Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACCESS.2017.2681207 | IEEE ACCESS |
Keywords | Field | DocType |
Periodic sensor network (PSN),data aggregation,clustering topology,big-data sensing,similarity and distance functions,Anova model | Sensor node,Key distribution in wireless sensor networks,Computer science,Computer network,Network topology,Mobile wireless sensor network,Cluster analysis,Data aggregator,Wireless sensor network,Big data,Distributed computing | Journal |
Volume | ISSN | Citations |
5 | 2169-3536 | 5 |
PageRank | References | Authors |
0.44 | 24 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hassan Harb | 1 | 37 | 6.88 |
| Abdallah Makhoul | 2 | 299 | 36.48 |
| Samar Tawbi | 3 | 18 | 4.33 |
| Raphaël Couturier | 4 | 307 | 51.68 |