Energy-efficient sensor selection for multi-channel cooperative spectrum sensing based on game theory

Bagheri, Asma; Ebrahimzadeh, Ataollah; Najimi, Maryam

doi:10.1007/s12652-020-02651-2

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…in which n SNR is the received signal-to-noise-ratio from the under-sensed channel in the sensor n due to reported data by Bagheri et al (20). According to a similar process, false alarm probability is defined by:…”

Section: Energy Harvestingmentioning

confidence: 99%

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Al-bosham,

Ebrahimzadeh,

Bagheri

2024

IJE

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5G communication technology supports the Internet of Things, remote health care centers, and cloud computing by tuning their communication services over a very wide range of frequency bands with lowcost, low-battery consumption, and low latency. However, the development of such wireless technology is highly dependent on radio frequency spectra. The Cognitive Radio Sensor Network (CRSN) is an excellent candidate to improve radio spectrum utilization and manage the heavy communication data traffic in 5G wireless networks. CRSN can sense the frequency channels, making it possible for secondary users (who are denied service) to use the free channels. Despite the outstanding features of CRSNs, some limitations overshadow their performance. The most critical limitation is energy and its optimal consumption to increase the network's lifetime. Recent research has shown that energy harvesting can be an effective way to increase the lifetime of CRSNs. However, the sensors should sense the frequency spectrum with a high success rate. In this paper, several optimal sensor nodes using energy harvesting with the approach of increasing the network's lifetime are proposed to solve the mentioned challenge. This way, the sensor nodes are divided into two independent groups for simultaneous spectrum sensing and energy harvesting in each time frame. We will solve this problem based on mathematical optimization and the use of proposed solutions for convex problems. Finally, simulations are developed to evaluate the ability of the proposed solution, assuming the systems use IEEE802.15.4/Zigbee and IEEE802.11af.

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Section: Energy Harvestingmentioning

confidence: 99%

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Al-bosham,

Ebrahimzadeh,

Bagheri

2024

IJE

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“…The discrete quality factors aware channel scheduling (DQFACS) method was researched in [14] to lower the recurrent transmission in a cognitive radio ad-hoc network (CRAN), but QoS-related metrics were not considered when scheduling the channels. A game theory-based algorithm was considered in [15] to assign sensors to diverse channels in order to minimize energy consumption by satisfying a few channels sensing-related constraints. Unfortunately, the throughput was lower.…”

Section: Related Workmentioning

confidence: 99%

Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs

Devi¹,

Maloj²

2022

JTIT

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Cognitive radio networks (CRNs) allow their users to achieve adequate QoS while communicating. The major concern related to CRN is linked to guaranteeing free channel selection to secondary users (SUs) in order to maintain the network’s throughput. Many techniques have been designed in the literature for channel selection in CRNs, but the throughput of the network has not been enhanced yet. Here, an efficient technique, known as multicriteria oppositional-learnt dragonfly resource optimized QoS-driven channel selection (MOLDRO-QoSDCS) is proposed to select the best available channel with the expected QoS metrics. The MOLDRO-QoSDCS technique is designed to improve energy efficiency and throughput, simultaneously reducing the sensing time. By relying on oppositional-learnt multiobjective dragonfly optimization, the optimal available channel is selected depending on signal-to-noise ratio, power consumption, and spectrum utilization. In the optimization process, the population of the available channels is initialized. Then, using multiple criteria, the fitness function is determined and the available channel with the best resource availability is selected. Using the selected optimal channel, data transmission is effectively performed to increase the network’s throughput and to minimize the sensing time. The simulated outputs obtained with the use of Matlab are compared with conventional algorithms in order to verify the performance of the solution. The MOLDRO-QoSDCS technique performs better than other methods in terms of throughput, sensing time, and energy efficiency.

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“…That means that the objective is taken to be energy efficiency and the parameter that is optimized is number of sensing nodes in a cooperative sensing environment. The second type of optimization problem attempts to maximize the throughput while maintaining the interference level from PU and other parameters at an acceptable level (Bagheri et al 2020 ). The Gaussian model finds recognition in parameter optimization problems to attain minimum error probability.…”

Section: Literature Reviewmentioning

confidence: 99%

An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime

Mahendru

Shukla

Patnaik

2021

J Ambient Intell Human Comput

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Energy-efficient sensor selection for multi-channel cooperative spectrum sensing based on game theory

Cited by 6 publications

References 27 publications

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Optimal Node Selection for Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks with Energy Harvesting

Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs

An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime

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