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Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications

Author

Listed:
  • Mahdi Zareei

    (Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Monterrey 64849, Mexico)

  • Cesar Vargas-Rosales

    (Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Monterrey 64849, Mexico)

  • Mohammad Hossein Anisi

    (School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK)

  • Leila Musavian

    (School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK)

  • Rafaela Villalpando-Hernandez

    (Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Monterrey 64849, Mexico)

  • Shidrokh Goudarzi

    (Advanced Informatics School, Universiti Teknologi Malaysia Kuala Lumpur (UTM), Jalan Semarak, Kuala Lumpur 54100, Malaysia)

  • Ehab Mahmoud Mohamed

    (Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addwasir 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count.

Suggested Citation

  • Mahdi Zareei & Cesar Vargas-Rosales & Mohammad Hossein Anisi & Leila Musavian & Rafaela Villalpando-Hernandez & Shidrokh Goudarzi & Ehab Mahmoud Mohamed, 2019. "Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications," Energies, MDPI, vol. 12(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2794-:d:250112
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    References listed on IDEAS

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    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. Wei, Chongfeng & Jing, Xingjian, 2017. "A comprehensive review on vibration energy harvesting: Modelling and realization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1-18.
    3. Jamal Uddin & Rozaida Ghazali & Mustafa Mat Deris, 2017. "An Empirical Analysis of Rough Set Categorical Clustering Techniques," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    4. Dehghani-Sanij, A.R. & Tharumalingam, E. & Dusseault, M.B. & Fraser, R., 2019. "Study of energy storage systems and environmental challenges of batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 192-208.
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    Cited by:

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    3. Ezekiel Darlington Nwalike & Khalifa Aliyu Ibrahim & Fergus Crawley & Qing Qin & Patrick Luk & Zhenhua Luo, 2023. "Harnessing Energy for Wearables: A Review of Radio Frequency Energy Harvesting Technologies," Energies, MDPI, vol. 16(15), pages 1-26, July.
    4. Chang-Wu Yu, 2021. "Wireless Rechargeable Sensor Networks," Energies, MDPI, vol. 14(23), pages 1-3, November.

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