Mobile Networks and Applications

, Volume 22, Issue 5, pp 931–942 | Cite as

Lifetime Enhancement of Dynamic Heterogeneous Wireless Sensor Networks with Energy-Harvesting Sensors

  • Chun-Cheng Lin
  • Yung-Chiao Chen
  • Jiann-Liang Chen
  • Der-Jiunn DengEmail author
  • Shang-Bin Wang
  • Shun-Yu Jhong


Lifetime enhancement has been the major constraint of developing wireless sensor networks (WSNs). Most of previous related works separately considered dynamics and heterogeneity of WSNs, and did not consider energy-harvesting (EH) sensors, which can absorb natural power (e.g., solar and wind power) to extend lifetime of sensor devices. Therefore, this work investigates the problem of extending the lifetime of dynamic heterogeneous WSNs with EH sensors to enhancing the total WSN lifetime. This problem can be characterized as finding the maximal number of covers each of which is a part of all sensors so that all targets can be monitored by these sensors. Since the case for static WSNs has been shown to be NP-complete, the concerned problem is also NP-complete. Hence, this work first models this problem mathematically, and then proposes a novel harmony search algorithm with multiple populations and local search (HSAML) for this problem with dynamics, heterogeneity, and EH sensors. By simulation, the network lifetime, stability, and executing time of the proposed algorithm are analyzed. From experimental results, the proposed HSAML performs better than the conventional algorithm in terms of average network lifetime for larger-scale problems (i.e., when the number of common and EH sensors is small). In addition, the results confirm that adding EH sensors really helps extend the total WSN lifetime.


Heterogeneous wireless sensor network Energy-harvesting sensor Harmony search algorithm Dynamic optimization 



The authors thank the anonymous referees for comments that improved the content as well as the presentation of this paper. This work has been supported in part by MOST 104-2221-E-009-134-MY2.


  1. 1.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  2. 2.
    Oliveira LM, Rodrigues JJ (2011) Wireless sensor networks: a survey on environmental monitoring. J Commun 6(2):143–151CrossRefGoogle Scholar
  3. 3.
    Ko J, Lu C, Srivastava M, Stankovic J, Terzis A, Welsh M (2010) Wireless sensor networks for healthcare. Proc IEEE 98(11):1947–1960CrossRefGoogle Scholar
  4. 4.
    Aminian M, Naji HR (2013) A hospital healthcare monitoring system using wireless sensor networks. In: Proceedings of Journal of Health & Medical Informatics (JHMI 2013), pp. 1–6. doi: 10.4172/2157-7420.1000121
  5. 5.
    Awan S W, Saleem S (2016) Hierarchical clustering algorithms for heterogeneous energy harvesting wireless sensor networks. In: Proceedings of 2016 International Symposium on Wireless Communication Systems (ISWCS 2016), IEEE press, pp. 270–274Google Scholar
  6. 6.
    Yang C, Chin K W (2016) On nodes placement in energy harvesting wireless sensor networks for coverage and connectivity. IEEE T Ind Inform, 13(1):27–36Google Scholar
  7. 7.
    Slijepcevic S, Potkonjak M (2011) Power efficient organization of wireless sensor networks. In: Proceedings of IEEE International Conference on Communications (ICC 2001), pp. 472–476, IEEE pressGoogle Scholar
  8. 8.
    Garey MR, Johnson DS (1979) Computers and Intractability - A Guide to the Theory of NP-Completeness. Freeman, San FranciscoGoogle Scholar
  9. 9.
    Liao C, Ting C (2012) Extending the lifetime of dynamic wireless sensor networks by genetic algorithm. In: Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2012), pp. 1–8, IEEE pressGoogle Scholar
  10. 10.
    Cardei M, Du DZ (2005) Improving wireless sensor network lifetime through power aware organization. In: Proceedings of IEEE Wireless and Mobile Computing, Networking and Communications (WiMob 2005), pp. 333–340, IEEE pressGoogle Scholar
  11. 11.
    Nezhad SE (2010) Solving k-coverage problem in wireless sensor networks using improved harmony search. In: Proceedings of IEEE Broadband, Wireless Computing, Communication and Applications (BWCCA 2010), pp. 49–55, IEEE pressGoogle Scholar
  12. 12.
    Cardei M, Wu J, Lu M, Pervaiz M (2005) Maximum network lifetime in wireless sensor networks with adjustable sensing ranges. In: Proceedings of IEEE Wireless and Mobile Computing, Networking and Communications (WiMob 2005), pp. 438–445, IEEE pressGoogle Scholar
  13. 13.
    Liu F, Tsui C, Zhang YJ (2010) Joint routing and sleep scheduling for lifetime maximization of wireless sensor networks. IEEE Trans Wirel Commun 9(7):2258–2267CrossRefGoogle Scholar
  14. 14.
    Zhao Y, Wu J, Li F, Lu S (2012) On maximizing the lifetime of wireless sensor networks using virtual backbone scheduling. IEEE Trans Parallel Distrib Syst 23(8):1528–1535CrossRefGoogle Scholar
  15. 15.
    Sudevalayam S, Kulkarni P (2010) Energy harvesting sensor nodes: survey and implications. In: Proceedings of IEEE Communications Surveys Tutorials (CST 2010), pp. 1–19, IEEE pressGoogle Scholar
  16. 16.
    Zhang P, Xiao G, Tan H (2013) Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors. J Comput Netw 57(4):2689–2704CrossRefGoogle Scholar
  17. 17.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  18. 18.
    Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579MathSciNetzbMATHGoogle Scholar
  19. 19.
    Lin CC, Deng DJ, Wang SB (2016) Extending the lifetime of dynamic underwater acoustic sensor networks using multi-population harmony search algorithm. IEEE Sensors J 16(11):4034–4042CrossRefGoogle Scholar
  20. 20.
    Shaikh FK, Zeadally S (2016) Energy harvesting in wireless sensor networks: a comprehensive review. J Renew Sust Energ Rev 55:1041–1054CrossRefGoogle Scholar
  21. 21.
    Azevedo JAR, Santos FES (2012) Energy harvesting from wind and water for autonomous wireless sensor nodes. IET Circ, Devices Syst 6(6):413–420MathSciNetCrossRefGoogle Scholar
  22. 22.
    Kansal A, Hsu J, Zahedi S, Srivastava MB (2007) Power management in energy harvesting sensor networks. ACM Trans Embed Comput Syst 6(4):1–32CrossRefGoogle Scholar
  23. 23.
    Michelusi N, Badia L, Carli R, Corradini L, Zorzi M (2013) Energy management policies for harvesting-based wireless sensor devices with battery degradation. IEEE Trans Commun 61(12):4934–4947CrossRefGoogle Scholar
  24. 24.
    Zhang H, Hou JC (2004) Maintaining sensing coverage and connectivity in large sensor networks. In: Proceedings of International Workshop on Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Wireless and Peer-to-Peer Networks, pp. 89–124Google Scholar
  25. 25.
    Geem ZW, Kim JH (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76(2):60–68CrossRefGoogle Scholar
  26. 26.
    Castelli M, Silva S, Manzoni L, Vanneschi L (2014) Geometric selective harmony search. Inf Sci 279(20):468–482MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Karimi M, Askarzadeh A, Rezazadeh A (2012) Using tournament selection approach to improve harmony search algorithm for modeling of proton exchange membrane fuel cell. Int J Electrochem Sci 7(7):6426–6435Google Scholar
  28. 28.
    Syswerda G (1980) Uniform crossover in genetic algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms (ICGA 3rd), pp. 2–9.Google Scholar
  29. 29.
    Mehrabi A, Kim K (2016) General framework for network throughput maximization in sink-based energy harvesting wireless sensor networks. IEEE Transactions on Mobile Computing, in pressGoogle Scholar
  30. 30.
    Mehrabi A, Kim K (2016) Optimal transmission period for improved sink-based data collection in energy harvesting wireless sensor networks. In: Proccedings of IEEE international Conference on Communications (ICC 2016), pp. 1–6Google Scholar
  31. 31.
    Qi X, Wang K, Huang A (2015) A harvesting-rate oriented self-adaptive algorithm in energy-harvesting wireless body area networks. In: Proceedings of IEEE 13th International Conference on Industrial Informatics (INDIN 2015), pp. 966–971Google Scholar
  32. 32.
    Kunikawa M, Yomo H, Abe K, Ito T (2015) A fair polling scheme for energy harvesting wireless sensor networks. In: Proceedings of IEEE 81st Vehicular Technology Conference (VTC spring 2015), pp. 1–5Google Scholar
  33. 33.
    Sedighimanesh A, Sedighimanesh M, Baqeri J (2015) Improving wireless sensor network lifetime using layering in hierarchical routing. In: Proceedings of 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), IEEE press, pp. 1145–1149Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.National Chiao Tung UniversityHsinchuTaiwan
  2. 2.Department of Electrical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  3. 3.Computer and Information Networking CenterNational Taiwan UniversityTaipeiTaiwan
  4. 4.Department of Computer Science and Information EngineeringNational Changhua University of EducationChanghuaTaiwan

Personalised recommendations