Joint Energy and Throughput Optimization in Energy Harvesting Cognitive Sensor Networks

Sharifi, Morteza; Feghhi, Mahmood Mohassel

doi:10.1109/icee52715.2021.9544156

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…To improve the sensing accuracy, cooperative spectrum sensing (CSS) has been studied in CRIoT networks with SWIPT. Sharifi et al [16] proposed a power and spectrum allocation scheme to maximize reachable data rate and minimize energy consumption in an EH-CRN. SUs cooperatively sensed the spectrum by using an energy detection method and adopted PS scheme in the SWIPT process.…”

Section: Related Workmentioning

confidence: 99%

Multi-Dimensional Resource Allocation for Throughput Maximization in CRIoT with SWIPT

Jiang

2023

Energies

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To solve the power supply problem of battery-limited Internet of Things devices (IoDs) and the spectrum scarcity problem, simultaneous wireless information and power transfer (SWIPT) and cognitive radio(CR) technology were integrated into the Internet of Things (IoT) network to build a cognitive radio IoT (CRIoT) with SWIPT. In this network, secondary users (SUs) could adaptively switch between spectrum sensing, SWIPT, and information transmission to improve the total throughput. To solve the complicated multi-dimensional resource allocation problem in CRIoT with SWIPT, we propose a multi-dimensional resource allocation algorithm for maximizing the total throughput. Three-dimensional resources were jointly optimized, which are time resource (the duration of each process), power resource (the transmit power and the power splitting ratio of each node), and spectrum resource, under some constraints, such as maximum transmit power constraint and maximum permissible interference constraint. To solve this intractable mixed-integer nonlinear program (MINLP) problem, firstly, the sensing task assignment for cooperative spectrum sensing (CSS) was obtained by using a greedy sensing algorithm. Secondly, the original problem was transformed into a convex problem via some transformations with fixed-power splitting ratio and time switching. The Lagrange dual method and subgradient method were adopted to obtain the optimal power and channel allocation. Then, a one-dimensional search algorithm was used to obtain the optimal power splitting ratio and the time switching ratio. Finally, a heuristic algorithm was adopted to obtain the optimal sensing duration. The simulation results show that the proposed algorithm can achieve higher total system throughput than other benchmark algorithms, such as a greedy algorithm, an average algorithm, and the Kuhn–Munkres (KM) algorithm.

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Section: Related Workmentioning

confidence: 99%

Multi-Dimensional Resource Allocation for Throughput Maximization in CRIoT with SWIPT

Jiang

2023

Energies

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“…If the detection performance constraints are maintained, the selection is stopped otherwise, another candidate sensor with the higher priority is selected. In the second step, the cost function of sensors are calculated according to (18). On the other hand, the node with the highest priority is selected for data transmission.…”

Section: -Proposed Algorithmmentioning

confidence: 99%

“…In this paper, a joint sub channel, power and leasing time allocation algorithm is proposed to maximize the network throughput with constraints on the energy harvesting and transmission outage probability. In [18], the optimization problem of power allocation and spectrum access for maximizing the achievable data rate and minimizing the energy consumption at the secondary network, is formulated and solved using Dinkelbach algorithm. In [19], an energy efficient algorithm applies the gravitational search method to determine the optimal number of clusters and cluster heads.…”

Section: -Introductionmentioning

confidence: 99%

Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks

Najimi

2023

jist

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Due to the limitations of the natural frequency spectrum, dynamic frequency allocation is required for wireless networks. Spectrum sensing of a radio channel is a technique to identify the spectrum holes. In this paper, we investigate a dynamic cognitive sensor network, in which the cognitive sensor transmitter has the capability of the energy harvesting. In the first slot, the cognitive sensor transmitter participates in spectrum sensing and in the existence of the primary user, it harvests the energy from the primary signal, otherwise the sensor transmitter sends its signal to the corresponding receiver while in the second slot, using the decode-and-forward (DF) protocol, a part of the bandwidth is used to forward the signal of the primary user and the remained bandwidth is used for transmission of the cognitive sensor. Therefore, our purposed algorithm is to maximize the cognitive network transmission rate by selection of the suitable cognitive sensor transmitters subject to the rate of the primary transmission and energy consumption of the cognitive sensors according to the mobility model of the cognitive sensors in the dynamic network. Simulation results illustrate the effectiveness of the proposed algorithm in performance improvement of the network as well as reducing the energy consumption.

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Joint Energy and Throughput Optimization in Energy Harvesting Cognitive Sensor Networks

Cited by 2 publications

References 17 publications

Multi-Dimensional Resource Allocation for Throughput Maximization in CRIoT with SWIPT

Multi-Dimensional Resource Allocation for Throughput Maximization in CRIoT with SWIPT

Joint Cooperative Spectrum Sensing and Resource Allocation in Dynamic Wireless Energy Harvesting Enabled Cognitive Sensor Networks

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