Social Group Optimization (SGO) for Clustering in Wireless Sensor Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Wireless Sensor Network (WSN) is a domain which has its application in the variety of fields like military, disaster management, environment monitoring. Energy consumption is one of the key challenges in the field of WSN where researchers are strongly exploring and discovering new techniques or methods. Direct or hop-by-hop transmission of data from the node to the BS leads to more number of transmissions. Clustering is applied to reduce the number of transmissions. Nodes can consume less energy if the distance between node to node or from node to BS is less. An optimization technique is used to minimize the transmission distance and to dynamically select the number of cluster heads. Social Group Optimization (SGO) is implemented, and the results are compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

Keywords

Wireless Sensor Network Social Group Optimization Clustering Sensor nodes Base station Optimization 

References

  1. 1.
    Abba Ari, A., Gueroui, A., Yenke, B.O., Labraoui. N.: Energy efficient clustering algorithm for Wireless Sensor Networks using the ABC metaheuristic. In: 2016 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, pp. 1–6 (2016)Google Scholar
  2. 2.
    Lalwani, P., Das, S.: Bacterial foraging optimization algorithm for CH selection and routing in wireless sensor networks. In: 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, pp. 95–100 (2016)Google Scholar
  3. 3.
    Aziz, L., Raghay, S., Aznaoui, H., Jamali, A.: A new approach based on a genetic algorithm and an agent cluster head to optimize energy in Wireless Sensor Networks. In: 2016 International Conference on Information Technology for Organizations Development (IT4OD), Fez, pp. 1–5 (2016)Google Scholar
  4. 4.
    Kalla, N., Parwekar, P.: A study of clustering techniques for Wireless Sensor Networks (WSN). In: 1st International Conference on Smart Computing & Informatics (SCI) (2017)Google Scholar
  5. 5.
    Al-kahtani, M.S.: Efficient cluster-based sleep scheduling for M2M communication network. Res. Artic. Comput. Eng. Comput. Sci. 40(8), 2361–2373 (2015)CrossRefGoogle Scholar
  6. 6.
    Alaybeyoglu, A.: An efficient monte carlo-based localization algorithm for mobile Wireless Sensor Networks. Arab. J. Sci. Eng. 1375–1384 (2015) (Springer Science & Business Media B.V.)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kamal, S., Jalal, A.: A hybrid feature extraction approach for human detection, tracking and activity recognition using depth sensors. Res. Artic. Comput. Eng. Comput. Sci. 41(3), 1043–1051 (2016)Google Scholar
  8. 8.
    Shi, X., Fan, L., Ling, Y., He, J., Xiong, D.: Dynamic and Quantitative Method of Analyzing Clock Inconsistency Factors among Distributed Nodes. Res. Artic. Comput. Eng. Comput. Sci. 40(2), 519–530 (2015)Google Scholar
  9. 9.
    Akila, I.S., Venkatesan, R., Abinaya, R.: A PSO based energy efficient clustering approach for Wireless Sensor Networks. In: 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), Chennai, pp. 259–264 (2016)Google Scholar
  10. 10.
    Elhabyan, R.S., Yagoub, M.C.E.: Evolutionary algorithms for cluster heads election in wireless sensor networks: performance comparison. In: 2015 Science and Information Conference (SAI), London, pp. 1070–1076 (2015)Google Scholar
  11. 11.
    Latiff, N.M.A., Tsimenidis, C.C., Sharif, B. S.: Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: 007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, pp. 1–5 (20070Google Scholar
  12. 12.
    Zhou, Y., Wang, N., Xiang. W.: Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. In: IEEE Access, pp. 2241–2253 (2017)CrossRefGoogle Scholar
  13. 13.
    Satapathy, S., Naik, A.: Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intell. Syst. 173–203 (2016)CrossRefGoogle Scholar
  14. 14.
    Parwekar, P., Rodda, S., Satapathy Dr. S.C.: Solar energy harvesting: an answer for energy hungry wireless sensor networks. In: CSI Communications, pp. 23–26 (2016)Google Scholar
  15. 15.
    Jin, S., Zhou, M., Wu. A.S.: Sensor network optimization using a genetic algorithm. In: Proceedings of the 7th World Multiconference on Systemics, Cybernetics, and Informatics, pp. 109–116 (2003)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Anil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia

Personalised recommendations