Reduced-Complexity Selective Mapping for Improving Wireless Communication in Smart Irrigation Systems

Hassan, Emad S.

doi:10.1007/s11277-023-10251-4

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transmission between the sensor nodes and the CH within the cluster is carried out using the unlicensed spectrum, while the transmission between the CH and the sink node takes place opportunistically through the licensed spectrum. The licensed spectrum that is being targeted includes M radio channels that exhibit varying PU communications and signal-to-noise-ratios (SNRs) [27].…”

Section: Cr-wsn System Modelmentioning

confidence: 99%

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

Hassan

2023

Agriculture

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Cr-wsn System Modelmentioning

confidence: 99%