Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks
Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and parti...
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| Materyal Türü: | Makale |
| Dil: | English |
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MDPI AG
2020-10-01
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| Seri Bilgileri: | Sensors |
| Konular: | |
| Online Erişim: | https://www.mdpi.com/1424-8220/20/20/5961 |
| _version_ | 1827703753474572288 |
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| author | Xuechen Chen Wenjun Xiong Sheng Chu |
| author_facet | Xuechen Chen Wenjun Xiong Sheng Chu |
| author_sort | Xuechen Chen |
| collection | DOAJ |
| description | Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute <i>i</i>-th effective routing path decide which positions in the <i>i</i>-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches. |
| first_indexed | 2024-03-10T15:26:18Z |
| format | Article |
| id | doaj.art-24636fc91044471a8a5584dd12e328a1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:26:18Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-24636fc91044471a8a5584dd12e328a12023-11-20T18:00:51ZengMDPI AGSensors1424-82202020-10-012020596110.3390/s20205961Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor NetworksXuechen Chen0Wenjun Xiong1Sheng Chu2School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510275, ChinaSchool of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510275, ChinaUnderwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute <i>i</i>-th effective routing path decide which positions in the <i>i</i>-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches.https://www.mdpi.com/1424-8220/20/20/5961Bayesian compressive sensingparticle swarm optimizationthree dimensional underwater wireless sensor networkBayesian Crame´r-Rao Bound |
| spellingShingle | Xuechen Chen Wenjun Xiong Sheng Chu Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks Sensors Bayesian compressive sensing particle swarm optimization three dimensional underwater wireless sensor network Bayesian Crame´r-Rao Bound |
| title | Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks |
| title_full | Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks |
| title_fullStr | Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks |
| title_full_unstemmed | Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks |
| title_short | Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks |
| title_sort | two tier pso based data routing employing bayesian compressive sensing in underwater sensor networks |
| topic | Bayesian compressive sensing particle swarm optimization three dimensional underwater wireless sensor network Bayesian Crame´r-Rao Bound |
| url | https://www.mdpi.com/1424-8220/20/20/5961 |
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