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Fire Fly Optimization Algorithm based Clustering by Preventing Residual Nodes in Mobile Wireless Sensor Networks

Affiliations

  • Department of Computer Science and Engineering, Chandigarh University, Gharuan - 140413, Punjab, India

Abstract


This paper regarding the use of the natural phenomenon’s based optimization techniques to resolve the problem of nonclustered nodes. Objectives: This research minimizes energy consumption. Its objective is to provide efficient clustering for avoidance of residual nodes and prevents occurrence of dead nodes with usage of mobile nodes. Methods: In this research paper firstly deploy number of mobile nodes in specified region with usage of MATLAB environment. For grouping of these nodes LEACH protocol uses for clustering. During clustering with LEACH some nodes residual and not become part of any cluster. Firefly Optimization used for prevention of residual nodes and efficient clustering. It uses distance and light intensity parameters for clustering. GSA algorithm used for finding best path for data transmission with less energy consumption. Findings: In this research paper shows efficient clustering of nodes with prevention of residual nodes. In existing LEACH protocol some static nodes are residual and not become part of any cluster. These nodes send data directly to base station and consume large amount of energy. These individual nodes die early due to maximum energy consumption. But in this research mobile nodes are used and occurrence of dead nodes prevented on basis of distance and light intensity parameters. Nodes which are at minimum distance are brighter than farthest nodes. Minimum distance nodes join nearest cluster and prevent formation of remaining nodes. Improvements: In this paper results shown that it performs better in terms of network lifetime, energy consumption, end to end delay and throughput and number of dead nodes.

Keywords

Cluster Formation, Firefly Optimization, Routing, Wireless Sensor Networks.

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References


  • Tolba FD, Ajib W, Obaid A. Distributed clustering algorithm for mobile wireless sensors networks. IEEE; 2013.
  • Varma GNSA, Reddy GAK, Theja YR, Kumar TA. Cluster Based multipath Dynamic Routing (CBDR) protocol for wireless sensor networks. Indian Journal of Science and Technology. 2015 Jan; 8(S2). DOI: 10.17485/ijst/2015/v8iS2/57793.
  • Abad MFK, Jamali MJA. Modify LEACH algorithm for wireless sensor network. International Journal of Computer Science. 2011 Sep; 8(5), No 1:219–24.
  • Revathi AR, Santhi B. Efficient clustering for wireless sensor networks using evolutionary computing. Indian Journal of Science and Technology. 2015 Jul; 8(14).
  • Chandl KK, Bharati PV, Ramanjaneyulu BS. Optimized energy efficient routing protocol for life-time improvement in wireless sensor networks. International Conference on Advances in Engineering, Science and Management (ICAESM -2012); 2012 Mar 30–31.
  • Aliouat Z, Aliouat M. Efficient management of energy budget for PEGASIS routing protocol. 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT); 2012.
  • Anitha RU, Kamalakkannan P. Enhanced cluster based routing protocol for mobile nodes in wireless sensor network. International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME); 2013.
  • El Khediri S, Nasri N, Wei A, Kachouri A. A new approach for clustering in wireless sensors networks based on LEACH. International Workshop on Wireless Networks and Energy Saving Techniques (WNTEST); 2014.
  • Kumar R, Kumar D. Hybrid swarm intelligence energy efficient clustered routing algorithm for wireless sensor networks. Hindawi Publishing Corporation, Journal of Sensors; 2016.
  • Elhabyan RS. PSO-HC: Particle Swarm Optimization Protocol for Hierarchical Clustering in wireless sensor networks. International Conference on Computing: Networking; 2014.
  • Latiff NMA, Tsimenidis CC, Sharif BS. Performance comparison of optimization algorithms for clustering in wireless sensor networks. IEEE; 2007.
  • Bains V, Sharma K. Ant colony based routing in wireless sensor networks. International Journal of Electronics and Computer Science Engineering. 2012; 1(4):2516–54 13. Parvin R. Particle swarm optimization based clustering by preventing residual nodes in wireless sensor networks. Sensors Journal; 2015.
  • Apostolopoulos T, Vlachos A. Application of the Firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics. 2011.
  • Arora S, Singh S. The Firefly Optimization algorithm: Convergence analysis and parameter selection. International Journal of Computer Applications. 2013 May; 69(3).
  • Manshahia MS. A Firefly based energy efficient routing in wireless sensor networks. African Journal of Computing & ICT; 2015.
  • Bansal JC, Deep K. Optimization of directional overcurrent relay times by particle swarm optimization. Swarm Intelligence Symposium (SIS 2008); 2008. p. 1–7.
  • Rafsanjani MK, Dowlatshahi MB. Using gravitational search algorithm for finding near-optimal base station location in two-tiered WSNs. International Journal of Machine Learning and Computing. 2012 Aug; 2(4).
  • Rashedi E, Nezambadi-pour H, Saryadzi S. GSA: A Gravitational Search Algorithm. Information Science; 2009.
  • Parvin JR, Vasanthanayaki C. Gravitational search algorithm based mobile aggregator sink nodes for energy efficient wireless sensor networks. International Conference on Circuits, Power and Computing Technologies; 2013.
  • Krishnaprabha R, Gopakumar A. Performance of gravitational search algorithm in wireless sensor network localization. 2014 National Conference on Communication, Signal Processing and Networking (NCCSN); 2014.

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