Design, implementation and analysis of cognitive radio enabled intelligent WBAN gateway for cost-efficient remote health monitoring

Manna, Tanumay; Misra, Iti Saha

doi:10.1016/j.phycom.2019.100713

Cited by 10 publications

(7 citation statements)

References 37 publications

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“…In addition, the document also studied the psychological data analysis of multidimensional data warehouse model of higher vocational students and designed a prototype system with VB. From the literature, its work is more systematic and comprehensive, but its biggest shortcoming is that it does not analyze the results of data mining, so there is no regular knowledge [11]. In order to improve the effectiveness of mental health intelligent evaluation, a mental health intelligent evaluation system based on a decision tree algorithm is proposed, which can effectively promote the information management of the system, solve the old problems of traditional system resources, give full play to the advantages of the Internet, create a new Internet environment, and effectively solve the psychological problems of contemporary people.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Online Intelligent Mental Health Testing Platform

Ren

Hou

Juz-he

2022

Journal of Healthcare Engineering

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In order to solve the problems of high misevaluation rate and low work efficiency in the process of mental health intelligent evaluation, a method of mental health intelligent evaluation system oriented to the decision tree algorithm is proposed. First, the current research status of mental health intelligent evaluation was analyzed and the framework of mental health intelligent evaluation system was constructed. Then, the mental health intelligent evaluation data were collected and the decision tree algorithm was used to analyze and classify the mental health intelligent evaluation data to obtain the mental health intelligent evaluation results. Finally, specific simulation experiments are used to analyze the feasibility and superiority of the mental health intelligent evaluation system. The experimental results show that the recall rate of each system increases with the increasing number of iterations, and the system has the highest recall rate. Also, it is stable after the number of iterations reaches 20, with good recall and adaptive scheduling performance. The recall rate of comparison system 1 and comparison system 2 fluctuates greatly, and the recall rate is lower than that of the system in this paper. It is proved that the method of the mental health intelligent evaluation system of the decision tree algorithm can effectively solve the problem and improve the accuracy of the mental health intelligent evaluation. The efficiency of mental health intelligent evaluation is improved, and the system stability is better, which can meet the actual requirements of current mental health intelligent evaluation.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of Online Intelligent Mental Health Testing Platform

Ren

Hou

Juz-he

2022

Journal of Healthcare Engineering

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“…The main measurement indexes include energy consumption per unit of GDP and energy production efficiency; The fourth is the value efficiency index of energy, which includes the market price factors of energy input and production output factors into the EE evaluation index. EE can be expressed as the ratio between the input cost and output value of energy [5][6].…”

Section: Single Factor Eementioning

confidence: 99%

Energy Efficiency and Energy Conservation and Emission Reduction based on Intelligent Optimization Algorithm

2021

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Under the development requirements of low-carbon economy, China has put forward specific targets and requirements for energy conservation(EC) and emission reduction(ER) for various industries. Due to the significant differences in the development of different industries between regions, accurately measuring the energy efficiency(EE) and EC and ER potential of the steel industry in various regions is an important basis for the rational allocation of EC and ER targets. Therefore, this paper proposes an intelligent optimization algorithm(IOA) to analyze the EE and EC and ER of resource-based cities. This paper briefly introduces the single factor EE and total factor EE, discusses the improvement of EE by EC and ER, and puts forward the ant colony algorithm of IOA. Taking the iron and steel industry as an example, the EC and ER and EE of iron and steel industry in different regions are analyzed. The test results show that the IOA proposed in this paper is effective and accurate, and has achieved good results.

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“…In [5], a cross‐layer verification method was designed for cloud environment. An intelligent gateway model was designed in [6] to use licensed PU channels via CR for making reliable transmission to the healthcare monitoring. The main objective of wireless network remains in the efficient utilisation of the spectrum band.…”

Section: Related Workmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.

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Design, implementation and analysis of cognitive radio enabled intelligent WBAN gateway for cost-efficient remote health monitoring

Cited by 10 publications

References 37 publications

Design and Implementation of Online Intelligent Mental Health Testing Platform

Design and Implementation of Online Intelligent Mental Health Testing Platform

Energy Efficiency and Energy Conservation and Emission Reduction based on Intelligent Optimization Algorithm

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

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