Nowadays sensor plays an important role in the day today life. People uses wireless technology along with sensor for monitoring home held devices, security alerts, natural disasters alert, building supervision, industrial quality management, etc. Wireless Sensor Network (WSN) consists of thousands of economical and feasible disposable sensors, deployed in the environment to sense parameters related to the surroundings such as temperature, moisture level, pressure etc., Number of sensor nodes are connected in these networks for communication. Each nodes are self-organized, having the capacity of sense, process, and aggregate data. Energy utilization in WSN is major issue in networks for improving network lifetime. Conventional clustering schemes are created with static cluster heads that die past than the normal nodes that degrade the network performance in routing. It is very vital area to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network life time. In this paper, a Energy Aware Clustering using Neuro-fuzzy approach (EACNF) is proposed to form finest and energy aware clusters. The proposed scheme consists of fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. EACNF used neural network that provide effective training set related to energy and density of all nodes to estimate the expected energy for Uncertain cluster heads. Sensor nodes with higher energy are trained with various location of base station to select energy aware cluster heads. Fuzzy if–then mapping rule is used in fuzzy logic part that inputs to form clusters and cluster heads. EACNF is designed for WSN that handling Trust factor for security to the network. EACNF used three metric such as transmission range, residual energy and Trust factor for improving network life time. The proposed scheme EACNF is compared with related clustering schemes namely Cluster-Head Election Mechanism using Fuzzy Logic and Energy-Aware Fuzzy Unequal Clustering. The experiment results show that EACNF performs better than the other related schemes.
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Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (HICSS-33) (pp. 3005–3014).
Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.
Harold Robinson, Y., Balaji, S., & Rajaram, M. (2016). ECBK: Enhanced cluster based key management scheme for achieving quality of service. Circuits and Systems, 7(8), 2014–2024.
Harold Robinson, Y., & Rajaram, M. (2016). A memory aided broadcast mechanism with fuzzy classification on a device-to-device mobile Ad Hoc network. Wireless Personal Communications. 1–23. doi:10.1007/s11277-016-3213-0.
Fan, C. S. (2013). Rich: Region-based intelligent cluster-head selection and node deployment strategy in concentric-based WSNs. Advances in Electrical and Ana Maria Popescu Computer Engineering, 13(4), 3–8. doi:10.4316/AECE.2013.04001.
Schalkoff, R. J. (1997). Artificial neural network. New York, NY: McGraw-Hill.
Kim, J. M., Park, S., Han, Y., & Chung, T. (2008). CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In Proceeding of the 10th international conference on advanced communication technology (ICACT) (pp. 654–659).
Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power–efficient gathering using in sensor information systems. In Proceeding of IEEE aerospace conference (pp. 1125–1130).
Kulkarni, R., Forster, V., & Venayagamoorthy, G. K. (2011). Computational intelligence in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 13(1), 68–96.
Harold Robinson, Y., & Rajaram, M. (2015). Energy-aware multipath routing scheme based on particle swarm optimization in mobile ad hoc networks. The Scientific World Journal. 1–9. doi:10.1155/2015/284276.
Manjeshwar, A. & Agarwal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In 1st International workshop on parallel and distributed computing issues in wireless networks and mobile computing.
Golden Julie, E., & Tamil Selvi, S. (2016). Development of energy efficient clustering protocol in wireless sensor network using neuro-fuzzy approach. The Scientific World Journal 2016, Article ID 5063261, 1–8.
Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13, 1741–1749.
Ayyasamy, A., & Venkatachalapathy, K. (2015). Context aware adaptive fuzzy based QoS routing scheme for streaming services over MANETs. Wireless Networks, 21(2), 421–430.
Ayyasamy, A., & Venkatachalapathy, K. (2014). Context aware adaptive fuzzy based Quality of service over MANETs. International Review on Computers and Software, 9(7), 1220–1226.
Xuxun, L. (2012). A survey on clustering routing protocols in wireless sensor networks. Sensors Journal, 12(8), 11113–11153.
Balaji, S., Harold Robinson, Y., & Rajaram, M. (2016). SCSBE: Secured cluster and sleep based energy-efficient sensory data collection with mobile sinks. Circuits and Systems, 7, 1992–2001.
Yong, Z., & Pei, Q. (2012). A energy efficient clustering routing algorithm based on distance and residual energy for wireless sensor networks. Procedia Engineering, 29, 1882–1888.
Nekooei, S. M., & Manzuri-Shalmani, M. T. (2011). Location finding in wireless sensor network based on soft computing methods, control. In 2011 International conference on control, automation and systems engineering (CASE) (pp. 1–5).
Veena, K. N., & Kumar, B. P. V. (2010). Dynamic clustering for wireless sensor networks: A neuro-fuzzy technique approach. In IEEE international conference on computational intelligence and computing research (ICCIC) (pp. 1–6).
Wu, Y., & Li, Y. (2008). Construction algorithms for k-connected m-dominating sets in wireless sensor networks. In Proceedings of 9th ACM international annual symposium on theory of computing (pp. 83–90).
Golden Julie, E., Tamilselvi, S., & Harold Robinson, Y. (2016). Performance analysis of energy efficient virtual back bone path based cluster routing protocol for WSN. Wireless Personal Communications. 1–21. doi:10.1007/s11277-016-3520-5.
Dang, G., & Cheng, X. (2014). Application of wireless sensor network in monitoring system based on Zigbee. In IEEE workshop on advanced research and technology in industry applications (WARTIA) (pp. 181–183).
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Harold Robinson, Y., Golden Julie, E., Balaji, S. et al. Energy Aware Clustering Scheme in Wireless Sensor Network Using Neuro-Fuzzy Approach. Wireless Pers Commun 95, 703–721 (2017). https://doi.org/10.1007/s11277-016-3793-8
- Fuzzy logic
- Neural network
- Energy efficiency
- Wireless sensor network