Abstract
Wireless sensor network is designed with low energy, and limited data rates. In wireless sensor networks, the sensors are designed with limited energy rates and bandwidth rates. Maximizing the network lifetime is a key aspect in traditional Wireless communication to maximize the data rate in typical environments. The clustering is an effective topology control approach to organize efficient communication in traditional sensor network models. However, the hierarchical-based clustering approach consumes more energy rates for large-scale networks for data distribution and data gathering process, the selection of efficient cluster and cluster heads (CH) play an import role to achieve the goal. In this paper, we proposed an Adaptive Genetic Co-relation Node Optimization for selecting an optimal number of clusters with cluster heads based on the node status or fitness level. Using the tradition Genetic Algorithm, we achieved the Cluster head selection and the co-relation approach identifies the optimal clusters heads in a network for data distribution. Cluster head election is an important parameter, which leads to energy minimization, and it is implemented by Genetic Algorithm. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
I.F. Akyildiz et al., A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)
Q. Zhang et al., The design of hybrid MAC protocol for industry monitoring system based on WSN. Procedia Eng. 23, 290–295 (2011)
A.H. Abbasi et al., Survey on clustering algorithms for wireless sensor networks. Comput. Commun. 30, 2826–2841 (2010)
W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, vol. 2 (2000), p. 10
S. Lindsey, C.S. Raghavendra, PEGASIS: power-efficient gathering in sensor information systems. In Proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, MT, USA, vol. 3 (9–16 March 2002), p. 3
A. Manjeshwar, D.P. Agrawal, TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings of the 15th International Parallel and Distributed Processing Symposium, San Francisco, CA, USA (23–27 April 2001), pp. 2009–2015
A. Manjeshwar, Q.-A. Zeng, D.P. Agrawal, An analytical model for information retrieval in wireless sensor networks using enhanced APTEEN protocol. IEEE Trans. Parallel Distrib. Syst. 13(12), 1290–1302 (2002)
S. Wang, T.L.N. Nguyen, Y. Shin, Energy-efficient clustering algorithm for magnetic induction-based underwater wireless sensor networks. IEEE Access. https://doi.org/10.1109/access.2018.2889910
Y. Zhou, S. Taneja, C. Zhang, X. Qin, GreenDB: Energy-efficient prefetching and caching in database clusters. IEEE Trans. Parallel Distrib. Syst. https://doi.org/10.1109/tpds.2018.2874014
A. Mehmood, Z. Lv, J. Lloret, M. Muneer Umar, ELDC: an artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs. IEEE Trans. Emerg. Top. Comput. https://doi.org/10.1109/tetc.2017.2671847
S. Tanwar, S. Tyagi, N. Kumar, M.S. Obaidat, LA-MHR: learning automata based multilevel heterogeneous routing for opportunistic shared spectrum access to enhance lifetime of WSN. Digit. Object Identifier. https://doi.org/10.1109/jsyst.2018.2818618
X. Tao, W. Song, Location-dependent task allocation for mobile crowdsensing with clustering effect. IEEE Internet Things J. https://doi.org/10.1109/jiot.2018.2866973
T-W. Kuo, M-J. Tsai, On the construction of data aggregation tree with minimum energy cost in wireless sensor networks: NP-completeness and approximation algorithms. https://doi.org/10.1109/infcom.2012.6195659
M. Mohammed Nasr, A.M.S. Abdelgader, L-F. Shen, Analytical exploration of energy savings for parked vehicles to enhance VANET connectivity. IEEE Trans. Intell. Transp. Syst. (Early Access)
A.H. Marc, L. Fuksz, P.C. Pop, D. Dănciulescu, A novel hybrid algorithm for solving the clustered vehicle routing problem. In Hybrid Artificial Intelligent Systems, ed. by E. Onieva, I. Santos, E. Osaba, H. Quintián, E. Corchado. HAIS 2015. Lecture Notes in Computer Science, vol. 9121 (Springer)
P.C. Srinivasa Rao, P.K. Jana, H. Banka, A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks (Springer Science+Business Media New York, 2016)
L.F. Akyildiz, T. Melodia, K.R. Chowdhury, A survey on wireless multimedia sensor networks. Comput. Netw. (Elsevier) 51(4), 921–960 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Srikanth, N., Siva Ganga Prasad, M. (2020). An Adaptive Genetic Co-relation Node Optimization Routing for Wireless Sensor Network. In: Jain, L., Tsihrintzis, G., Balas, V., Sharma, D. (eds) Data Communication and Networks. Advances in Intelligent Systems and Computing, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-15-0132-6_7
Download citation
DOI: https://doi.org/10.1007/978-981-15-0132-6_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0131-9
Online ISBN: 978-981-15-0132-6
eBook Packages: EngineeringEngineering (R0)