Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT

Venkatachalam, K.; Prabu, P.; Alluhaidan, Ala Saleh; Hubálovský, Štěpán; Trojovský, Pavel

doi:10.1007/s11036-021-01861-y

Cited by 22 publications

(12 citation statements)

References 22 publications

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“…e results of the current study showed a signi cant result when regression analysis was carried out between the independent variables and the accuracy of IoT devices. e results suggest that the current study of training a regression model shows statistically valuable results [26]. In the case of doctors, the results were not statistically signi cant, which suggests that the accuracy levels of doctors vary among them [27].…”

Section: Discussion and Findingsmentioning

confidence: 66%

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

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Chronic diabetes among adults is a public health concern and clinicians are trying to implement new strategies to effectively manage the disease. Traditionally, healthcare professionals are used to monitor and track the lab reports of patients. After that, they used to provide respective medicines and lifestyle plans to manage the chronic disease. The lifestyle of the patients and access to safe and secure food products is also responsible for developing chronic diseases. Thus, the Internet of Things (IoT) has taken an utmost interest in managing diabetes. This research is going to analyze the accuracy of IoT in assisting chronic diabetes management and determining food safety. To accomplish the research objectives, the researchers performed a linear regression analysis to understand whether IoT devices and Artificial Intelligence (AI) assist in assessing food safety and diabetes management. The independent variables selected were lab test values, treatment records, epoch size of AI, and image resolution of the training dataset. Dependent variables were the accuracy of IoT. Here, the accuracy of IoT and AI has been determined. Moreover, the accuracy of clinicians in diabetes management has been observed. It has been found that clinicians have high variance in accuracy (max 99%) whereas machines have limited variance in accuracy (max. 98%). Secondary research identified that clinicians need to be involved along with IoT devices for better management of this chronic disease and help patients by providing the safest food options.

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Section: Discussion and Findingsmentioning

confidence: 66%

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…Because the reason is that the last node dies longer in the proposed scheme, the first node kills faster than in previous designs. The squirrel search method [26], which was motivated by the hunting behavior of flying squirrels is used in the fields including such Micro-Electrical Mechanical components (MEMS) [27], storage systems are used [28][29][30][31] yet another continuously evolving planning procedure. In the energy-aware confidence, the Gravitational Search Algorithm (ETGSA) was introduced in WSN addressing energy-saving issues, boosting network reliability, and lowering computing complexity.…”

Section: Related Workmentioning

confidence: 99%

“…The location and the speed of the particles in the method are initialized once the cluster head node is chosen, and the fitness value is built using the nodes power usage, remaining energy, minimal hop count, distances from BS, and link quality. In order to establish the best route discovery [28][29][30][31] in the complex and dynamic surroundings of farming, the Particle Swarm Optimization technique is applied.…”

Section: Multi-hop Path Optimization Using Improved Particle Swarm Op...mentioning

confidence: 99%

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Hamza¹,

Hashim²,

Elkamchouchi³

et al. 2023

Computer Systems Science and Engineering

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Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

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“…On a Local Area Network (LAN) [9], IoT edge applications [10] can run without the need of cloud services, but they may also be used to connect to the cloud. Using EdgeX Foundry or Apache Edgent, clients may build and manage their own back-end systems, independent of any cloud platform.…”

Section: Scenariosmentioning

confidence: 99%

Open Source Network Optimization Tools for Edge Intelligence

Wang

Steeg

2022

JCNS

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It is indeed possible to bring analysis and information storage closer to where the information is generated by implementing an edge computing model. Response times should improve while bandwidth use is reduced as a result." A common misconception is that "edge" and "IoT" are synonymous. Using edge computing in the Internet of Things (IoT) is an example of this type of distributed computing, which is sensitive to configuration and location." Instead, then alluding to a specific piece of technology, the word refers to an overall architecture. In order to discover novel study opportunities and aid users in selecting more suitable edge computing advancements, this paper provides an analysis of existing open-source computing projects. Also, a comparison of the project’s applicability will be defined.

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Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT

Cited by 22 publications

References 22 publications

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Open Source Network Optimization Tools for Edge Intelligence

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