Abstract
Illegal nodes can send malicious data to WSN continuously, which will accelerate the energy consumption rate. Even if the cluster heads in the inner regions have been exhausted simultaneously, the ones in the outer regions may still hold sufficient energy resource. This situation is defined as energy-hole problem in WSN. To investigate the principle of such problem and design corresponding defense strategies, it is critical to analyze the configuration and distribution of cluster heads. According to the balance conditions of energy consumption, a novel mathematical model is formulated to accurately estimate the communication radius of cluster heads in network deployment. Then mobile sinks are introduced to gather the data transmitted by malicious nodes in circular regions. Considering the uniformity principle in collecting data and shortest path routing, the migration path of mobile sinks is calculated. An energy-hole alleviating algorithm is ultimately proposed based on the energy analysis in WSN. Finally, experimental results validate the efficiency and effectiveness of the proposed algorithm in energy-hole detection and mitigation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nayak, P., Devulapalli, A.: A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors J. 16(1), 137–144 (2016)
Sun, J., Zou, J., Huang, L.: Distributed optimization of lifetime and throughput with power consumption balance opportunistic routing in dynamic wireless sensor networks. Int. J. Distrib. Sensor Netw. 12(10), 1–15 (2016)
Yu, X., Chang, X., Zhong, S., et al.: An efficient energy hole alleviating algorithm for wireless sensor networks. IEEE Trans. Consum. Electron. 60(3), 347–355 (2014)
Zhang, D., Li, G., Zheng, K., et al.: An energy-balanced routing method based on forward-aware factor for wireless sensor networks. IEEE Trans. Industr. Inform. 10(1), 766–773 (2014)
Pak, J.M., Ahn, C.K., Shi, P., et al.: Distributed hybrid particle/FIR filtering for mitigating NLOS effects in TOA-based localization using wireless sensor networks. IEEE Trans. Industr. Electron. 64(6), 5182–5191 (2017)
Liu, Y., Dong, M., Ota, K., et al.: ActiveTrust: secure and trustable routing in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 11(9), 2013–2027 (2016)
Dong, M., Ota, K., Liu, A.: RMER: reliable and energy-efficient data collection for large-scale wireless sensor networks. IEEE Internet Things J. 3(4), 511–519 (2016)
Han, G., Liu, L., Jiang, J., et al.: Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Trans. Industr. Inform. 13(1), 135–143 (2017)
Kurt, S., Yildiz, H.U., Yigit, M., et al.: Packet size optimization in wireless sensor networks for smart grid applications. IEEE Trans. Industr. Electron. 64(3), 2392–2401 (2017)
Kuo, T.W., Lin, K.C., Tsai, M.J.: On the construction of data aggregation tree with minimum energy cost in wireless sensor networks: NP-completeness and approximation algorithms. IEEE Trans. Comput. 65(10), 3109–3121 (2016)
Zhang, H., Xing, H., Cheng, J., et al.: Secure resource allocation for OFDMA two-way relay wireless sensor networks without and with cooperative jamming. IEEE Trans. Industr. Inform. 12(5), 1714–1725 (2016)
Gu, Y., Ren, F., Ji, Y., et al.: The evolution of sink mobility management in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 18(1), 507–524 (2016)
Villas, L.A., Boukerche, A., Ramos, H.S., et al.: DRINA: a lightweight and reliable routing approach for in-network aggregation in wireless sensor networks. IEEE Trans. Comput. 62(4), 676–689 (2013)
Ren, J., Zhang, Y., Zhang, K., et al.: Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Trans. Industr. Inform. 12(2), 788–800 (2016)
Keskin, M.E., Altınel, İ.K., Aras, N., et al.: Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility. Ad Hoc Netw. 17, 18–36 (2014)
Farash, M.S., Turkanović, M., Kumari, S., et al.: An efficient user authentication and key agreement scheme for heterogeneous wireless sensor network tailored for the Internet of Things environment. Ad Hoc Netw. 36, 152–176 (2016)
Salarian, H., Chin, K.W., Naghdy, F.: An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Trans. Veh. Technol. 63(5), 2407–2419 (2014)
Abd, M.A., Al-Rubeaai, S.F., Singh, B.K., et al.: Extending wireless sensor network lifetime with global energy balance. IEEE Sensors J. 15(9), 5053–5063 (2015)
Cheng, H., Su, Z., Xiong, N., et al.: Energy-efficient node scheduling algorithms for wireless sensor networks using Markov random field model. Inf. Sci. 329, 461–477 (2016)
Liu, X.: A novel transmission range adjustment strategy for energy hole avoiding in wireless sensor networks. J. Netw. Comput. Appl. 67, 43–52 (2016)
Lin, H., Uster, H.: Exact and heuristic algorithms for data-gathering cluster-based wireless sensor network design problem. IEEE/ACM Trans. Netw. 22(3), 903–916 (2014)
Han, Z., Wu, J., Zhang, J., et al.: A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Trans. Nuclear Sci. 61(2), 732–740 (2014)
Yao, Y., Cao, Q., Vasilakos, A.V.: EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Trans. Netw. (TON) 23(3), 810–823 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, W., Yu, B. (2018). An Efficient Energy-Hole Alleviating Algorithm for Wireless Sensor Network Based on Energy-Balanced Clustering Protocol. In: Li, J., et al. Wireless Sensor Networks. CWSN 2017. Communications in Computer and Information Science, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-10-8123-1_10
Download citation
DOI: https://doi.org/10.1007/978-981-10-8123-1_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8122-4
Online ISBN: 978-981-10-8123-1
eBook Packages: Computer ScienceComputer Science (R0)