The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks
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 technique...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717727713 |
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| _version_ | 1849407191975460864 |
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| author | Xiangmao Chang Quan Wang Zhiguo Qu Yanchao Zhao |
| author_facet | Xiangmao Chang Quan Wang Zhiguo Qu Yanchao Zhao |
| author_sort | Xiangmao Chang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-45dddd4f8f054b7d940ccf684c5763d7 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-45dddd4f8f054b7d940ccf684c5763d72025-08-20T03:36:10ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-08-011310.1177/1550147717727713The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networksXiangmao Chang0Quan Wang1Zhiguo Qu2Yanchao Zhao3College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaJiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaThe 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.https://doi.org/10.1177/1550147717727713 |
| spellingShingle | Xiangmao Chang Quan Wang Zhiguo Qu Yanchao Zhao The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks International Journal of Distributed Sensor Networks |
| title | The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks |
| title_full | The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks |
| title_fullStr | The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks |
| title_full_unstemmed | The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks |
| title_short | The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks |
| title_sort | integration of compressive sensing and clustering for date gathering in unmanned aircraft system aided networks |
| url | https://doi.org/10.1177/1550147717727713 |
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