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A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols

Author

Listed:
  • Gaddafi Abdul-Salaam

    (Universiti Teknologi Malaysia (UTM)
    Kwame Nkrumah University of Science & Technology)

  • Abdul Hanan Abdullah

    (Universiti Teknologi Malaysia (UTM))

  • Mohammad Hossein Anisi

    (University of Malaya)

  • Abdullah Gani

    (University of Malaya)

  • Abdulhameed Alelaiwi

    (King Saud University)

Abstract

Wireless sensor network (WSN) has become part of human life as it is used in several applications including healthcare, environment and agricultural, public safety, military, transportation as well as in the industry. In spite of its usefulness, it is challenging to maintain long-term operations due to limited battery life. Several energy efficient protocols have been designed to prolong the network lifetime. The integration of mobility technology with the conventional static sensor network, described as hybrid WSN, promises a new solution that balances energy consumption among sensor nodes and extends the network lifetime. To the best of our knowledge, there has not been as yet an evaluation of the energy-efficiency of the data collection approaches in terms of the energy conservation techniques adopted. In this paper, the architecture of data collection approaches in WSN is discussed. Then, we propose and discuss a taxonomy of types of data collection in WSN. We further present and discuss in details a thematic taxonomy of energy conservation techniques adopted in the various hybrid WSN data collection approaches. Consequently, we compare the different energy conservation approaches that minimize energy consumption in hybrid WSN, highlighting their pros and cons. In conclusion, we point out open research challenges and future directions in the field.

Suggested Citation

  • Gaddafi Abdul-Salaam & Abdul Hanan Abdullah & Mohammad Hossein Anisi & Abdullah Gani & Abdulhameed Alelaiwi, 2016. "A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 61(1), pages 159-179, January.
    • Handle: RePEc:spr:telsys:v:61:y:2016:i:1:d:10.1007_s11235-015-0092-8
    DOI: 10.1007/s11235-015-0092-8
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    References listed on IDEAS

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    1. Yifan Hu & Yongsheng Ding & Kuangrong Hao & Lihong Ren & Hua Han, 2014. "An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 337-350.
    2. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    3. Akhtar, Fayaz & Rehmani, Mubashir Husain, 2015. "Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 769-784.
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    Cited by:

    1. Babayo, Aliyu Aliyu & Anisi, Mohammad Hossein & Ali, Ihsan, 2017. "A Review on energy management schemes in energy harvesting wireless sensor networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1176-1184.
    2. Alireza Abdollahi & Karim Rejeb & Abderahman Rejeb & Mohamed M. Mostafa & Suhaiza Zailani, 2021. "Wireless Sensor Networks in Agriculture: Insights from Bibliometric Analysis," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    3. Houriya Hojjatinia & Mohsen Jahanshahi & Saeedreza Shehnepoor, 2021. "Improving lifetime of wireless sensor networks based on nodes’ distribution using Gaussian mixture model in multi-mobile sink approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 255-268, May.
    4. Damianos Gavalas & Ioannis E. Venetis & Charalampos Konstantopoulos & Grammati Pantziou, 2016. "Energy-efficient multiple itinerary planning for mobile agents-based data aggregation in WSNs," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 63(4), pages 531-545, December.
    5. M. Gholipour & A. T. Haghighat & M. R. Meybodi, 2018. "Congestion avoidance in cognitive wireless sensor networks using TOPSIS and response surface methodology," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(3), pages 519-537, March.
    6. Sofiane Hamrioui & Pascal Lorenz & Jaime Lloret, 2018. "Self-organizing technique for improving coverage in connected mobile objects networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(2), pages 179-193, February.
    7. Sunil Kr. Jha & Egbe Michael Eyong, 2018. "An energy optimization in wireless sensor networks by using genetic algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(1), pages 113-121, January.

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