Clustering Based Optimal Data Storage Strategy Using Hybrid Swarm Intelligence in WSN.
Saved in:
| Title: | Clustering Based Optimal Data Storage Strategy Using Hybrid Swarm Intelligence in WSN. |
|---|---|
| Authors: | Mohanasundaram, R.1 mohanasundaramr@vit.ac.in, Periasamy, P.2 psperiasamy.ps@gmail.com |
| Source: | Wireless Personal Communications. Dec2015, Vol. 85 Issue 3, p1381-1397. 17p. |
| Subjects: | Wireless sensor network access control, Back up systems, Data warehousing, Energy consumption, Particle swarm optimization, Heuristic algorithms, Information retrieval, Swarm intelligence |
| Abstract: | In most wireless sensor network applications a large amount of data has been continuously collected for future data query and analysis. Data storage management becomes an important challenge in wireless sensor networks. A popular application of sensor networks is event monitoring. In such applications, the observers may not be interested in the sensors or the raw data from the sensors, but more interested in the events. To address the issues of data storage for event monitoring, an optimal data storage scheme is proposed specifically for Fire detection event. In this work, mainly storage node position problem is considered. Hybrid particle swarm optimization is integrated with FCM clustering to attain the suitable positions for k storage nodes in WSN based on the energy cost of data transmission which in turn assists in the detection of the fire event. To reduce data access energy consumption, FCM clustering based on data storage (CBDS) algorithm has been proposed. CBDS has appropriately adapted to adjust the location of data storage through the process of data storage and query access cost calculations respectively. This paper is focused on monitoring the fire event detection in WSN based on the hybrid PSO with FCM clustering. [ABSTRACT FROM AUTHOR] |
| Copyright of Wireless Personal Communications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
