The week's pick
Reseach Article
Data Fusion in Wireless Sensor Networks using Fuzzy Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 125 - Number 12 |
| Year of Publication: 2015 |
| Authors: Awat Mandeh, Keyhan Khamforoosh, Vafa Maihami |
10.5120/ijca2015906151
|
Awat Mandeh, Keyhan Khamforoosh, Vafa Maihami . Data Fusion in Wireless Sensor Networks using Fuzzy Systems. International Journal of Computer Applications. 125, 12 ( September 2015), 31-36. DOI=10.5120/ijca2015906151
Abstract
Since battery is the source of energy for sensors, one of the important issues in wireless sensor networks is the energy and network lifetime. A method to reduce energy consumption and, as a result, increase the network lifetime is the fusion of data collected from the sensors in the covered environment before transmission to wireless sensor network. Data fusion in sensors is defined as the process in which the data received from multiple sources are integrated in order to achieve better perceived information with respect to only one source. In this paper, a new method is proposed for data fusion in network sensors using fuzzy systems. In the proposed method, by integrating the input data into each sensor, each of which had three inputs, the similarity percent of the data in sensors was obtained in order to identify the size of data (packets) to be sent. Simulation results on the proposed method verified the efficiency of the proposed method in terms of energy consumption in the network.
References
- Akyildiz, I. F. (2010). Wireless sensor networks. John Wiley & Sons
- Abdelgawad. A, B. M. (2012). Resource-Aware data fusion algorithms for wireless sensor networks. Springer Science & Business Media.
- Allahverdi, N. (2002). An application of artificial intelligence. Expert systems.
- Sargolzaei. Javad, K. M. (2008). Fuzzy inference system to modeling of crossflow milk ultrafiltration. Applied Soft Computing, 8(1), 456-465.
- Brooks. Richard R, I. S. (1998). Multi-sensor fusion: fundamentals and applications with software. Prentice-Hall, Inc.
- E.F. Nakamura, A. A. (2007). Information Fusion for Wireless Sensor Networks. ACM Computing Surveys, 39.
- Yager. R R, Z. L. (2012). An introduction to fuzzy logic applications in intelligent systems . Springer Science & Business Media.
- Boulis. Athanassios, G. S. (2003). Aggregation in sensor networks: an energy–accuracy trade-off. Ad hoc networks, 1(2), 317-331.
- Boyd. Stephen, G. A. (2005). Gossip algorithms: Design, analysis and applications. INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE. 3, pp. 1653-1664. IEEE.
- Chen. Jen-Yeu, ,. P. (2006). Robust computation of aggregates in wireless sensor networks: distributed randomized algorithms and analysis. Parallel and Distributed Systems, IEEE Transactions on, 17(9), 987-1000.
- Polastre. Joseph, H. J. (2004). Versatile low power media access for wireless sensor networks. Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 95-107). ACM.
- Huang. Pei, X. L. (2013). The evolution of MAC protocols in wireless sensor networks: A survey. Communications Surveys & Tutorials, IEEE, 15(1), 101-120.
- Kim. Jong-Myoung, P. S.-H.-J.-M. (2008). CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. Advanced communication technology, 2008. ICACT 2008. 10th international conference on. 1, pp. 654-659. IEEE.
- Fathi, M., Maihami, V., & Moradi, P. (2013). Reinforcement Learning for Multiple Access Control in Wireless Sensor Networks: Review, Model, and Open Issues. Wireless personal communications, 72(1), 535-547.
- Fathi, M.; Maihami, V. (2015), "Operational State Scheduling of Relay Nodes in Two-Tiered Wireless Sensor Networks," in Systems Journal, IEEE , vol.9, no.3,pp.686-693.
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