A decision model for selecting best reliable relay queue for cooperative relaying in cooperative cognitive radio networks: the extent analysis based fuzzy AHP solution

Banerjee, Jyoti Sekhar; Chakraborty, Arpita; Chattopadhyay, Abir

doi:10.1007/s11276-021-02597-z

Cited by 16 publications

(5 citation statements)

References 71 publications

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“…As a result of which no proper dataset is available to use as a reference. A synthetic multicriterion ( Banerjee, 2014 , Banerjee, 2015 , Banerjee, 2021 , Banerjee & Chakraborty, 2014 , Banerjee et al, 2017 , Banerjee et al, 2018b , Banerjee et al, 2018c , Banerjee et al, 2018a , Banerjee, Chakraborty, & Karmakar, 2013 , Banerjee & Karmakar, 2012 , Saha et al, 2019 , Saha et al, 2017 ) dataset has been coined by the authors to serve the purpose of this chapter, which is mentioned in the appendix ( Table A1 ). The attributes or symptoms and conditions that have been taken into consideration include age, body temperature, dry cough, chest pain, breathing rate, hypertension, cardiovascular diseases, diabetes, tiredness, current smoker, and contact with a person with fever or cold in the last few days.…”

Section: Proposed Covid-19 Detection Methodologymentioning

confidence: 99%

A new approach to predict COVID-19 using artificial neural networks

Guhathakurata¹,

Saha²,

Kundu

et al. 2022

Cyber-Physical Systems

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Section: Proposed Covid-19 Detection Methodologymentioning

confidence: 99%

A new approach to predict COVID-19 using artificial neural networks

Guhathakurata¹,

Saha²,

Kundu

et al. 2022

Cyber-Physical Systems

Self Cite

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“…To incorporate intelligence without increasing the computational load, we find AHP as a suitable counterpart. [23][24][25] There are several cross-layer optimizations in the literature that consider optimizing another layer's parameters to achieve desired QoS performance. One of the first cross-layer methodologies was MATTEMPT, 17 followed by several recent works.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…The modern learning‐based and predictive methods are best suited for Software Defined Network–based architectures. To incorporate intelligence without increasing the computational load, we find AHP as a suitable counterpart 23–25 …”

Section: Related Work and Motivationmentioning

confidence: 99%

Joint Power and Temperature Aware Routing for implant wireless body area networks

2023

Int J Communication

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In healthcare IoT, there is a vogueing need for better performing monitoring and reporting services of both medical and non-medical applications. Off late, the solutions are stemming from wireless body area networks (WBAN). Keen, fast, and reliable management of clinical information at every level of the network opens room for researchers to work on the network level of WBAN. Being in the era of Implant Medical Devices, the challenges are versatile.Obtaining the desired throughput, combating node heating, and improvement in energy efficiency are a few challenges. Interestingly, the heterogenous nature of nodes in a WBAN thrusts the next hop selection at the intra-WBAN level advancing into a non-persistent hard problem. To this, we present a Joint Power and Temperature Aware Routing (JPTAR) Scheme with two elements.First is an analytic hierarchy process-based next hop selection method that accommodates, link quality, residual energy, closeness to sink, and node heating for the process. To this an imperceptibly, low computation algorithm for received signal strength indicator (RSSI) estimation-based transmission power control is also added to ensure an optimized energy expenditure. Requisite elements of WBAN network management like QoS goals of emergency data and redundancy removal in transmissions are inherently part of the proposed work. The performance metrics like network lifetime, node temperature rise, throughput, end-to-end delay, and computation time are acquired under the worst-case scenario. The obtained results depict that the proposed JPTAR scheme outperforms state-of-the-art reactive multi-hop routing algorithms (MATTEMPT and SIMPLE).

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“…In a collaborative cognitive radio network (CCRN), authorized and unauthorized users are able to share the same frequency band, i.e., unauthorized users gain access and use spectrum by assisting authorized users to achieve spectrum sharing during forwarding data [8]. For collaborative relay user selection, it is an important issue to choose the appropriate relay node to collaborate with authorized users because of the diferences in geographic location and channel fading parameters of each relay node and data transmission capabilities [9]. Since each user can be seen as a rational and selfsh individual that will compete with each other for spectrum, game theory can efectively analyze and solve problems such as spectrum allocation and sharing in cognitive wireless networks owing to its ability to efectively solve the adversarial and selection problems of decision-makers [10].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

Zhang¹

2023

Journal of Electrical and Computer Engineering

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In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α = α ∗ , the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.

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A decision model for selecting best reliable relay queue for cooperative relaying in cooperative cognitive radio networks: the extent analysis based fuzzy AHP solution

Cited by 16 publications

References 71 publications

A new approach to predict COVID-19 using artificial neural networks

A new approach to predict COVID-19 using artificial neural networks

Joint Power and Temperature Aware Routing for implant wireless body area networks

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

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