Author
Listed:
- Xiangmao Chang
- Quan Wang
- Zhiguo Qu
- Yanchao Zhao
Abstract
The development of the unmanned aircraft systems is envisioned to greatly reduce the energy consumption of sensor nodes in data gathering process using unmanned aircraft systems as mobile sinks. In traditional sensor networks, compressive sensing and clustering are two key energy-efficient techniques for data gathering. However, how to integrate two techniques into the data gathering for unmanned aircraft system–aided wireless sensor networks effectively is still an open problem. Moreover, most clustering schemes focus on the cluster head selection strategy and simplified the problem of cluster member selection, and most compressive sensing schemes are not integrated with the clustering strategy. To this end, this article studies the problem of integrating compressive sensing with clustering for data gathering in unmanned aircraft system–aided networks. We first give a theoretical formulation of this problem. Considering the non-deterministic polynomial-time hard complexity of the problem, we present two algorithms by jointly considering the compressive ratio variation factor and the distance factor to find near-optimal solutions heuristically. Evaluations based on real data traces show that the proposed algorithms greatly reduced the energy consumption of sensor nodes efficiency.
Suggested Citation
Xiangmao Chang & Quan Wang & Zhiguo Qu & Yanchao Zhao, 2017.
"The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks,"
International Journal of Distributed Sensor Networks, , vol. 13(8), pages 15501477177, August.
Handle:
RePEc:sae:intdis:v:13:y:2017:i:8:p:1550147717727713
DOI: 10.1177/1550147717727713
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