Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern deal...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/597486 |
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| author | Ranganathan Mohanasundaram Pappampalayam Sanmugam Periasamy |
| author_facet | Ranganathan Mohanasundaram Pappampalayam Sanmugam Periasamy |
| author_sort | Ranganathan Mohanasundaram |
| collection | DOAJ |
| description | The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. |
| format | Article |
| id | doaj-art-e895339e465a4af9825ee723612f9e28 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e895339e465a4af9825ee723612f9e282025-02-03T07:23:52ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/597486597486Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor NetworksRanganathan Mohanasundaram0Pappampalayam Sanmugam Periasamy1School of Computing Science and Engineering, VIT University, Vellore 632014, IndiaDepartment of ECE, K.S.R. College of Engineering, Tiruchengode 637215, IndiaThe current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.http://dx.doi.org/10.1155/2015/597486 |
| spellingShingle | Ranganathan Mohanasundaram Pappampalayam Sanmugam Periasamy Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks The Scientific World Journal |
| title | Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks |
| title_full | Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks |
| title_fullStr | Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks |
| title_full_unstemmed | Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks |
| title_short | Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks |
| title_sort | hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks |
| url | http://dx.doi.org/10.1155/2015/597486 |
| work_keys_str_mv | AT ranganathanmohanasundaram hybridswarmintelligenceoptimizationapproachforoptimaldatastoragepositionidentificationinwirelesssensornetworks AT pappampalayamsanmugamperiasamy hybridswarmintelligenceoptimizationapproachforoptimaldatastoragepositionidentificationinwirelesssensornetworks |