Decentralized Energy Efficient Model for Data Transmission in IoT-based Healthcare System

Sodhro, Ali Hassan; Al-Rakhami, Mabrook S.; Wang, Lei; Magsi, Hina; Zahid, Noman; Pirbhulal, Sandeep; Nisar, Kashif; Ahmad, Awais

doi:10.1109/vtc2021-spring51267.2021.9448886

Cited by 25 publications

(8 citation statements)

References 21 publications

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“…Sodhro et al [ 63 ] proposed an energy-efficient algorithm (EEA) that mainly focuses on data transmission and connectivity increase with a reduced interruption during information transfer. The authors compared the proposed algorithm with battery recovery-based lifetime enhancement (BRLE) using parameters, such as energy dissipation and charge dissipation.…”

Section: Related Workmentioning

confidence: 99%

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Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers’ attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own. IoMT is playing a crucial role within the healthcare industry to increase the accuracy, reliability, and productivity of electronic devices. This research work provides an overview of IoMT with emphasis on various enabling techniques used in smart healthcare systems (SHS), such as radio frequency identification (RFID), artificial intelligence (AI), and blockchain. We are providing a comparative analysis of various IoMT architectures proposed by several researchers. Also, we have defined various health domains of IoMT, including the analysis of different sensors with their application environment, merits, and demerits. In addition, we have figured out key protocol design challenges, which are to be considered during the implementation of an IoMT network-based smart healthcare system. Considering these challenges, we prepared a comparative study for different data collection techniques that can be used to maintain the accuracy of collected data. In addition, this research work also provides a comprehensive study for maintaining the energy efficiency of an AI-based IoMT framework based on various parameters, such as the amount of energy consumed, packet delivery ratio, battery lifetime, quality of service, power drain, network throughput, delay, and transmission rate. Finally, we have provided different correlation equations for finding the accuracy and efficiency within the IoMT-based healthcare system using artificial intelligence. We have compared different data collection algorithms graphically based on their accuracy and error rate. Similarly, different energy efficiency algorithms are also graphically compared based on their energy consumption and packet loss percentage. We have analyzed our references used in this study, which are graphically represented based on their distribution of publication year and publication avenue.

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Section: Related Workmentioning

confidence: 99%

“…Sodhro et al [63] proposed an energy-efficient algorithm (EEA) that mainly focuses on data transmission and connectivity increase with a reduced interruption during information transfer.…”

Section: Comparison Of Energy Efficiency Measurementmentioning

confidence: 99%

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Srivastava

Routray

Ahmad

et al. 2022

Computational Intelligence and Neuroscience

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“…The vast data gathered demonstrate that novel model save operational expenses for internet services and enhance the area under the curve of the model [13], [18]. Artificial intelligence-based models [14] and secure IoMT devices [15], [19] are recently gaining importance.…”

Section: Related Workmentioning

confidence: 99%

A Review on Intelligent Health Care System Using Learning Methods

Raul

Das

Murty

et al. 2023

Advances in Transdisciplinary Engineering

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All organizations that are striving for healthy living must establish and maintain updated diet regimens. A health care recommendation system uses data to give medical advice. Machine Learning analyses data to predict future requests and improve healthcare management. An intelligent health care system needs to be built using machine learning and blockchain for health care recommendation systems. This paper discusses in detail the various systems for providing high quality health services. The existing systems that are discussed are Distributed Ledger Technology (DLT), swarm intelligence learning-based systems, adaptive systems, and deep learning systems. Furthermore, various techniques, advantages, disadvantages, tools used, and their accuracy are compared and contrasted. From the studies, it is clear that block chain technology with deep learning techniques provides better accuracy than other conventional methods.

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“…In [10] for typical WSN-IoT nodes in smart city applications, we recommend using machine learning as an optimization technique. As far as the authors are aware, this is the first thorough analysis of the literature on all ML techniques in low-power WSN-IoT for smart cities.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

Dinesh

2023

JMC

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Wireless sensor networks are widely used in various Internet of Things applications, including healthcare, underwater sensor networks, body area networks, and multiple offices. Wireless Body Area Network (WBAN) simplifies medical department tasks and provides a solution that reduces the possibility of errors in the medical diagnostic process. The growing demand for real-time applications in such networks will stimulate significant research activity. Designing scenarios for such critical events while maintaining energy efficiency is difficult due to dynamic changes in network topology, strict power constraints, and limited computing power. The routing protocol design becomes crucial to WBAN and significantly impacts the communication stack and network performance. High node mobility in WBAN results in quick topology changes, affecting network scalability. Node clustering is one of many other mechanisms used in WBANs to address this issue. We consider optimization factors like distance, latency, and power consumption of IoT devices to achieve the desired CH selection. This paper proposes a high-level CH selection and routing approach using a hybrid fuzzy with a modified Rider Optimization Algorithm (MROA). This research work is implemented using MATLAB software. The simulations are carried out under a range of conditions. In terms of energy consumption and network life time, the proposed scheme outperforms current state-of-the-art techniques like Low Energy Adaptive Clustering Hierarchy (LEACH), Energy Control Routing Algorithm (ECCRA), Energy Efficient Routing Protocol (EERP), and Simplified Energy Balancing Alternative Aware Routing Algorithm (SEAR).

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Decentralized Energy Efficient Model for Data Transmission in IoT-based Healthcare System

Cited by 25 publications

References 21 publications

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress

A Review on Intelligent Health Care System Using Learning Methods

Energy Efficient Clustering and Routing Using Hybrid Fuzzy with Modified Rider Optimization Algorithm in IoT - Enabled Wireless Body Area Network

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