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Snapshot and Continuous Data Collection in Probabilistic Wireless Sensor Networks
March 2014 (vol. 13 no. 3)
pp. 626-637
Shouling Ji, Georgia State University, Atlanta and Georgia Institute of Technology, Atlanta
Raheem Beyah, Georgia Institute of Technology, Atlanta
Zhipeng Cai, Georgia State University, Atlanta
Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the performance can be measured by its achievable network capacity. Most existing works studying the network capacity issue are based on the unpractical model called deterministic network model. In this paper, a more reasonable model, probabilistic network model, is considered. For snapshot data collection, we propose a novel Cell-based Path Scheduling (CPS) algorithm that achieves capacity of $(\Omega ({1/ 5\omega \ln n} \cdot W))$ in the sense of the worst case and order-optimal capacity in the sense of expectation, where $(n)$ is the number of sensor nodes, $(\omega)$ is a constant, and $(W)$ is the data transmitting rate. For continuous data collection, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the continuous data collection process by forming a data transmission pipeline, and achieves a capacity gain of $(N \sqrt{n}/ \sqrt{\log n} \ln n)$ or $(n/ \log n \ln n)$ times better than the optimal capacity of the snapshot data collection scenario in order in the sense of the worst case, where $(N)$ is the number of snapshots in a continuous data collection task. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works.
Index Terms:
Data collection,Wireless sensor networks,Probabilistic logic,Data communication,Scheduling,Interference,Data models,lossy links,Probabilistic wireless sensor networks,data collection,probabilistic network model
Citation:
Shouling Ji, Raheem Beyah, Zhipeng Cai, "Snapshot and Continuous Data Collection in Probabilistic Wireless Sensor Networks," IEEE Transactions on Mobile Computing, vol. 13, no. 3, pp. 626-637, March 2014, doi:10.1109/TMC.2013.30
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