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An energy-aware cluster-based stable protocol for wireless sensor networks

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Abstract

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user-friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing techniques to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation technique in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, cluster-head (CH) has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long-run operation of WSN. In this paper, genetic algorithm (GA)-based threshold-sensitive energy-efficient routing protocol (TERP) is proposed to prolong network lifetime. Multi-hop communication between CHs and base station (BS) is utilized using GA to achieve optimal link cost for load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TERP named stable TERP (STERP) so as to extend the stability period (time interval from initial time to the death of first node) of the network. In STERP, energy-aware heuristics is applied for CH selection in order to improve the stability period. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

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References

  1. Afsar MM, Tayarani-N M (2014) Clustering in sensor networks: a literature survey. J Netw Comput Appl 46:198–226

    Article  Google Scholar 

  2. Pantazis NA, Nikolidakis SA, Vergados DD (2013) Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutor 15(2):551–591

    Article  Google Scholar 

  3. Arain QA, Uqaili MA, Deng Z, Memon I, Jiao J, Shaikh MA, Zubedi A, Ashraf A, Arain UA (2016) Clustering based energy efficient and communication protocol for multiple mix-zones over road networks. Wirel Pers Commun 1–18

  4. Khalil EA, Attea BA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evol Comput 1:195–203

    Article  Google Scholar 

  5. Attea BA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl Soft Comput 12:1950–1957

    Article  Google Scholar 

  6. Khalil EA, Attea BA (2013) Stable-aware evolutionary routing protocol for wireless sensor networks. Wirel Pers Commun 69(4):1799–1817

    Article  Google Scholar 

  7. Hussain S, Matin AW, Islam O (2007) Genetic algorithm for hierarchical wireless sensor networks. J Netw 2:87–97

    Google Scholar 

  8. Mittal N, Singh U, Sohi BS (2017) A novel energy efficient stable clustering approach for wireless sensor networks. Wirel Pers Commun, Springer 95(3):2947–2971

    Article  Google Scholar 

  9. Kuila P, Jana PK (2014) A novel differential evolution based clustering algorithm for wireless sensor networks. Appl Soft Comput 25:414–425

    Article  Google Scholar 

  10. Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Netw 18:847–860

    Article  Google Scholar 

  11. Mittal N, Singh U, Sohi BS (2017) Harmony search algorithm based threshold-sensitive energy-efficient clustering protocols for WSNs. Adhoc Sens Wirel Netw 36(1–4):149–174

    Google Scholar 

  12. Hoang DC, Yadav P, Kumar R, Panda SK (2014) Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Trans Ind Inf 10(1):774–783

    Article  Google Scholar 

  13. Mittal N, Singh U, Salgotra R, Sohi, BS (2017) A boolean spider monkey optimization based energy efficient clustering approach for WSNs. Wirel Netw 1–17

  14. Bennani K, El Ghanami D (2012) Particle swarm optimization based clustering in wireless sensor networks: the effectiveness of distance altering. In: International conference on complex systems (ICCS), Omaha, Nebraska, pp 1–4

  15. Heinzelman WB, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proc. 33rd annual Hawaii international conference on system siences (HICSS-33), IEEE, 2000, p 223. https://doi.org/10.1109/hicss.2000.926982

  16. Manjeshwar A, Agrawal DP (2001) TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In: Proc. international parallel and distributed processing symposium (IPDPS’01) workshops, 2001, pp 2009–2015, San Francisco, CA, USA. https://doi.org/10.1109/ipdps.2001.925197

  17. Manjeshwar A, Agrawal D (2002) APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In: International parallel and distributed processing symposium, Florida, pp 195–202

  18. Smaragdakis G, Matta I, Bestavros A (2004) SEP: a stable election protocol for clustered heterogeneous wireless sensor networks. In: Proc. international workshop on SANPA. http://open.bu.edu/xmlui/bitstream/handle/2144/1548/2004-022-sep.pdf?sequence=1

  19. Qing L, Zhu Q, Wang M (2006) Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor network. Comput Commun 29:2230–2237

    Article  Google Scholar 

  20. Kang SH, Nguyen T (2012) Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Commun Lett 16(9):1396–1399

    Article  Google Scholar 

  21. Kumar D, Aseri TC, Patel RB (2009) EEHC: energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput Commun 32:662–667

    Article  Google Scholar 

  22. Kumar D (2014) Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wirel Sens Syst 4(1):9–16

    Google Scholar 

  23. Tarhani M, Kavian YS, Siavoshi S (2014) SEECH: scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sens J 14(11):3944–3954

    Article  Google Scholar 

  24. Rajan S, Mittal N, Sohi BS (2018) Zone-based energy efficient routing protocol for wireless sensor networks. Int J Inf Technol Web Eng (IJITWE), pp 1–18

  25. Mittal N, Preet K, Sohi BS, Singh U (2015) Mobility based application specific low power routing protocol for wireless sensor networks. In: IEEE conference on recent advances in engineering and computational sciences, UIET, Panjab University, Chandigarh, pp 1–6

  26. Aderohunmu FA, Deng JD, Purvis MK (2011) A deterministic energy-efficient clustering protocol for wireless sensor networks. In: 7th International conference on intelligent sensors, sensor networks and information processing (ISSNIP ’11), IEEE, pp 341–346. https://doi.org/10.1109/issnip.2011.6146592

  27. Mittal N, Singh U (2015) Distance-based residual energy-efficient stable election protocol for WSNs. Arab J Sci Eng 40(6):1637–1646

    Article  Google Scholar 

  28. Mittal N, Singh U, Sohi BS (2017) A stable energy efficient clustering protocol for wireless sensor networks. Wireless Netw 23(6):1809–1821

    Article  Google Scholar 

  29. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Longman Publishing Co., Inc., Boston. ISBN 0201157675

    MATH  Google Scholar 

  30. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural network, vol 4, pp1942–1948

  31. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybern) 26(1):29–41

    Article  Google Scholar 

  32. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  33. Karaboga D, Akay B (2009) A comparative study of Artificial Bee Colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  34. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  35. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933

    Article  Google Scholar 

  36. Hussain S, Matin AW (2006) Hierarchical cluster-based routing in wireless sensor networks. In: IEEE/ACM international conf. on information processing in sensor networks, IPSN

  37. Tillett J, Rao R, Sahin F (2002) Cluster-head identification in ad hoc sensor networks using particle swarm optimization. In: IEEE international conference on personal wireless communications, New Delhi, India, pp 201–205

  38. Latiff N (2007) Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: IEEE 18th international symposium on personal, indoor and mobile radio communications, Athens, Greece, pp 1–5

  39. Guru SM, Halgamuge SK, Fernando S (2005) Particle swarm optimisers for cluster formation in wireless sensor networks. In: International conference on intelligent sensors, sensor networks and information processing, Melbourne, Australia, 5–8 December 2005, pp 319–324

  40. Cao X, Zhang H, Shi J, Cui G (2008) Cluster heads election analysis for multi-hop wireless sensor networks based on weighted graph and particle swarm optimization. In: 4th International conference on natural computing, vol 7, pp 599–603

  41. Kuila P, Gupta SK, Jana PK (2013) A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol Comput 12:48–56

    Article  Google Scholar 

  42. Shokouhifar M, Jalali A (2015) A new evolutionary based application specific routing protocol for clustered wireless sensor networks. Int J Electron Commun 69:432–441

    Article  Google Scholar 

  43. Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput pp 1–16

  44. Lalwani P, Das S, Banka H, Kumar C (2016) CRHS: clustering and routing in wireless sensor networks using harmony search algorithm. Neural Comput Appl pp 1–21

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, 140413, India

    Nitin Mittal & Balwinder Singh Sohi

  2. Department of Electronics and Communication Engineering, Thapar University, Patiala, Punjab, 147004, India

    Urvinder Singh

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  1. Nitin Mittal
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  2. Urvinder Singh
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  3. Balwinder Singh Sohi
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Correspondence to Nitin Mittal.

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Mittal, N., Singh, U. & Sohi, B.S. An energy-aware cluster-based stable protocol for wireless sensor networks. Neural Comput & Applic 31, 7269–7286 (2019). https://doi.org/10.1007/s00521-018-3542-x

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