Novel data preprocessing algorithm for WSN lifetime maximization in water pipeline monitoring system
Institute of Electrical and Electronics Engineers Inc.
Wireless Sensor Networks (WSN) are widely deployed to maintain Structural Health Monitoring of Water Pipeline System (WPS). Accordingly, it is imperatively important to ensure reliable communication between sensor nodes deployed in harsh environment to allow a continuous data collection and processing. In this context, we propose and implement an energy efficient solution that enables a seamless interconnection between sensor nodes, and trusty data transmission in order to maximize the network lifetime. After a clustering step, a Data Redundancy Elimination technique is applied to remove redundant data at each cluster head. This operation is followed by a data fusion algorithm based on Dempster-Shafer evidence theory at the Base Station. This scheme is proposed with aim of reducing the size of data carried by the network and consequently save on energy consumption. This results in improved WSN lifetime and more accurate WPS systems. © 2019 IEEE.
This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2019 IEEE Wireless Communications and Networking Conference (WCNC) (2019), available online at: https://doi.org/10.1109/WCNC.2019.8885808.
Data aggregation, Data fusion, Dempster-Shafer evidence theory, Industrial wireless sensor network, Network lifetime, Water Pipeline monitoring, Data handling, Energy efficiency, Energy utilization, Pipelines, Sensor data fusion, Sensor nodes, Structural health monitoring (SHM), Water pipelines, Dempster Shafer evidence theory, Industrial wireless sensor networks, Network lifetime, Pipeline monitoring, Monitoring
Abdelhafidh, M., Fourati, M., Fourati, L. C., Ben Mnaouer, A., & Zid, M. (2019). Novel data preprocessing algorithm for WSN lifetime maximization in water pipeline monitoring system. In IEEE Wireless Communications and Networking Conference, WCNC (Vol. 2019–April). https://doi.org/10.1109/WCNC.2019.8885808