Genetic Algorithm (GA) for optimisation of multihop sensor networks. The goal of the system is to generate optimal number of sensor clusters with Cluster-Heads (CHs). It results in minimisation of the power consumption of the sensor system while maximising the sensor objectives (coverage and exposure). The GA is used to adaptively create various components such as cluster-members, CHs and next-cluster. These components are then used to evaluate the average fitness of the system based on the sequence of communication links towards the sink. In addition, the mechanism supports dynamically changing coverage, task requirements, failures, incremental redeployment and reconfiguration.