Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 98, Issue -, Pages 565-576Publisher
ELSEVIER
DOI: 10.1016/j.future.2019.01.021
Keywords
Internet of Medical Things (IoMT); Autonomous sensor nodes; Energy charging; Healthcare; Reward-based protocol
Categories
Funding
- International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia [ISPP-121]
Ask authors/readers for more resources
The Internet of Medical Things (IoMT) is an essential paradigm for ubiquitous monitoring in healthcare environments. The IoMT system collects data (e.g. temperature, hazardous contamination, light intensity, room and patient status, etc.) from connected medical devices and sensor nodes to a central or distributed computer network. In order for these devices and sensor nodes to continue operating, they must be charged with sufficient energy at all times. In this paper, we propose an IoMT system that employs autonomous mobile chargers, which is equipped with wireless energy transfer technology, to support sensor nodes recharging requests. In this model, the mobile charger must distribute the energy among the sensor nodes so that their operation is not interrupted. This is challenging because the mobile charger carries limited amount of energy that may not be sufficient to satisfy all recharging requests. In this paper, we propose a reward-based energy charging decision mechanism that allows mobile chargers and sensor nodes to coordinate the charging process. The proposed reward-based mechanism utilizes the Analytical Hierarchy Process (AHP) for fair distribution of energy among the nodes. This paper presents the theoretical analysis of the model and the simulation experiments. Our results show that the proposed model can support a larger number of active nodes with less energy compared to conventional first come first served methods. Also the coverage utility of sensor nodes is much higher using our method compared to the on-demand recharging request schemes found in existing studies. (C) 2019 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available