Power minimization in IC design: principles and applications
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Topology management for sensor networks: exploiting latency and density
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Optimization of Task Allocation in a Cluster-Based Sensor Network
ISCC '03 Proceedings of the Eighth IEEE International Symposium on Computers and Communications
Smart Fabric, or "Wearable Clothing"
ISWC '97 Proceedings of the 1st IEEE International Symposium on Wearable Computers
An improved hybrid genetic algorithm for the generalized assignment problem
Proceedings of the 2004 ACM symposium on Applied computing
Decentralized, adaptive resource allocation for sensor networks
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Markov chain models of parallel genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Communications Magazine
Distributed genetic evolution in WSN
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks
Journal of Network and Computer Applications
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In this work we consider lifetime-aware resource management for sensor network using distributed genetic algorithm (GA). Our goal is to allocate different detection methods to different sensor nodes in the way such that the required detection probability can be achieved while the network lifetime is maximized. The contribution of this paper is twofold. Firstly, the resource management problem is formulated as a constraint optimization problem and is solved using a distributed GA. Secondly, empirical analysis results are provided that reveals the relationship between the configuration parameters and the quality of the search. A regression model is designed to estimate the runtime of the distributed GA given the configuration parameters. The model is utilized to find energy efficient configurations of the algorithm.