A trust-based fuzzy neural network for smart data fusion in internet of things

Kumar, M. Sunil; Kallam, Suresh; Al‐Turjman, Fadi; Patan, Rizwan

doi:10.1016/j.compeleceng.2020.106901

Cited by 34 publications

(7 citation statements)

References 18 publications

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“…Malchi et al 68 have introduced a fuzzy and trust‐based neural networks approach that enables three algorithms to maintain a trade‐off between adaptive coverage fraction, delay time, and energy consumption, which are all proportionally influencing the storage capacity. First, a probabilistic sensing model is used to estimate the coverage percentage of each IoT sensor.…”

Section: Review Of Energy‐efficient Data Fusion Approaches In Iotmentioning

confidence: 99%

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Pourghebleh

Hekmati

Davoudnia

et al. 2022

Concurrency and Computation

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Nowadays, with the rapid progress of Internet-based and distributed systems such as cloud computing, peer-to-peer networking, and Internet of Things (IoT), significant improvements in almost every engineering and commercial field have been made. On the basis of IoT, smart cities are formed utilizing intelligent information processing, universal connectivity, ubiquitous sensing, and real-time monitoring. Energy conservation is one of the significant issues in current IoT development due to the poor battery endurance of IoT objects. Over the last years, with smart cities' explosive growth, a huge range of studies regarding energy efficiency have been done. The diversity of sparse data and multi-sourcing is utilized in developing IoT scenarios. In order to use efficiently of these data to improve the IoT services, data fusion plays an important role. It saves network resources, improves data transmission efficiency, and extracts useful information from raw data. To the best of our knowledge, there is still a lack of a comprehensive and systematic study about surveying and analyzing the available energy-efficient data fusion techniques in the IoT. Thus, this article aims to address this gap using a systematic method.

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Section: Review Of Energy‐efficient Data Fusion Approaches In Iotmentioning

confidence: 99%

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Pourghebleh

Hekmati

Davoudnia

et al. 2022

Concurrency and Computation

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“…In this method, based on the fuzzy theory based on the IoT confidence [21,27] and credibility model, node inactivity is identified by an interpretation of IoT services' unique features. A fuzzy approach has made it possible to trust the development of a feedback system focused on relationship mapping and forwarding action among neighbours [10].…”

Section: Trust Management Model Based On Fuzzy Reputation For Iotmentioning

confidence: 99%

Intelligent Trust based Security Framework for Internet of Things

Rana¹,

Singh²,

Vijaya

2022

IJRITCC

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Trust models have recently been proposed for Internet of Things (IoT) applications as a significant system of protection against external threats. This approach to IoT risk management is viable, trustworthy, and secure. At present, the trust security mechanism for immersion applications has not been specified for IoT systems. Several unfamiliar participants or machines share their resources through distributed systems to carry out a job or provide a service. One can have access to tools, network routes, connections, power processing, and storage space. This puts users of the IoT at much greater risk of, for example, anonymity, data leakage, and other safety violations. Trust measurement for new nodes has become crucial for unknown peer threats to be mitigated. Trust must be evaluated in the application sense using acceptable metrics based on the functional properties of nodes. The multifaceted confidence parameterization cannot be clarified explicitly by current stable models. In most current models, loss of confidence is inadequately modeled. Esteem ratings are frequently mis-weighted when previous confidence is taken into account, increasing the impact of harmful recommendations. In this manuscript, a systematic method called Relationship History along with cumulative trust value (Distributed confidence management scheme model) has been proposed to evaluate interactive peers trust worthiness in a specific context. It includes estimating confidence decline, gathering & weighing trust parameters and calculating the cumulative trust value between nodes. Trust standards can rely on practical contextual resources, determining if a service provider is trustworthy or not and does it deliver effective service? The simulation results suggest that the proposed model outperforms other similar models in terms of security, routing and efficiency and further assesses its performance based on derived utility and trust precision, convergence, and longevity.

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“…Machine learning can change the healthcare industry into a modernization of operational skills, help forecast and develop numerous diseases, enhance clinical preliminary checks and rationalize insurance expenditures at all levels (yoo, et al 2012). Authors have provided an overview of several data mining approaches utilized in the biological and health insurance fields (Malchi et al 2021). Predictive analysis is an engaging big data specialist and identifies important designs to estimate the future opportunity.…”

Section: Related Workmentioning

confidence: 99%

“…The Pre-Processing and data transformation methods of (Malchi et al 2021) were utilized to generate awareness concerning the connection among many of the deliberate characteristics & patient endurance. 02 separate information mining procedures were worn in the variety of result policies to dig out information.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Machine Learning Techniques for Effective Predictive Analysis in Health Care Management

Prasad¹

2021

Biosc Biotech Res Comm

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The health division has seen a huge transformation subsequent the introduction of latest computer technology & this has led to added medicinal information producing various sectors of research. In recent years, there has been surges in interest in study on assessment sustain appliances in healthcare, for example individuals relating to analysis, forecast, behavior forecast, and so on. This progress is due to increased data availability, breakthroughs in artificial intelligence and machine learning research, and contact to computational assets. Data Mining and Predictive Analysis are being used by a number of healthcare organizations. Predictive analysis utilizes assortment of statistical procedures as well as representation, machine learning & data mining to approximate the future by breaking into past and current realism. Appropriate to the information ambitious environment of machine learning algorithms, artificial intelligence trends has achieved its full prospective when back up by large information positions. Our study examines the implications of current breakthroughs in information analytics and how these will be used to the healthcare industry, with a focus on analytical & prophecy applications.

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A trust-based fuzzy neural network for smart data fusion in internet of things

Cited by 34 publications

References 18 publications

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Intelligent Trust based Security Framework for Internet of Things

Implementation of Machine Learning Techniques for Effective Predictive Analysis in Health Care Management

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