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A Case Study of Bio-Optimization Techniques for Wireless Sensor Network in Node Location Awareness


  • School of Electrical Engineering, VIT University, Vellore – 632014, Tamil Nadu, India


Background: In wireless sensor networks the sensors are deployed randomly in the sensing field, therefore the location awareness of the deployed nodes is challenging. The objective is to estimate the location of the deployed sensor nodes through bio optimized algorithms. Methods: This paper compares performance of three best bio-optimization algorithms available: Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFLA) and Firefly Algorithms (FFA) in estimating the optimal location of randomly deployed sensors. The optimum solution helps to ensure the maximum coverage of sensing capability and Quality of Service (QoS) of the network. The simulation is done using LabVIEW to understand the performance of these algorithms. Findings: The objective function and fitness value is calculated for the algorithms and based on those the error value is determined for 50 nodes and 10 beacons in a 100x100 sensor field dimension. To evaluate the performance under noisy environment a noise of 2% and 5% is added and simulated. Also the transmission radius of beacons changed from 25 to 20m to analyse the error value for optimum location estimation, however when changing the transmission radius it is found that the number of localized nodes are reduced. The performance is analysed based on localization error, computing time and memory. SFLA offers better in localization error however its computation time is more. The PSO takes less computing time and memory compared to FFA. The average error Vs sensor nodes depicts SFLA performance is superior. The methods adapted in this paper throw more lights on performance of these algorithms under noisy environment and effect of localization error under various transmission radiuses. Improvements: These algorithms can be further analysed for centralised localization and also location awareness for mobile nodes.



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  • Kulkarni RV, Ganesh KV. Bio-Inspired node localisation in wireless sensor networks. IEEE International conference on system; Sant Antonio, TX. USA: 2009. p. 205–10.
  • Singh PK, Tripathi B, Singh NP. Node localization wireless sensor networks. (IJCSIT) International Journal of Computer Science and Information Technologies. 2011;2(6):2568–72.
  • Gopakumar A, Jacob L. Localization in wireless sensor networks using particle swarm optimization. Proceedings IET International conference on Wireless, Mobile and Multimedia Networks; Mumbai, India: 2008. p. 227–30.
  • Fan X, Du F. Shuffled frog leaping algorithm based unequal clustering strategy for wireless sensor networks. Applied Mathematics and Information Sciences. 2015; 9(3):1415–26.
  • Cao S, Wang J, Yang XS. A Wireless sensor network location algorithm based on firefly algorithm. Berlin, Germany: Springer Communications in Computer and Information Science; 2012. p 18–26.
  • Chandirasekaran D, Jayabarathi T. Wireless sensor networks node localization-a performance comparison of shuffled frog leaping and firefly algorithm in LabVIEW.Telkomnika Indonesian Journal of Electrical Engineering.2015 Jun; 14(3):516–24. ISSN: 2302-4046
  • Kulkarni RV, Venayagamoorthy GK. Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Transactions on Systems Man and Cybernetics-Part C Application and Reviews. 2010 Nov;40(6):663–75.
  • Cobo L, Castro H, Quintero A. A location routing protocol based on smart antennas for wireless sensor networks. Indian Journal of Science and Technology. 2015 Jun;8(11):70655.
  • Ren W, Shao C. A localization algorithm based on SFLA and PSO for wireless sensor networks. Information technology journal. 2013:12(3):502–5. ISSN 1812-5638
  • Savarese C. Robust positioning algorithms for distributed Ad-Hoc wireless sensor networks. Research project at Berkeley Wireless Research Center. 2002. p. 317–27.
  • Wang X, Luo J, Liu Y, Shanshan Li, Dezun Dong. Component-based localization in sparse wireless networks. IEEE/ACM transactions on networking. 2011 Apr;19(2):540–8.
  • Szynkiewicz E, Marks M. Optimization schemes for wireless sensor network localization. International Journal of Applied Mathematics and Computer Science. 2009; 19(2):291–302.
  • Hu X, Shi Y, Eberhart R. Recent advances in particle swarm. Proceedings of CEC 2004 Congress on Evolutionary Computation.IEEE Press. 2004 Jun 19–23; 1:90–7.
  • Farahani M, Movahhed SB, Ghaderi SF. A hybrid meta-heuristic optimization algorithm based on SFLA. 2nd International Conference on Engineering Optimization;Lisbon, Portugal. 2010 Sep 6-9. p. 1–8.
  • Ebrahimi J, Hosseinian S, Gharehpetian G B. Unit commitmentcommitment problem solution using shuffled frog leaping algorithm. IEEE Transactions On Power Systems. 2011 May;26(20):573–81.
  • Sun H, Zhao J. Application of particle sharing based particle swarm frog leaping hybrid optimization algorithm in wireless sensor. Journal of Information and Computational Science. 2011; 8(14):3181–8.
  • Yang XS. Firefly Algorithm, L´evy Flights and Global Optimization. arXiv: 1003.1464v1 [math.OC]. 2009 Oct 7.p.209–18.


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