An immune clone selection based power control strategy for alleviating energy hole problems in wireless sensor networks

  • Xuejian ZhaoEmail author
  • Xiaoxiao Xiong
  • Zhe Sun
  • Xinhui Zhang
  • Zhixin Sun
Original Research


In wireless sensor networks (WSNs), the creation of energy holes is extremely difficult to be avoided because the data flow usually follows a many-to-one and multi-hop pattern. Since energy holes exhaust their energy faster than other nodes, network partitions might be created, which might lead to failure of the network. Cluster-based WSNs have been widely used because of their good performance, and power control strategies are an effective way to improve energy efficiency in WSNs. In this paper, we first propose a power-based energy consumption model and a cluster-based coronal model for analyzing the energy hole problem in WSNs. Then, on the basis of the proposed models, we investigate the feasibility and effectiveness of the existing approaches for solving the energy hole problem in WSNs. Furthermore, an immune clone selection-based power control (ICSPC) strategy for alleviating the energy hole problem in WSNs is proposed. In the ICSPC strategy, the immune clone selection algorithm is used to optimize the transmission ranges of sensors in various coronas to balance the energy consumption rates of the coronas. Finally, simulation results are analyzed to show that the energy hole problem in WSNs has been largely alleviated by the ICSPC strategy, and the network lifetime is greatly prolonged.


Cluster-based coronal model Energy hole problem Immune clone selection Power control Transmission range adjustment Wireless sensor network 



This work was supported in part by the National Natural Science Foundation of China under Grant 61672299, in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20160913, in part by the China Postdoctoral Science Foundation funded project under Grant 2018M640509.


  1. Ammari HM, Das SK (2008) Promoting heterogeneity, mobility, and energy-aware voronoi diagram in wireless sensor networks. IEEE Trans Parallel Distrib Syst 19(7):995–1008CrossRefGoogle Scholar
  2. Asharioun H, Asadollahi H, Wan TC, Gharaei N (2015) A survey on analytical modeling and mitigation techniques for the energy hole problem in corona-based wireless sensor network. Wirel Pers Commun 81(1):161–187CrossRefGoogle Scholar
  3. Azad AKM, Kamruzzaman J (2011) Energy-balanced transmission policies for wireless sensor networks. IEEE Trans Mob Comput 10(7):927–940CrossRefGoogle Scholar
  4. Bandyopadhyay S, Coyle EJ (2004) Minimizing communication costs in hierarchically-clustered networks of wireless sensors. Comput Netw 44(1):1–16CrossRefGoogle Scholar
  5. Bi Y, Sun L, Ma J, Li N, Khan IA, Chen C (2007) HUMS: an autonomous moving strategy for mobile sinks in data-gathering sensor networks. EURASIP J Wirel Commun Netw 2007(1):1–15CrossRefGoogle Scholar
  6. Brazil MN, Ras CJ, Thomas DA (2013) Relay augmentation for lifetime extension of wireless sensor networks. IET Wirel Sens Syst 3(2):145–152CrossRefGoogle Scholar
  7. Çam H, Özdemir S, Nair P, Muthuavinashiappan D, Sanli HO (2006) Energy-efficient secure pattern based data aggregation for wireless sensor networks. Comput Commun 29(4):446–455CrossRefGoogle Scholar
  8. Chen Y, Li Q, Fei L, Gao Q (2012) Mitigating energy holes in wireless sensor networks using cooperative communication. In: 2012 IEEE 23rd international symposium on personal, indoor and mobile radio communications, Sydney, NSW, Australia, 9–12 September 2012, pp 857–862Google Scholar
  9. Esseghir M, Bouabdallah N, Pujolle G (2007) Energy provisioning model for maximizing wireless sensor network lifetime. In: 2007 First international global information infrastructure symposium, Marrakech, Morocco, 2–6 July 2007, pp 80–84Google Scholar
  10. Fei L, Chen Y, Gao Q, Peng XH, Li Q (2015) Energy hole mitigation through cooperative transmission in wireless sensor networks. Int J Distrib Sens Netw 11(2):1–14CrossRefGoogle Scholar
  11. Ferng HW, Hadiputro MS, Kurniawan A (2010) Design of novel node distribution strategies in corona-based wireless sensor networks. IEEE Trans Mob Comput 10(9):1297–1311CrossRefGoogle Scholar
  12. Halder S, Ghosal A, Bit SD (2011) A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network. Comput Commun 34(11):1294–1306CrossRefGoogle Scholar
  13. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRefGoogle Scholar
  14. Hou YT, Shi Y, Sherali HD, Midkiff SF (2005) On energy provisioning and relay node placement for wireless sensor networks. IEEE Trans Wirel Commun 4(5):2579–2590CrossRefGoogle Scholar
  15. Jan N, Javaid N, Javaid Q, Alrajeh N, Alam M, Khan ZA, Niaz IA (2017) A balanced energy-consuming and hole-alleviating algorithm for wireless sensor networks. IEEE Access 5:6134–6150CrossRefGoogle Scholar
  16. Khan A, Ahmedy I, Anisi MH, Javaid N, Ali I, Khan N, Alsaqer M, Mahmood H (2018) A localization-free interference and energy holes minimization routing for underwater wireless sensor networks. Sensors 18(1):1–17CrossRefGoogle Scholar
  17. Li J, Mohapatra P (2005) An analytical model for the energy hole problem in many-to-one sensor networks. In: 2005 IEEE 62nd vehicular technology conference, Dallas, TX, USA, 28 September 2005, pp 2721–2725Google Scholar
  18. Li J, Mohapatra P (2007) Analytical modeling and mitigation techniques for the energy hole problem in sensor networks. Pervasive Mob Comput 3(3):233–254CrossRefGoogle Scholar
  19. Lian J, Chen L, Naik K, Agnew GB (2004) Modeling and enhancing the data capacity of wireless sensor networks. IEEE Monogr Sens Netw Oper 2:91–138Google Scholar
  20. Lian J, Naik K, Agnew GB (2006) Data capacity improvement of wireless sensor networks using non-uniform sensor distribution. Int J Distrib Sens Netw 2(2):121–145CrossRefGoogle Scholar
  21. Liu T (2013) Avoiding energy holes to maximize network lifetime in gradient sinking sensor networks. Wirel Pers Commun 70(2):581–600CrossRefGoogle Scholar
  22. Liu X (2016) A novel transmission range adjustment strategy for energy hole avoiding in wireless sensor networks. J Netw Comput Appl 67:43–52CrossRefGoogle Scholar
  23. Liu Y, Ngan H, Ni LM (2006) Power-aware node deployment in wireless sensor networks. Int J Distrib Sens Netw 3(2):225–241CrossRefGoogle Scholar
  24. Liu J, Lu K, Cai X, Murthi MN (2009) Regenerative cooperative diversity with path selection and equal power consumption in wireless networks. IEEE Trans Wirel Commun 8(8):3926–3932CrossRefGoogle Scholar
  25. Marta M, Cardei M (2008) Using sink mobility to increase wireless sensor networks lifetime. In: 2008 International symposium on a world of wireless, mobile and multimedia networks, Newport Beach, CA, USA, 23–26 June 2008, pp 1–10Google Scholar
  26. Mohemed RE, Saleh AI, Abdelrazzak M, Samra AS (2017) Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks. Comput Netw 114:51–66CrossRefGoogle Scholar
  27. Morteza S, Mortaza A (2018) Proposing a method to solve energy hole problem in wireless sensor networks. Alex Eng J 57(3):1585–1590CrossRefGoogle Scholar
  28. Nguyen KV, Nguyen PL, Vu QH, Do TV (2017a) An energy efficient and load balanced distributed routing scheme for wireless sensor networks with holes. J Syst Softw 123(1):92–105CrossRefGoogle Scholar
  29. Nguyen PL, Ji Y, Liu Z, Vu H, Nguyen KV (2017b) Distributed hole-bypassing protocol in WSNs with constant stretch and load balancing. Comput Netw 129:232–250CrossRefGoogle Scholar
  30. Potdar V, Sharif A, Chang E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  31. Prabha KL, Selvan S (2018) Energy efficient energy hole repelling (EEEHR) algorithm for delay tolerant wireless sensor network. Wirel Pers Commun 101(3):1395–1409CrossRefGoogle Scholar
  32. Rahman AU, Hasbullah H, Sama N (2012) Impact of Gaussian deployment strategies on the performance of wireless sensor network. In: International conference on computer & information science (ICCIS), Kuala Lumpur, Malaysia, 12–14 June 2012, pp 771–776Google Scholar
  33. Ren J, Zhang Y, Zhang K, Liu A, Chen J, Shen XS (2016) Lifetime and energy hole evolution analysis in data-gathering wireless sensor networks. IEEE Trans Ind Inform 12(2):788–800CrossRefGoogle Scholar
  34. Song C, Liu M, Cao J, Zheng Y, Gong H, Chen G (2009) Maximizing network lifetime based on transmission range adjustment in wireless sensor networks. Comput Commun 32(11):1316–1325CrossRefGoogle Scholar
  35. Suganthi K, Sundaram VB (2012) A constraint based relay node deployment in heterogeneous wireless sensor networks for lifetime maximization. In: 2012 fourth international conference on advanced computing (ICoAC), Chennai, India, 13–15 December 2012, pp 1–6Google Scholar
  36. Thanigaivelu K, Murugan K (2012) K-level based transmission range scheme to alleviate energy hole problem in WSN. In: Proceedings of the second international conference on computational science, engineering and information technology, Coimbatore UNK, India, 26–28 October 2012, pp 476–483Google Scholar
  37. Wang Q, Xu K, Takahara G, Hassanein H (2006) On lifetime-oriented device provisioning in heterogeneous wireless sensor networks: approaches and challenges. IEEE Netw 20(3):26–33CrossRefGoogle Scholar
  38. Wang K, Shao Y, Shu L, Han G, Zhu C (2015) LDPA: a local data processing architecture in ambient assisted living communications. IEEE Commun Mag 53(1):56–63CrossRefGoogle Scholar
  39. Wang K, Gao H, Xu X, Jiang J, Yue D (2016a) An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks. IEEE Sens J 16(11):4051–4062CrossRefGoogle Scholar
  40. Wang K, Shao Y, Shu L, Zhu C, Zhang Y (2016b) Mobile big data fault-tolerant processing for ehealth networks. IEEE Netw 30(1):36–42CrossRefGoogle Scholar
  41. Wang K, Wang Y, Sun Y, Guo S, Wu J (2016c) Green industrial internet of things architecture: an energy-efficient perspective. IEEE Commun Mag 54(12):48–54CrossRefGoogle Scholar
  42. Wang K, Gu L, He X, Guo S, Sun Y, Vinel A, Shen J (2017) Distributed energy management for vehicle-to-grid networks. IEEE Netw 31(2):22–28CrossRefGoogle Scholar
  43. Wu X, Chen G, Das SK (2006) On the energy hole problem of nonuniform node distribution in wireless sensor networks. In: 2006 IEEE international conference on mobile ad hoc and sensor systems, Vancouver, BC, Canada, 9–12 October 2006, pp 180–187Google Scholar
  44. Wu X, Chen G, Das SK (2007) Avoiding energy holes in wireless sensor networks with nonuniform node distribution. IEEE Trans Parallel Distrib Syst 19(5):710–720Google Scholar
  45. Xia C, Guan N, Deng Q, Yi W (2014) Maximizing lifetime of three-dimensional corona-based wireless sensor networks. Int J Distrib Sens Netw 10(5):1–13CrossRefGoogle Scholar
  46. Xu K, Hassanein H, Takahara G, Wang Q (2010) Relay node deployment strategies in heterogeneous wireless sensor networks. IEEE Trans Mob Comput 9(2):145–159CrossRefGoogle Scholar
  47. Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379CrossRefGoogle Scholar
  48. Zhang J, Ci S, Sharif H, Alahmad M (2009) A battery-aware deployment scheme for cooperative wireless sensor networks. In: 2009 IEEE global telecommunications conference, Honolulu, HI, USA, 30 November–4 December 2009, pp 1–5Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Broadband Wireless Communication and Sensor Network Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Research and Development Center of Postal Industry TechnologyNanjing University of Posts and TelecommunicationsNanjingChina
  3. 3.School of Modern PostsNanjing University of Posts and TelecommunicationsNanjingChina

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