A Review on Big IoT Data Analytics for Improving QoS-Based Performance in System: Design, Opportunities, and Challenges

Safa, M.; Pandian, A.

doi:10.1007/978-981-15-5329-5_40

Cited by 7 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Health research downplays the significance of multiple data processing techniques [10] despite being warned that these developments will outline their benefits and drawbacks [11] and reiterating the difficulties and problems with health record processing technique [12,13]. Longstanding research interests include the forecasting of cardiovascular risk using medical information [14,15]. The prediction's accuracy is still sufficient despite extensive attempts.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Rule Based- Model for Proficient Heart Disease Prediction in Data Lake

Mani,

Munusamy

2023

RIA

View full text Add to dashboard Cite

Cardiovascular diseases remain the leading cause of mortality on a global scale. Currently, healthcare organizations are shifting their operational strategies to enhance efficiency and reduce costs. For this transition, the integration of analytics into IT strategy is imperative. Data lakes, which aggregate data from multiple sources and employ analytical models, provide an innovative approach to information management, reporting, and predictive analytics. These models enable the production of advanced analytical insights, the implementation of evidence-based care plans, and the improvement of patient engagement outcomes, thus setting the stage for IoT-based prognostic systems aimed at reducing mortality rates. The present research proposes a comprehensive data analysis for the prognosis of coronary heart disease, a task that poses considerable challenges due to the volume of data across various disciplines and the complexity involved in analyzing, extracting, managing, and configuring data with massive data technologies and tools. To tackle this challenge, a multi-level fuzzy rule generation is suggested for identifying the features used in heart disease prediction. These features are then trained using an optimized recurrent neural network. The features are classified into labeled classes based on the risk assessment of a medical professional, enabling the prediction of the class based on risk. Early diagnosis and treatment are thus facilitated. When benchmarked against traditional systems, the proposed approach demonstrates superior performance, validating its potential for efficient heart disease prediction.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Rule Based- Model for Proficient Heart Disease Prediction in Data Lake

Mani,

Munusamy

2023

RIA

View full text Add to dashboard Cite

show abstract

“…The current healthcare legacy and service delivery solutions, and the existing smart contract-based serverless environment, create a lack of interoperability. It is difficult to adopt and develop a platform between patients and healthcare service delivery systems due to this disunion and weak connection [46,47].…”

Section: Cross-chain Platform Issuesmentioning

confidence: 99%

QoS-Ledger: Smart Contracts and Metaheuristic for Secure Quality-of-Service and Cost-Efficient Scheduling of Medical-Data Processing

et al. 2021

View full text Add to dashboard Cite

Quality-of-service (QoS) is the term used to evaluate the overall performance of a service. In healthcare applications, efficient computation of QoS is one of the mandatory requirements during the processing of medical records through smart measurement methods. Medical services often involve the transmission of demanding information. Thus, there are stringent requirements for secure, intelligent, public-network quality-of-service. This paper contributes to three different aspects. First, we propose a novel metaheuristic approach for medical cost-efficient task schedules, where an intelligent scheduler manages the tasks, such as the rate of service schedule, and lists items utilized by users during the data processing and computation through the fog node. Second, the QoS efficient-computation algorithm, which effectively monitors performance according to the indicator (parameter) with the analysis mechanism of quality-of-experience (QoE), has been developed. Third, a framework of blockchain-distributed technology-enabled QoS (QoS-ledger) computation in healthcare applications is proposed in a permissionless public peer-to-peer (P2P) network, which stores medical processed information in a distributed ledger. We have designed and deployed smart contracts for secure medical-data transmission and processing in serverless peering networks and handled overall node-protected interactions and preserved logs in a blockchain distributed ledger. The simulation result shows that QoS is computed on the blockchain public network with transmission power = average of −10 to −17 dBm, jitter = 34 ms, delay = average of 87 to 95 ms, throughput = 185 bytes, duty cycle = 8%, route of delivery and response back variable. Thus, the proposed QoS-ledger is a potential candidate for the computation of quality-of-service that is not limited to e-healthcare distributed applications.

show abstract

“…One of the reasons behind its success is minimum human intervention, allowing electronic devices to interact with each other directly as well as with the environment to communicate information [2]. To deploy these devices in numerous quantities over a large area requires them to be affordable concerning their battery, power, and memory computation because the most critical issue in a tiny ubiquitous device is its limited amount of resources [4]. Only a small, finite amount of energy may be available through a miniature battery to the tiny processors embedded in ubiquitous IoT devices.…”

Section: Introductionmentioning

confidence: 99%

“…Also, IoT devices generate huge data with varying confidentiality levels. Hence, for a resource-constraint device, using one encryption protocol for confidential as well as for non-confidential information [3,4] is not the optimal approach. Moreover, the cryptographic algorithm for IoT devices should be selected wisely because every cipher consumes a varying amount of computing resources including its security strength, resource requirements, memory consumption, CPU utilization, bandwidth, and battery consumption [6].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Ontology-based Context-aware Encryption approach in IoT through Device and Information Classification

Dar

Awada

Zeshan

et al. 2023

Preprint

View full text Add to dashboard Cite

IoT devices produce a vast amount of data ranging from personal to sensitive information. Usually, these devices remain connected to the internet so protecting the information produced by them is crucial. Since most of the IoT devices are resource-constrained, they must be supported with light-weight encryption standards to protect information. Recent research has used the concept of context awareness to select the most suitable data encryption standard based on the device resources along with the required information confidentiality level. However, to effectively use the context information, it is required to be organized explicitly while considering the dynamic nature of IoT systems. In this regard, ontology-based systems effectively reduce the volume of manual work while recommending solutions. Currently, these systems cannot work with precision due to multiple uncertain factors of IoT sensory data,. To overcome this challenge, this research proposes a fuzzy ontology-based context-aware system to protect IoT device information with the help of an encryption algorithm that considers device capabilities and user priorities regarding the data confidentiality. In-order to automate the recommendation process, Semantic Web Rule Language (SWRL) rules and fuzzy logic are used, whereas, Description Logic and RDF Query Language is used to evaluate the results. The evaluation results confirm that the proposed method can produce results according to human perception by significantly increasing the accuracy of prediction.

show abstract

A Review on Big IoT Data Analytics for Improving QoS-Based Performance in System: Design, Opportunities, and Challenges

Cited by 7 publications

References 12 publications

Fuzzy Rule Based- Model for Proficient Heart Disease Prediction in Data Lake

Fuzzy Rule Based- Model for Proficient Heart Disease Prediction in Data Lake

QoS-Ledger: Smart Contracts and Metaheuristic for Secure Quality-of-Service and Cost-Efficient Scheduling of Medical-Data Processing

A Fuzzy Ontology-based Context-aware Encryption approach in IoT through Device and Information Classification

Contact Info

Product

Resources

About