International Journal of Scientific & Technology Research

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IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

An Energy Aware Data Aggregation In Wireless Sensor Network Using Multi Verse Optimized Connected Dominant Set

[Full Text]



K.Santhoshkumar Dr.P.Suganthi



Wireless Sensor Network (WSN), Energy, Data Aggregation, Routing, Multi-Verse Optimizer (MVO), Connected Dominating Set (CDS),Fault Tolerance.



The Wireless Sensor Network (WSN) can be defined as a self-organizing multi-hop network composed of several sensor nodes that are scattered within a particular region by means of wireless communication. The network also faces some critical barriers such as the transmission of redundant data, consumption of energy owing to heterogeneous traffic and fault tolerance. Aggregation of data is an extremely critical technique that is used for data processing in the WSN. With aggregation of data, energy consumption can be reduced by the elimination of all types of redundant data or by means of bringing down the packets forwarded. There are several routing protocols that are based on clustering which provide efficient methods to extend the WSN lifetime. It further involves large node quantity which entails the multi-hop network wherein the nodes interact for vicinity with one another that has higher routing responsibilities. Connected Dominating Set (CDS) will serve to be a backbone for the WSN as there has been no infrastructure that is fixed or any centralized management available in the WSN. Using the CDS, routing can become easier and easily adaptable to changes in topology. The problem of the CDS is extensively studied using undirected graphs in the Unit Disk Graphs (UDG), wherein every senor node has the same range of transmission. In this work, a Multi-hop - Low Energy Adaptive Clustering Hierarchy (M-LEACH) based protocol with a multiverse optimized CDS algorithm is proposed. The results have proved that the method was attain the better levels of performance in terms of the number of clusters, energy consumption, and lifetime computation. It also has a lower end-to-end delay and packet loss rate.



[1] Tiwari, P., Saxena, V. P., Mishra, R. G., & Bhavsar, D. (2015). Wireless sensor networks: Introduction, advantages, applications and research challenges. HCTL Open International Journal of Technology Innovations and Research (IJTIR), 14, 1-11.
[2] Sangolgi, N. B., & Zakir, S. K. A. (2013). Energy aware data aggregation technique in WSN. International Journal of Scientific and Research Publications, 3(10).
[3] Ambigavathi, M., & Sridharan, D. (2018). Energy-aware data aggregation techniques in wireless sensor network. In Advances in Power Systems and Energy Management (pp. 165-173). Springer, Singapore.
[4] Latiff, N. A., Tsimenidis, C. C., & Sharif, B. S. (2007, September). Energy-aware clustering for wireless sensor networks using particle swarm optimization. In 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1-5). IEEE.
[5] Ababneh, A. A., & Al-Zboun, E. (2016). EDAC: A Novel Energy-Aware Clustering Algorithm for Wireless Sensor Networks. International Journal of Advanced Computer Science & Applications, 1(7), 333-338.
[6] Kumar, S., Kumar, S., & Bhushan, B. (2014). Energy aware clustering protocol (EACP) for Heterogeneous WSNs. arXiv preprint arXiv:1408.2910.
[7] Humidi, N. A., & Chowdhary, V. (2019). Lightweight Data Transmission Scheme Based on Data Aggregation Technique in Wireless Sensor Networks. Girish, Lightweight Data Transmission Scheme Based on Data Aggregation Technique in Wireless Sensor Networks (May 17, 2019).
[8] Tabatabaei, S., Rajaei, A., & Rigi, A. M. (2019). A Novel Energy-Aware Clustering Method via Lion Pride Optimizer Algorithm (LPO) and Fuzzy Logic in Wireless Sensor Networks (WSNs). Wireless Personal Communications, 1-23.
[9] Subramanian, S. A., &Krishnasamy, K. (2019). Residual Energy and Time Aware Cluster Based Data Gathering in Wireless Sensor Network. Sensor Letters, 17(2), 150-159.
[10] Darabkh, K. A., Odetallah, S. M., Al-qudah, Z., Ala’F, K., &Shurman, M. M. (2019). Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks. Applied Soft Computing, 80, 154-166.
[11] Darabkh, K. A., & Al-Jdayeh, L. (2019). AEA-FCP: an adaptive energy-aware fixed clustering protocol for data dissemination in wireless sensor networks. Personal and Ubiquitous Computing, 1-19.
[12] Bouamama, S., Blum, C., &Fages, J. G. (2019). An algorithm based on ant colony optimization for the minimum connected dominating set problem. Applied Soft Computing, 80, 672-686.
[13] Hedar, A. R., Ismail, R., El-Sayed, G. A., &Khayyat, K. M. J. (2019). Two Meta-Heuristics Designed to Solve the Minimum Connected Dominating Set Problem for Wireless Networks Design and Management. Journal of Network and Systems Management, 1-41.
[14] Hedar, A. R., & El-Sayed, G. A. (2018, June). Parallel genetic algorithm with elite and diverse cores for solving the minimum connected dominating set problem in wireless networks topology control. In Proceedings of the 2nd International Conference on Future Networks and Distributed Systems (p. 27). ACM.
[15] Gogu, A., Nace, D., Dilo, A., Meratnia, N., & Ortiz, J. H. (2012). Review of optimization problems in wireless sensor networks. In Telecommunications Networks—Current Status and Future Trends (pp. 153-180). New York, NY, USA: InTech.
[16] Sharma, N., & Verma, V. (2013). Energy efficient LEACH protocol for wireless sensor network. International Journal of Information and Network Security, 2(4), 333.
[17] Rauthan, J. S., & Mishra, S. (2012). An improved cluster based multi-hop routing in self-organizing wireless sensor networks. networks, 14(15), 16.
[18] Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010, July). MR-LEACH: multi-hop routing with low energy adaptive clustering hierarchy. In 2010 Fourth International Conference on Sensor Technologies and Applications (pp. 262-268). IEEE.
[19] Purohit, G. N., & Sharma, U. (2010). Constructing minimum connected dominating set: Algorithmic approach. International journal on applications of graph theory in wireless ad hoc networks and sensor networks, 2(3).
[20] Rai, M., Verma, S., &Tapaswi, S. (2009). A power aware minimum connected dominating set for wireless sensor networks. JNW, 4(6), 511-519.
[21] S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, "Multi-verse optimizer: a nature-inspired algorithm for global optimization," Neural Computing and Applications, vol. 27, pp. 495-513, 2016.