Cognitive Radio-Enabled Internet of Things (CR-IoT): An Integrated Approach towards Smarter World

Das, Kuntala; Banerjee, Jyoti Sekhar

doi:10.1201/9781003269793-56

Cited by 6 publications

(2 citation statements)

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“…The execution of the algorithm was appraised using the robustness and spectrum efficiency. In [31], authors reviewed the current topics used for the best channel selection in a cognitive radio networks. The initial method used for channel selection was the dynamic channel access in which various strategies were categorized for ideal channel selection.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Kumar,

Sharanya

2023

ijmst

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During the last decade, Cognitive Radio Network (CRN) technology has been a significant advance in addressing the ever-increasing spectrum demand. As the number of licensed and unlicensed users in a network rises to complete a certain activity, the information exchange between various types of traffic becomes more complex and difficult. Congestion in CRN is also caused by the conflict among several users for channel access (PUs and SUs). In a very crowded network, many applications perform badly owing to packet collisions and, as a result, packet loss before significant buffer queue building. This circumstance is aggravated by an increase in network users. Congestion control is a vital and essential aspect of the present research issue in communication networks. Several recent reviews in the literature indicate that the congestion issue in the CRN has not been thoroughly studied. Thus, effective and efficient congestion control strategies are sought to optimize network resource usage and management. To prevent congestion, it is crucial for CRN to do research on the creation of an efficient congestion management system. This will improve the network's resource consumption and performance. "Performance improvement via efficient spectrum management through optimum resource management and congestion control in the CRN by mitigating different threats" is the primary target of this project. This research also focused on enhancing performance by addressing security issues in an IoT-based CRN environment. This study provides a comprehensive review of several similar studies and their limitations, which may be used to formulate a new research target.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Kumar,

Sharanya

2023

ijmst

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“…The Brain Computer Interface (BCI) is considered as one of the latest paradigms in order to provide an interaction among individuals and intelligent devices using a direct pathway among brain and external devices. In addition, the recent research tells that the BCI have witnessed the translation of brain thinking into physical actions such as IoT enabled appliances which brings the BCI a major aiding technology in human interaction systems [5], [6]. Though BCI considered as a cognitive interactive facility having several benefits and advantages of controlling the human brains and interact the brain signals through intelligent devices.…”

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confidence: 99%

An Accurate and Inter-Operatable Fuzzy-Based System Using Genetic and Canonical Correlation Analysis Methods in Internet-of-Brain Things

Rathee

Kerrache

Bilal

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The brain computer interface is defined as the way of acquiring the brain signals that analyse and translates them into commands that are relayed to intelligent devices for carrying out various actions. Through number of BCI mechanism and approaches have been proposed by various scientists to empower the individuals for directly controlling their objects via their thoughts. However, the actual implementation and realization of this method faces number of challenging with low accuracy and less interoperability. In addition, the pre-processing signals and feature extraction process is further time consuming and less accurate. In order to overcome the mentioned issues, this paper proposes an accurate and highly inter-operable system using genetic fuzzy system along. The predictive model and analysis can be further improved using canonical correlation analysis. The proposed framework is validated and demonstrated using brain typing system analysis. The results are computed against accuracy, latency and interoperability of the signals received from brain with less SNR along with traditional method. The proposed mechanism shows approximately 87% improvement as compare to existing approaches during the simulation over various performance metrics.

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