Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network
In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2012/601287 |
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| author | T. Panigrahi P. M. Pradhan G. Panda B. Mulgrew |
| author_facet | T. Panigrahi P. M. Pradhan G. Panda B. Mulgrew |
| author_sort | T. Panigrahi |
| collection | DOAJ |
| description | In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS) and block incremental least mean square (BILMS) by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms. |
| format | Article |
| id | doaj-art-17ff2074d49a44cf9e5f8bebb15d878a |
| institution | Kabale University |
| issn | 2090-7141 2090-715X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-17ff2074d49a44cf9e5f8bebb15d878a2025-02-03T06:08:47ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2012-01-01201210.1155/2012/601287601287Block Least Mean Squares Algorithm over Distributed Wireless Sensor NetworkT. Panigrahi0P. M. Pradhan1G. Panda2B. Mulgrew3Department of ECE, National Institute of Technology, Rourkela 769008, IndiaSchool of Electrical Sciences, Indian Institute of Technology, Bhubaneswar 713002, IndiaSchool of Electrical Sciences, Indian Institute of Technology, Bhubaneswar 713002, IndiaInstitute for Digital Communication, The University of Edinburgh, Edinburgh EH899AD, UKIn a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS) and block incremental least mean square (BILMS) by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.http://dx.doi.org/10.1155/2012/601287 |
| spellingShingle | T. Panigrahi P. M. Pradhan G. Panda B. Mulgrew Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network Journal of Computer Networks and Communications |
| title | Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network |
| title_full | Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network |
| title_fullStr | Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network |
| title_full_unstemmed | Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network |
| title_short | Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network |
| title_sort | block least mean squares algorithm over distributed wireless sensor network |
| url | http://dx.doi.org/10.1155/2012/601287 |
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