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Energy-Efficient Resource Allocation Algorithm for CR-WSN-Based Smart Irrigation System under Realistic Scenarios

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  • Emad S. Hassan

    (Department of Electrical Engineering, College of Engineering, Jazan University, Jizan 45142, Saudi Arabia
    Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt)

Abstract

Cognitive radio wireless sensor networks (CR-WSNs) are a type of WSNs that use cognitive radio technology to enhance the spectrum utilization and energy efficiency. This paper proposes an energy-efficient resource allocation algorithm (EERAA) to prolong the lifetime of a WSN-based smart irrigation system under realistic scenarios. In the proposed algorithm, power allocation and subcarrier assignment are performed consecutively. Considering the impact of the intercarrier interference (ICI) caused by timing offset, the problem of maximizing network-averaged capacity is formulated considering power and interference constraints in realistic scenarios. The obtained results reveal that the proposed algorithm attempts to maximize the averaged capacity of the CR-WSN subject to the total power constraint and tolerable interference. Numerically, the proposed algorithm can reduce the network energy consumption by up to 30%, compared with conventional approaches, while maintaining a high level of system performance in terms of secondary users’ (SUs) averaged capacity.

Suggested Citation

  • Emad S. Hassan, 2023. "Energy-Efficient Resource Allocation Algorithm for CR-WSN-Based Smart Irrigation System under Realistic Scenarios," Agriculture, MDPI, vol. 13(6), pages 1-13, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1149-:d:1159153
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    References listed on IDEAS

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    1. Mohammed Al-Medhwahi & Fazirulhisyam Hashim & Borhanuddin Mohd Ali & A Sali & Abdulsalam Alkholidi, 2019. "Resource allocation in heterogeneous cognitive radio sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.
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