Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Highly-resilient, energy-efficient multipath routing in wireless sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review
ESRT: event-to-sink reliable transport in wireless sensor networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Simulating the power consumption of large-scale sensor network applications
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Energy efficient strategies for deployment of a two-level wireless sensor network
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Information processing for live photo mosaic with a group of wireless image sensors
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
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In this paper, a Wireless Sensor Network (WSN) deployment problem is posed: in a two-level heterogeneous WSN, we need to optimally determine the location of cluster-heads in order to minimize communication power. We require that each sensor node connects to at least p cluster-heads for reliability, and each cluster-head can accept at most q connections. The optimization problem in formulated as a Mixed Integer Nonlinear Programming (MINLP) problem. To overcome the fact that a MINLP solver fails to solve large-scale cases or obtain a global optimum, we propose an iterative decomposition algorithm and use a randomized multi-start technique for global optimization. We also propose an incremental deployment approach and use it to solve the original problem as if the WSN is built incrementally. Numerical results show that the decomposition algorithm is very efficient. While the incremental deployment method is slower in each run, it produces a better solution distribution compared to the pure multi-start approach. Both, however, are capable of solving large-scale problems.