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Distributed and morphological operation-based data collection algorithm

Author

Listed:
  • Yalin Nie
  • Haijun Wang
  • Yujie Qin
  • Zeyu Sun

Abstract

When monitoring the environment with wireless sensor networks, the data sensed by the nodes within event backbone regions can adequately represent the events. As a result, identifying event backbone regions is a key issue for wireless sensor networks. With this aim, we propose a distributed and morphological operation-based data collection algorithm. Inspired by the use of morphological erosion and dilation on binary images, the proposed distributed and morphological operation-based data collection algorithm calculates the structuring neighbors of each node based on the structuring element, and it produces an event-monitoring map of structuring neighbors with less cost and then determines whether to erode or not. The remaining nodes that are not eroded become the event backbone nodes and send their sensing data. Moreover, according to the event backbone regions, the sink can approximately recover the complete event regions by the dilation operation. The algorithm analysis and experimental results show that the proposed algorithm can lead to lower overhead, decrease the amount of transmitted data, prolong the network lifetime, and rapidly recover event regions.

Suggested Citation

  • Yalin Nie & Haijun Wang & Yujie Qin & Zeyu Sun, 2017. "Distributed and morphological operation-based data collection algorithm," International Journal of Distributed Sensor Networks, , vol. 13(7), pages 15501477177, July.
    • Handle: RePEc:sae:intdis:v:13:y:2017:i:7:p:1550147717717593
    DOI: 10.1177/1550147717717593
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    References listed on IDEAS

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    1. Yong Wang & Dianhong Wang, 2013. "Energy-Efficient Node Selection for Target Tracking in Wireless Sensor Networks," International Journal of Distributed Sensor Networks, , vol. 9(1), pages 830950-8309, January.
    2. Hongju Cheng & Zhihuang Su & Daqiang Zhang & Jaime Lloret & Zhiyong Yu, 2014. "Energy-Efficient Node Selection Algorithms with Correlation Optimization in Wireless Sensor Networks," International Journal of Distributed Sensor Networks, , vol. 10(3), pages 576573-5765, March.
    3. Sunyong Kim & Chiwoo Cho & Kyung-Joon Park & Hyuk Lim, 2017. "Increasing network lifetime using data compression in wireless sensor networks with energy harvesting," International Journal of Distributed Sensor Networks, , vol. 13(1), pages 15501477166, January.
    4. Xiaoxiao Jiang & Wei He & Xuguang Yang & Yingguan Wang, 2014. "An Energy-Efficient Node Selection Algorithm in Bearings-Only Target Tracking Sensor Networks," International Journal of Distributed Sensor Networks, , vol. 10(2), pages 483279-4832, February.
    5. Aziz Nasridinov & Sun-Young Ihm & Young-Ho Park, 2013. "Skyline-Based Aggregator Node Selection in Wireless Sensor Networks," International Journal of Distributed Sensor Networks, , vol. 9(9), pages 356194-3561, September.
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