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Chen W, Mei T, Meng MQH, Liang H, Liu Y, Li Y, Li S. Localization Algorithm Based on a Spring Model (LASM) for Large Scale Wireless Sensor Networks. Sensors (Basel) 2008; 8:1797-1818. [PMID: 27879793 PMCID: PMC3663024 DOI: 10.3390/s8031797] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2007] [Accepted: 03/12/2008] [Indexed: 11/16/2022]
Abstract
A navigation method for a lunar rover based on large scale wireless sensor networks is proposed. To obtain high navigation accuracy and large exploration area, high node localization accuracy and large network scale are required. However, the computational and communication complexity and time consumption are greatly increased with the increase of the network scales. A localization algorithm based on a spring model (LASM) method is proposed to reduce the computational complexity, while maintaining the localization accuracy in large scale sensor networks. The algorithm simulates the dynamics of physical spring system to estimate the positions of nodes. The sensor nodes are set as particles with masses and connected with neighbor nodes by virtual springs. The virtual springs will force the particles move to the original positions, the node positions correspondingly, from the randomly set positions. Therefore, a blind node position can be determined from the LASM algorithm by calculating the related forces with the neighbor nodes. The computational and communication complexity are O(1) for each node, since the number of the neighbor nodes does not increase proportionally with the network scale size. Three patches are proposed to avoid local optimization, kick out bad nodes and deal with node variation. Simulation results show that the computational and communication complexity are almost constant despite of the increase of the network scale size. The time consumption has also been proven to remain almost constant since the calculation steps are almost unrelated with the network scale size.
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Affiliation(s)
- Wanming Chen
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, P. R. China; E-mail:
- Department of Automation, University of Science and Technology of China, Hefei, 230027, P.R. China
| | - Tao Mei
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, P. R. China; E-mail:
- Author to whom correspondence should be addressed; E-mail:
| | - Max Q.-H. Meng
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, P. R. China; E-mail:
- Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tian, Hong Kong; E-mail:
| | - Huawei Liang
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, P. R. China; E-mail:
| | - Yumei Liu
- Department of Automation, University of Science and Technology of China, Hefei, 230027, P.R. China
| | - Yangming Li
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, P. R. China; E-mail:
- Department of Automation, University of Science and Technology of China, Hefei, 230027, P.R. China
| | - Shuai Li
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, P. R. China; E-mail:
- Department of Automation, University of Science and Technology of China, Hefei, 230027, P.R. China
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