Simulated annealing: theory and applications
Simulated annealing: theory and applications
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MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
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New methods to color the vertices of a graph
Communications of the ACM
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Buffer minimization using max-coloring
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Automatic storage optimization
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Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Target tracking by time difference of arrival using recursive smoothing
Signal Processing
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ICPADS '05 Proceedings of the 11th International Conference on Parallel and Distributed Systems - Workshops - Volume 02
Adaptive Sleep Scheduling for Energy-efficient Movement-predicted Wireless Communication
ICNP '05 Proceedings of the 13TH IEEE International Conference on Network Protocols
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Exploiting geometry for improved hybrid AOA/TDOA-based localization
Signal Processing
Distributed and energy-efficient target localization and tracking in wireless sensor networks
Computer Communications
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UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
The capacity of wireless networks
IEEE Transactions on Information Theory
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This paper considers a location system where a number of deployed sensor nodes collaborate with objects that need to be localized. Unlike existing works, we focus on reducing the energy consumption of the sensor nodes, which are assumed to be static and run on limited battery power. To minimize the total wake-up time of the sensor nodes, we control the transmission schedule of each object. Because it is difficult to find an optimal solution to the considered optimization problem, we consider an approach to this problem that consists of two steps: (1) create an equivalent modified graph coloring subproblem, and (2) permute the coloring result to obtain a best possible solution. We adopt some existing graph coloring algorithms for step 1 and find two properties of optimal schedules that can be used to confine the search space for step 2. Additionally, we propose a heuristic algorithm that aims at significantly reducing the complexity for the case where the confined search space is still too large. The performance of our heuristic algorithm is evaluated through extensive simulations. It is shown that its performance is comparable to that of the simulated annealing algorithm, which gives a near-optimal solution.