Design Issues of Wireless Sensor Networks in Ubiquitous Learning

Wang, Ming-Fei; Ci, Linlin; Zhan, Ping; Xu, Yongjun

doi:10.1109/icmlc.2007.4370873

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Recent developments in sensor, wireless communication, and embedded computing areas now make it possible to deploy a wireless sensor network composed of a large number of inexpensive microsensor nodes to "achieve quality through quantity" in complex applications [1][2][3]. The nodes are typically with limited processing ability, battery power, and sensing range.…”

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An Energy-Efficient Target Tracking Framework in Wireless Sensor Networks

Wei

Liu

2009

EURASIP J. Adv. Signal Process.

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Recommended by Sudharman JayaweeraThis study devises and evaluates an energy-efficient distributed collaborative signal and information processing framework for acoustic target tracking in wireless sensor networks. The distributed processing algorithm is based on mobile agent computing paradigm and sequential Bayesian estimation. At each time step, the short detection reports of cluster members will be collected by cluster head, and a sensor node with the highest signal-to-noise ratio (SNR) is chosen there as reference node for time difference of arrive (TDOA) calculation. During the mobile agent migration, the target state belief is transmitted among nodes and updated using the TDOA measurement of these fusion nodes one by one. The computing and processing burden is evenly distributed in the sensor network. To decrease the wireless communications, we propose to represent the belief by parameterized methods such as Gaussian approximation or Gaussian mixture model approximation. Furthermore, we present an attraction force function to handle the mobile agent migration planning problem, which is a combination of the node residual energy, useful information, and communication cost. Simulation examples demonstrate the estimation effectiveness and energy efficiency of the proposed distributed collaborative target tracking framework.

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confidence: 99%