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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.
Ratnasamy, S., Karp, B., Shenker, S., Estrin, D., Govindan, R., Yin, L., & Yu, F. (2003). Data-centric storage in sensornets with GHT: A geographic hash table. Mobile Networks and Applications, 8(4), 427–442.
Khan, N., Yaqoob, I., Hashem, I. A. T, Inayat, Z., Mahmoud, A. W. K., Alam, M., & Gani, A. (2014). Big data: Survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014, 1–18.
Guo, L., Li, Y., & Li, J. (2006). WSN01-6: Event query processing based on data-centric storage in wireless sensor networks. In: Global Telecommunications Conference, GLOBECOM’06 (pp. 1–6). IEEE.
Wang, J.-H., Yan, Y., & Madukasi, C. N. (2013). Data storage algorithms based on clustering in wireless sensor networks. Journal of Networks, 8(8), 1796–1802.
Yu, Z., Xiao, B., & Zhou, S. (2010). Achieving optimal data storage position in wireless sensor networks. Computer Communications, 33(1), 92–102.
Silberstein, A., Braynard, R., & Yang, J. (2006). Constraint-chaining: On energy-efficient continuous monitoring in sensor networks. In Proceedings of the 25th ACM SIGMOD International Conference on Management of Data (SIGMOD’06), Chicago, Illinois, USA (pp. 157–168).
Kumar, P., & Chaturvedi, A. (2014). Life time enhancement of wireless sensor network using fuzzy c-means clustering algorithm. In Electronics and Communication Systems (ICECS), 2014 International Conference on (pp. 1–5). IEEE.
Ghosh, S., & Dubey, S. K. (2014). Comparative analysis of k-means and fuzzy c-means algorithms. International Journal of Advanced Computer Science and Applications (IJACSA), 4(4), 35–39.
Edwin, K. P. C., & Stanislaw, H. Z. (2004). An introduction to optimization (2nd ed.). Hoboken: Wiley.
Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd International Conference on System Sciences (HICSS ‘00).
Fu, X., Wang, R., Huang, L., & Wang, Y. (2010). A heuristic algorithm for data storage position in wireless sensor networks. In 12th IEEE International Conference on Communication Technology (ICCT).
About this article
Cite this article
Mohanasundaram, R., Periasamy, P.S. Clustering Based Optimal Data Storage Strategy Using Hybrid Swarm Intelligence in WSN. Wireless Pers Commun 85, 1381–1397 (2015). https://doi.org/10.1007/s11277-015-2846-8
- Heuristic algorithm
- Wireless sensor network
- Particle swarm optimization
- Data storage node
- FCM clustering
- Event monitoring
- Fire event