Comparison of Different Data Aggregation Techniques in Distributed Sensor Networks

• Published in: IEEE access Vol. 5; pp. 4250 - 4263
• Main Authors: Harb, Hassan, Makhoul, Abdallah, Tawbi, Samar, Couturier, Raphael
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Anova model; big-data sensing; Clustering; clustering topology; Computer Science; Cryptography and Security; Data aggregation; Data management; Distributed sensor systems; Distributed, Parallel, and Cluster Computing; Emerging Technologies; Energy consumption; Modeling and Simulation; Multiagent Systems; Network latency; Network topology; Periodic sensor network (PSN); Redundancy; Sensors; Silicon; similarity and distance functions; Software Engineering; Statistical tests; Temperature measurement; Topology; Ubiquitous Computing; Wireless networks; Wireless sensor networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2681207

Wireless sensor networks (WSNs) are almost everywhere, they are exploited for thousands of applications in a densely distributed manner. Such deployment makes WSNs one of the highly anticipated key contributors of the big data nowadays. Hence, data aggregation is attracting much attention from researchers as efficient way to reduce the huge volume of data generated in WSNs by eliminating the redundancy among sensing data. In this paper, we propose an efficient data aggregation technique for clustering-based periodic wireless sensor networks. Further to a local aggregation at sensor node level, our technique allows cluster-head to eliminate redundant data sets generated by neighbouring nodes by applying three data aggregation methods. These proposed methods are based on the sets similarity functions, the one-way Anova model with statistical tests and the distance functions, respectively. Based on real sensor data, we have analyed their performances according to the energy consumption and the data latency and accuracy, and we show how these methods can significantly improve the performance of sensor networks.