Bayesian game-based user behavior analysis for spectrum mobility in cognitive radios

Iftikhar, Ayesha; Rauf, Zahid; Khan, Faisal; Ali, Mohammad Shoaib; Kakar, Malghalara

doi:10.1016/j.phycom.2018.12.002

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Li et al [ 24 ] propose a method based on deep learning for the prediction of user mobility using a preferential exploration and return model as a deep-learning technique to predict the future locations of the nodes. According to the proposal made by Iftikhar et al [ 25 ], a decision-making algorithm using game theory is presented to model spectral mobility, which serves as a switching game that considers whether to change or remain in the channel. Alozie et al’s scheme [ 26 ] presents a strategy to minimize the delay that occurs during spectrum handoff from a backup channel selection mechanism based on fuzzy data.…”

Section: Related Workmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2024

Sensors

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The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability.

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Section: Related Workmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2024

Sensors

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“…Feng Li et al [22] propose a method based on Deep Learning for the prediction of user mobility, using a Preferential Exploration and Return model as a deep learning technique in order to predict the future locations of the nodes. On the other hand, Joseph Tlouyamma and Mthulisi Velempini [23], carry out a study that focuses on the design of a channel selection algorithm, the process considers an ordering in the descending order of the probabilities of channel inactivity, thus the grouping of channels ensures that channels are detected simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Decision-Making Algorithm with Geographic Mobility for Cognitive Radios

Cervantes-Junco,

Rodriguez-Colina,

Palacios-Luengas

et al. 2023

Preprint

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According to the cognitive radio (CR) paradigm, the efficient use of the radio spectrum is achieved by performing four main functions: spectral perception, spectral mobility, decision-making, and cooperation. In this work, a multivariable algorithm that considers geographic mobility (GM) is presented. The decision-making process includes a feedback mechanism for the dynamic selection of reservation channels in a wireless network with cognitive capabilities. Geographic mobility plays a crucial role in how radio takes advantage of its environment; however, it has been scarcely explored in the literature. In the present work, geographic mobility tests were carried out involving the interaction of CR and primary users (PU), where a measurement of the latency time of the proposed algorithm called: Algorithm for Decision-Making with Geographic Mobility (ATDeMoGeo) was evaluated and compared with other algorithms such as: Dijkstra's Algorithm, Analytic Hierarchical Process (AHP), Fuzzy Analytic Hierarchical Process (FAHP) and Decision-Making Algorithm with Modified Dijkstra (ATDDiM). It was found that the proposed algorithm is robust and that it considerably reduces the latency time to accurately determine the best communication channels, thanks to the decision-making function implemented to establish which channels the CRs can occupy. For the selection of the best reserve channels, a series of criteria were considered, such as: signal-to-interference plus noise ratio (SINR), bandwidth (BW), probability of channel availability (AP), estimated channel time of availability (ETA) using the random waypoint mobility model (RWPM). These criteria are evaluated for two types of service, i.e., real time (RT) and best effort (BE). For the evaluation and validation of the ATDeMoGeo algorithm, a network with cognitive characteristics in NS-3 was simulated. The simulation scenarios consisted of a base station (BS) with mobile CRs and PUs. Based on the previous criteria, i.e., BW, SINR, AP and ETA, the BS assesses the spectrum occupancy information obtained by the CRs. The results show that the proposed algorithm can be used without problem for the proposed scenarios with higher speed and accuracy for the selection of better communication channels.

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“…Budget constraint has been emphasized by this algorithm for different levels such as operator, mobile user and mobile relay. To predict the mobility, the authors have used Bayesian game theory and Graph Theory concepts [30].…”

Section: Crn Is a Wireless Network Which Comprises Several Types Of Primary Users (Pu) Andmentioning

confidence: 99%

Optimized Channel Decision & Secure Data Transmission for Health Monitoring Using Wireless Body Area Network Through Cognitive Controller

Rajaguru¹,

Devi

Marichamy³

2021

Preprint

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Remote monitoring system has been applied in different applications such as agriculture, industrial automation, defence, telecommunication and health care. In health care applications, wireless networks get the impact with Wireless Body Area Network (WBAN). WBAN is helpful in monitoring patients’ health and it also possesses secure transmission and access control mechanism with different sensors. WBAN monitors the patient’s health and transfers the information to data pool without influencing patient’s daily routine activities. Further, the health report data are sent to the doctor over the network from the place of the patient without any data loss and delay. Due to increasing usage of wireless services, the available networks have been congested with heavy traffic which leads to miscommunication and delay. To overcome this scenario, solution has been proposed with help of Cognitive Radio Networks (CRN). Collected information are transferred to cognitive controller which acts as central node. Cognitive controller selects the channel to transfer the information with QoS as well as without any delay. Based on the input parameters, the channel selection process is optimized and it will also improve the system performance with secure transmissions. Using Fuzzy Inference System (FIS) optimizing, the channel selection process has been carried out and it also provides more accurate solution to choose the channel. For the optimization of the proposed approach Mamdani and Sugeno methods have been used. These methods yield the best results with minimum error probability of 0.9 compared to the existing methods and these methods have achieved efficiencies of 98% and 99%, respectively.

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Bayesian game-based user behavior analysis for spectrum mobility in cognitive radios

Cited by 6 publications

References 19 publications

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Decision-Making Algorithm with Geographic Mobility for Cognitive Radios

Optimized Channel Decision & Secure Data Transmission for Health Monitoring Using Wireless Body Area Network Through Cognitive Controller

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Bayesian game-based user behavior analysis for spectrum mobility in cognitive radios

Cited by 6 publications

References 19 publications

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

Decision-Making Algorithm with Geographic Mobility for Cognitive Radios

Optimized Channel Decision &amp; Secure Data Transmission for Health Monitoring Using Wireless Body Area Network Through Cognitive Controller

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Optimized Channel Decision & Secure Data Transmission for Health Monitoring Using Wireless Body Area Network Through Cognitive Controller