Decentralized Energy Efficient Model for Data Transmission in IoT-based Healthcare SystemShow others and affiliations
2021 (English)In: 2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING), IEEE, 2021, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]
The growing world population is facing challengessuch as increased chronic diseases and medical expenses.Integrate the latest modern technology into healthcare systemcan diminish these issues. Internet of medical things (IoMT) isthe vision to provide the better healthcare system. The IoMTcomprises of different sensor nodes connected together. TheIoMT system incorporated with medical devices (sensors) forgiven the healthcare facilities to the patient and physician canhave capability to monitor the patients very efficiently. Themain challenge for IoMT is the energy consumption, batterycharge consumption and limited battery lifetime in sensor basedmedical devices. During charging the charges that are stored inbattery and these charges are not fully utilized due to nonlinearity of discharging process. The short time period neededto restore these unused charges is referred as recovery effect. Analgorithm exploiting recovery effect to extend the batterylifetime that leads to low consumption of energy. This paperprovides the proposed adaptive Energy efficient (EEA)algorithm that adopts this effect for enhancing energyefficiency, battery lifetime and throughput. The results havebeen simulated on MATLAB by considering the Li-ion battery.The proposed adaptive Energy efficient (EEA) algorithm is alsocompared with other state of the art existing method named,BRLE. The Proposed algorithm increased the lifetime ofbattery, energy consumption and provides the improvedperformance as compared to BRLE algorithm. It consumes lowenergy and supports continuous connectivity of devices withoutany loss/ interruptions.
Place, publisher, year, edition, pages
IEEE, 2021. p. 1-5
Keywords [en]
Internet of medical things, recovery effect, EEA, discharging, battery charge.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-42719DOI: 10.1109/VTC2021-Spring51267.2021.9448886ISI: 000687839601087Scopus ID: 2-s2.0-85112452917ISBN: 978-1-7281-8964-2 (print)OAI: oai:DiVA.org:miun-42719DiVA, id: diva2:1582251
Conference
93rd IEEE Vehicular Technology Conference (VTC) 2021-Spring, Helsinki, Finland, April 25-28, 2021.
2021-07-292021-07-292021-09-23Bibliographically approved