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Energy harvesting in wireless sensor networks: A comprehensive review

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  • Shaikh, Faisal Karim
  • Zeadally, Sherali

Abstract

Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Suggested Citation

  • Shaikh, Faisal Karim & Zeadally, Sherali, 2016. "Energy harvesting in wireless sensor networks: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1041-1054.
    • Handle: RePEc:eee:rensus:v:55:y:2016:i:c:p:1041-1054
    DOI: 10.1016/j.rser.2015.11.010
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    1. Song, Malin & Zhang, Jie & Wang, Shuhong, 2015. "Review of the network environmental efficiencies of listed petroleum enterprises in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 65-71.
    2. A. Helme-Guizon & M.-L. Gavard-Perret & D. Gotteland, 2008. "Collecter les données : l'enquête," Post-Print halshs-00325141, HAL.
    3. Kusiak, Andrew & Li, Mingyang & Zheng, Haiyang, 2010. "Virtual models of indoor-air-quality sensors," Applied Energy, Elsevier, vol. 87(6), pages 2087-2094, June.
    4. Constantin - Eugen CORNEL, 2015. "New Techniques For Implementing Online Virtual Laboratories," Internal Auditing and Risk Management, Athenaeum University of Bucharest, vol. 39(1), pages 55-64, September.
    5. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    6. Harb, Adnan, 2011. "Energy harvesting: State-of-the-art," Renewable Energy, Elsevier, vol. 36(10), pages 2641-2654.
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