AI-driven adaptive reliable and sustainable approach for internet of things enabled healthcare system

Noman Zahid; Ali Hassan Sodhro; Usman Rauf Kamboh; Ahmed Alkhayyat; Lei Wang; Noman Zahid; Ali Hassan Sodhro; Usman Rauf Kamboh; Ahmed Alkhayyat; Lei Wang

doi:10.3934/mbe.2022182

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 4: 3953-3971. doi: 10.3934/mbe.2022182

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AI-driven adaptive reliable and sustainable approach for internet of things enabled healthcare system

1.
Office of Research, Innovation and Commercialization (ORIC), The University of Faisalabad, Faisalabad, Punjab, Pakistan
2.
Department of Computer Science, Kristianstad University, Kristianstad SE-291 88, Sweden
3.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
4.
Department of Computational Sciences, The University of Faisalabad, Faisalabad, Punjab, Pakistan
5.
College of Technical Engineering, the Islamic University, Najaf, Iraq

Academic Editor:Yang Kuang

Received: 12 October 2021 Revised: 23 January 2022 Accepted: 24 January 2022 Published: 11 February 2022

Artificial Intelligence (AI) driven adaptive techniques are viable to optimize the resources in the Internet of Things (IoT) enabled wearable healthcare devices. Due to the miniature size and ability of wireless data transfer, Body Sensor Networks (BSNs) have become the center of attention in current medical media technologies. For a long-term and reliable healthcare system, high energy efficiency, transmission reliability, and longer battery lifetime of wearable sensors devices are required. There is a dire need for empowering sensor-based wearable techniques in BSNs from every aspect i.e., data collection, healthcare monitoring, and diagnosis. The consideration of protocol layers, data routing, and energy optimization strategies improves the efficiency of healthcare delivery. Hence, this work presents some key contributions. Firstly, it proposes a novel avant-garde framework to simultaneously optimize the energy efficiency, battery lifetime, and reliability for smart and connected healthcare. Secondly, in this study, an Adaptive Transmission Data Rate (ATDR) mechanism is proposed, which works on the average constant energy consumption by varying the active time of the sensor node to optimize the energy over the dynamic wireless channel. Moreover, a Self-Adaptive Routing Algorithm (SARA) is developed to adopt a dynamic source routing mechanism with an energy-efficient and shortest possible path, unlike the conventional routing methods. Lastly, real-time datasets are adopted for intensive experimental setup for revealing pervasive and cost-effective healthcare through wearable devices. It is observed and analysed that proposed algorithms outperform in terms of high energy efficiency, better reliability, and longer battery lifetime of portable devices.
- AI,
- IoT,
- Sustainable,
- reliable,
- routing,
- data rate,
- smart and connected,
- healthcare
Citation: Noman Zahid, Ali Hassan Sodhro, Usman Rauf Kamboh, Ahmed Alkhayyat, Lei Wang. AI-driven adaptive reliable and sustainable approach for internet of things enabled healthcare system[J]. Mathematical Biosciences and Engineering, 2022, 19(4): 3953-3971. doi: 10.3934/mbe.2022182

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Abstract

Artificial Intelligence (AI) driven adaptive techniques are viable to optimize the resources in the Internet of Things (IoT) enabled wearable healthcare devices. Due to the miniature size and ability of wireless data transfer, Body Sensor Networks (BSNs) have become the center of attention in current medical media technologies. For a long-term and reliable healthcare system, high energy efficiency, transmission reliability, and longer battery lifetime of wearable sensors devices are required. There is a dire need for empowering sensor-based wearable techniques in BSNs from every aspect i.e., data collection, healthcare monitoring, and diagnosis. The consideration of protocol layers, data routing, and energy optimization strategies improves the efficiency of healthcare delivery. Hence, this work presents some key contributions. Firstly, it proposes a novel avant-garde framework to simultaneously optimize the energy efficiency, battery lifetime, and reliability for smart and connected healthcare. Secondly, in this study, an Adaptive Transmission Data Rate (ATDR) mechanism is proposed, which works on the average constant energy consumption by varying the active time of the sensor node to optimize the energy over the dynamic wireless channel. Moreover, a Self-Adaptive Routing Algorithm (SARA) is developed to adopt a dynamic source routing mechanism with an energy-efficient and shortest possible path, unlike the conventional routing methods. Lastly, real-time datasets are adopted for intensive experimental setup for revealing pervasive and cost-effective healthcare through wearable devices. It is observed and analysed that proposed algorithms outperform in terms of high energy efficiency, better reliability, and longer battery lifetime of portable devices.

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