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Telecommunication Systems
Erschienen in: Telecommunication Systems 1/2018

03.05.2017

An energy optimization in wireless sensor networks by using genetic algorithm

verfasst von: Sunil Kr. Jha, Egbe Michael Eyong

Erschienen in: Telecommunication Systems | Ausgabe 1/2018

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Abstract

Wireless sensor networks (WSNs) are used for several commercial and military applications, by collecting, processing and distributing a wide range of data. Maximizing the battery life of WSNs is crucial in improving the performance of WSN. In the present study, different variations of genetic algorithm (GA) method have been implemented independently on energy models for data communication of WSNs with the objective to find out the optimal energy \(\hbox {(E)}\) consumption conditions. Each of the GA methods results in an optimal set of parameters for minimum energy consumption in WSN related to the type of selected energy model for data communication, while the best performance of the GA method [energy consumption \((\hbox {E}=3.49\times 10^{-4}\,\hbox {J})\)] is obtained in WSN for communication distance (d) \({\ge }87\,\hbox {m}\) in between the sensor cluster head and a base station.
Vorheriger Artikel Throughput-efficient coalition formation of selfish/altruistic nodes in ad hoc networks: a hedonic game approach
Nächster Artikel Energy harvesting assisted cognitive radio: random location-based transceivers scheme and performance analysis

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Literatur
1.
Yang, K. (2014). Wireless sensor networks—Principles, design and applications. London: Springer.
2.
Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications Surveys and Tutorials, 15(2), 551–591.CrossRef
3.
Younis, M., & Akkaya, K. (2008). Strategies and techniques for node placement in wireless sensor networks: A survey. Ad Hoc Networks, 6(4), 621–655.CrossRef
4.
Rawat, P., Singh, K. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. Journal of Supercomputing, 68(1), 1–48.CrossRef
5.
Demigha, O., Hidouci, W. K., & Ahmed, T. (2013). On energy efficiency in collaborative target tracking in wireless sensor network: A review. IEEE Communications Surveys and Tutorials, 15(3), 1210–1222.CrossRef
6.
Abo-Zahhad, M., Amin, O., Farrag, M., & Ali, A. (2014). Survey on energy consumption models in wireless sensor networks. Open Transaction on Wireless Sensor Network, 1(1), 1–4.
7.
Basaran, C., & Kang, K. D. (2009). Quality of service in wireless sensor networks. In S. C. Misra, I. Woungang, & S. Misra (Eds.), Guide to wireless sensor networks (pp. 305–321). London: Springer.CrossRef
8.
Mansourkiaie, F., & Ahmed, M. H. (2015). Cooperative routing in wireless networks: A comprehensive survey. IEEE Communications Surveys and Tutorials, 17(2), 604–626.CrossRef
9.
Han, G., Xu, H., Duong, T. Q., Jiang, J., & Hara, T. (2013). Localization algorithms of wireless sensor networks: A survey. Telecommunication Systems, 54(4), 2419–2436.CrossRef
10.
Bajaber, F., & Awan, I. (2014). An efficient cluster-based communication protocol for wireless sensor networks. Telecommunication Systems, 55(3), 387–401.CrossRef
11.
Anastasi, G., Conti, M., Di Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568.CrossRef
12.
Mini, R. A., & Loureiro, A. A. (2009). Energy in wireless sensor networks. In B. Garbinato, H. Miranda, & L. Rodrigues (Eds.), Middleware for network eccentric and mobile applications (pp. 3–24). Berlin: Springer.CrossRef
13.
Shaikh, F. K., & Zeadally, S. (2016). Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55, 1041–1054.CrossRef
14.
Karahan, A., Erturk, I., Atmaca, S., & Cakici, S. (2014). Effects of transmit-based and receive-based slot allocation strategies on energy efficiency in WSN MACs. Ad Hoc Networks, 13, 404–413.CrossRef
15.
Chidean, M. I., Morgado, E., Sanromán-Junquera, M., Ramiro-Bargueno, J., Ramos, J., & Caamaño, A. J. (2016). Energy efficiency and quality of data reconstruction through data-coupled clustering for self-organized large-scale WSNs. IEEE Sensors Journal, 16(12), 5010–5020.CrossRef
16.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.CrossRef
17.
Snajder, B., Jelicic, V., Kalafatic, Z., & Bilas, V. (2016). Wireless sensor node modelling for energy efficiency analysis in data-intensive periodic monitoring. Ad Hoc Networks, 49, 29–41.CrossRef
18.
Raza, U., Bogliolo, A., Freschi, V., Lattanzi, E., & Murphy, A. L. (2016). A two-prong approach to energy-efficient WSNs: Wake-up receivers plus dedicated, model-based sensing. Ad Hoc Networks, 45, 1–12.CrossRef
19.
Norouzi, A., & Zaim, A. H. (2014). Genetic algorithm application in optimization of wireless sensor networks. The Scientific World Journal, 2014, 1–15.CrossRef
20.
Peiravi, A., Mashhadi, H. R., & Javadi, S. H. (2013). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114–126.CrossRef
21.
Li, Z., & Lei, L. (2009). Sensor node deployment in wireless sensor networks based on improved particle swarm optimization. In Proceedings of ASEMD (pp. 215–217).
22.
Zungeru, A. M., Seng, K. P., Ang, L. M., & Chong Chia, W. (2013). Energy efficiency performance improvements for ant-based routing algorithm in wireless sensor networks. Journal of Sensors, 2013, 1–17.CrossRef
23.
Lanza-Gutierrez, J. M., & Gomez-Pulido, J. A. (2015). Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for relay node deployment in wireless sensor networks. Applied Soft Computing, 30, 675–687.CrossRef
24.
Zeng, B., & Dong, Y. (2016). An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Applied Soft Computing, 41, 135–147.CrossRef
25.
Chang, W. L., Zeng, D., Chen, R. C., & Guo, S. (2015). An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks. International Journal of Machine Learning and Cybernetics, 6(3), 375–383.CrossRef
26.
Zhu, N., & Vasilakos, A. V. (2016). A generic framework for energy evaluation on wireless sensor networks. Wireless Networks, 22(4), 1199–1220.CrossRef
27.
Catarinucci, L., Colella, R., Del Fiore, G., Mainetti, L., Mighali, V., Patrono, L., et al. (2014). A cross-layer approach to minimize the energy consumption in wireless sensor networks. International Journal of Distributed Sensor Networks, 10(1), 268284.CrossRef
28.
Shareef, A., & Zhu, Y. (2010). Energy modeling of wireless sensor nodes based on Petri nets. In Proceedings of ICPP (pp. 101–110).
29.
Abdul-Salaam, G., Abdullah, A. H., Anisi, M. H., Gani, A., & Alelaiwi, A. (2016). A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols. Telecommunication Systems, 61(1), 159–179.CrossRef
30.
Du, W., Mieyeville, F., & Navarro, D. (2010). Modeling energy consumption of wireless sensor networks by system. In Proceedings of ICSNC (pp. 94–98).
31.
Keskin, M. E., Altınel, İ. K., Aras, N., & Ersoy, C. (2014). Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility. Ad Hoc Networks, 17, 18–36.CrossRef
32.
He, S., Chen, J., Yau, D. K., & Sun, Y. (2012). Cross-layer optimization of correlated data gathering in wireless sensor networks. IEEE Transactions on Mobile Computing, 11(11), 1678–1691.CrossRef
33.
Liu, H., Chu, X., Leung, Y. W., & Du, R. (2013). Minimum-cost sensor placement for required lifetime in wireless sensor-target surveillance networks. IEEE Transactions on Parallel and Distributed Systems, 24(9), 1783–1796.CrossRef
34.
Gu, Y., Ji, Y., Li, J., & Zhao, B. (2013). ESWC: Efficient scheduling for the mobile sink in wireless sensor networks with delay constraint. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1310–1320.CrossRef
35.
Melodia, T., Pompili, D., Gungor, V. C., & Akyildiz, I. F. (2007). Communication and coordination in wireless sensor and actor networks. IEEE Transactions on Mobile Computing, 6(10), 1116–1129.CrossRef
36.
Raghunathan, V., Schurgers, C., Park, S., & Srivastava, M. B. (2002). Energy-aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2), 40–50.CrossRef
37.
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.CrossRef
38.
Zhang, H., Zhang, S., & Bu, W. (2014). A clustering routing protocol for energy balance of wireless sensor network based on simulated annealing and genetic algorithm. International Journal of Hybrid Information Technology, 7(2), 71–82.CrossRef
39.
Goldberg, D. E. (2006). Genetic algorithms. New Delhi: Pearson Education.
40.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.CrossRef
41.
Gen, M., & Cheng, R. (2000). Genetic algorithms and engineering optimization. Toronto: Wiley.
42.
Norouzi, A., Babamir, F. S., & Zaim, A. H. (2011). A new clustering protocol for wireless sensor networks using genetic algorithm approach. Wireless Sensor Network, 3(11), 362–370.CrossRef
43.
Naranjo, P. G. V., Shojafar, M., Mostafaei, H., Pooranian, Z., & Baccarelli, E. (2016). P-SEP: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. The Journal of Supercomputing, 73(2), 733–755.CrossRef
44.
Umar, M. M., Mehmood, A., & Song, H. (2016). SeCRoP: Secure cluster head centered multi-hop routing protocol for mobile ad hoc networks. Security and Communication Networks, 9(16), 3378–3387.CrossRef
45.
Ahmadi, A., Shojafar, M., Hajeforosh, S. F., Dehghan, M., & Singhal, M. (2014). An efficient routing algorithm to preserve k-coverage in wireless sensor networks. The Journal of Supercomputing, 68(2), 599–623.CrossRef
46.
Rani, S., Talwar, R., Malhotra, J., Ahmed, S. H., Sarkar, M., & Song, H. (2015). A novel scheme for an energy efficient Internet of Things based on wireless sensor networks. Sensors, 15(11), 28603–28626.CrossRef
Metadaten
Titel
An energy optimization in wireless sensor networks by using genetic algorithm
verfasst von
Sunil Kr. Jha
Egbe Michael Eyong
Publikationsdatum
03.05.2017
Verlag
Springer US
Erschienen in
Telecommunication Systems / Ausgabe 1/2018
Print ISSN: 1018-4864
Elektronische ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-017-0324-1

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