Self-organizing cooperative sensor network for remote surveillance: current results

Burne, Richard A.; Buczak, Anna L.; Jin, Yaochu; Jamalabad, Vikram R.; Kadar, Ivan; Eadan, Eitan R.

doi:10.1117/12.357139

Cited by 9 publications

(2 citation statements)

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“…Here, we use an approximate approach that contains two steps. First, candidate sensor nodes that can cover the whole sensing field are determined by GA, as proposed in [26]. Second, the useful sensor nodes which can efficiently cover the recovery area are selected.…”

Section: Optimal Strategies For Target Tracking Sensingmentioning

confidence: 99%

Energy-efficient Optimization of Reorganization-Enabled Wireless Sensor Networks

Wang

Ding

et al. 2007

Sensors

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This paper studies the target tracking problem in wireless sensor networks where sensor nodes are deployed randomly. To achieve tracking accuracy constrained by energy consumption, an energy-efficient optimization approach that enables reorganization of wireless sensor networks is proposed. The approach includes three phases which are related to prediction, localization and recovery, respectively. A particle filter algorithm is implemented on the sink node to forecast the future movement of the target in the first prediction phase. Upon the completion of this phase, the most energy efficient sensor nodes are awakened to collaboratively locate the target. Energy efficiency is evaluated by the ratio of mutual information to energy consumption. The recovery phase is needed to improve the robustness of the approach. It is performed when the target is missed because of the incorrect predicted target location. In order to recapture the target by awakening additional sensor nodes as few as possible, a genetic-algorithm-based mechanism is introduced to cover the recovery area. We show that the proposed approach has excellent tracking performance. Moreover, it can efficiently reduce energy consumption, prolong network lifetime and reduce network overheads.

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Section: Optimal Strategies For Target Tracking Sensingmentioning

confidence: 99%

Energy-efficient Optimization of Reorganization-Enabled Wireless Sensor Networks

Wang

Ding

et al. 2007

Sensors

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“…Burne [3] adopted genetic algorithm (GA) to implement the dynamic sensor nodes selection, because GA has outstanding performance in global searching. However, the convergence speed of GA algorithm is slow, so it cannot afford the real-time requirement of dynamic sensor nodes selection.…”

Section: Introductionmentioning

confidence: 99%

Dynamic sensor nodes selection strategy for wireless sensor networks

Wang

2007

2007 International Symposium on Communications and Information Technologies

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Dynamic sensor nodes selection strategy refers to the optimization for achieving the tradeoff between energy consumption and effective coverage rate, enhancing energy efficiency, enlarging the effective coverage rate and prolonging the lifetime of wireless sensor networks (WSN). This paper proposes a dynamic sensor nodes selection strategy, so-called HN-GA, which uses the genetic algorithm (GA) to implement global searching and adopts the Hopfield network (HN) to reduce the search space of GA and ensure the validity of each chromosome. For evaluating the sensor nodes selection results, a combined metric based on several practically feasible measures of the energy consumption and effective coverage rate is introduced. The simulation results verify that the proposed HN-GA algorithm performs well in dynamic sensor nodes selection. With the help of HN-GA based dynamic sensor nodes selection, the lifetime and the effective coverage performance of WSN can be significantly improved. Compared to GA and HN, HN-GA has better performance in regional convergence and global searching, and it can achieve dynamic sensor nodes selection optimization efficiently, robustly and rapidly.I.

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