Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes

Rajasekaran, M.; Yassine, Abdulsalam; Hossain, M. Shamim; Alhamid, Mohammed F.; Guizani, Mohsen

doi:10.1016/j.future.2019.01.021

Cited by 46 publications

(22 citation statements)

References 26 publications

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“…Further, the data transmission to cloud server in case of large number of requests increases latency and consumes more energy consumption, which degrades the performance of the system. Manikandan et al [16] proposed an Autonomous Monitoring System (AMS) model for Internet of Medical Things (IoMT) to provide healthcare facilities. In this research work, a reward-based mechanism designed which utilizes the Analytical Hierarchy Process (AHP) for fair distribution of energy among the nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Rabindra and Rojalina [22] proposed a fogbased machine learning model for smart system big data analytics called FogLearn for application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. Alvin et al [ [11] ✓ ✓ FogCepCare [12] ✓ ✓ IoT e-health service [13] ✓ ✓ ECGH [14] ✓ ✓ ✓ AMS [16] ✓ ✓ GRAM [17] ✓ [39] proposed a Cloud-based Smart Home Environment (CoSHE) to deliver home healthcare to provide humans contextual information and monitors the vital signs using robot assistant. Initially, CoSHE uses non-invasive wearable sensors to gather the audio, motion and physiological signals and delivers the contextual information in terms of the residents daily activity.…”

Section: Related Workmentioning

confidence: 99%

“…Other works that propose computing models for healthcare applications in Fog Computing do not consider various aspects which HealthFog does. Many prior works [13,16,17,22,23,39,42] do not leverage resources close to the edge of the network. As per Figure 13, such models provide a much higher latency as all computation is done on the cloud and hence has higher data transfer times.…”

Section: Analysis With Related Workmentioning

confidence: 99%

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HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

498

248

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Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Analysis With Related Workmentioning

confidence: 99%

See 1 more Smart Citation

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

498

248

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“…In addition to hardware solutions to supply energy, we can find some software-based techniques to control energy consumption for mobile devices. In [9], authors propose a reward-based energy charging mechanism for connected medical devices. The approach proposes an Internet of Medical Things system, which is equipped with wireless energy transfer technology and employs autonomous mobile chargers to support sensor nodes recharging requests.…”

Section: Related Workmentioning

confidence: 99%

Self-Adaptation of mHealth Devices: The Case of the Smart Cane Platform

Ayala

Ballesteros

Caro-Romero

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Nowadays, more than one billion people are in need of one or more assistive technologies, and this number is expected to increase beyond two billion by 2050. The majority of assistive technologies are supported by battery-operated devices like smartphones and wearables. This means that battery weight is an important concern in such assistive devices because it may affect negatively its ergonomics. Saving power in these assistive devices is of utmost importance for its potential twofold benefits: extend the device life and reduce the global warming aggravated by billion of these devices. Dynamic Software Product Lines (DSPLs) are a suitable technology that supports system adaptation, in this case, to reduce energy consumption at runtime, considering contextual information and the current state of the device. However, a reduction in battery consumption could negatively affect other quality of service parameters, like response time. Therefore, it is important to trade-off battery saving and these other concerns. This work illustrates how to approach the self-adaptation of smart assistive devices by means of a DSPL-based strategy that optimizes battery consumption taking into account other QoS parameters at the same time. We illustrate our proposal with a real case study: a Smart Cane that is integrated with a DSPL platform, Tanit. Experimentation shows that it is possible to make a trade-off between different quality concerns (energy consumption and relative error). The results of the experiments allow us to conclude that the Tanit approach elongates battery duration of the Smart Cane in one day (an increase of a 6% with a relative error of 1%), so we improve the user quality of experience and reduce the energy footprint with a reasonable relative error.

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“…For use in clinical applications, a sensor system must either function for a very long time using a primary cell or, as was investigated in earlier studies by Daniol et al and Lech [ 15 , 16 ], be designed to be autonomous with the help of thermal energy harvesting. Other approaches, for example, made by Rajasekaran et al [ 17 ], consider automated wireless charging of sensors, but this creates further challenges that should not be considered in a first step.…”

Section: Introductionmentioning

confidence: 99%

Sensors in the Autoclave-Modelling and Implementation of the IoT Steam Sterilization Procedure Counter

Boehler

Danioł

Sroka

et al. 2021

Sensors

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Surgical procedures involve major risks, as pathogens can enter the body unhindered. To prevent this, most surgical instruments and implants are sterilized. However, ensuring that this process is carried out safely and according to the normative requirements is not a trivial task. This study aims to develop a sensor system that can automatically detect successful steam sterilization on the basis of the measured temperature profiles. This can be achieved only when the relationship between the temperature on the surface of the tool and the temperature at the measurement point inside the tool is known. To find this relationship, the thermodynamic model of the system has been developed. Simulated results of thermal simulations were compared with the acquired temperature profiles to verify the correctness of the model. Simulated temperature profiles are in accordance with the measured temperature profiles, thus the developed model can be used in the process of further development of the system as well as for the development of algorithms for automated evaluation of the sterilization process. Although the developed sensor system proved that the detection of sterilization cycles can be automated, further studies that address the possibility of optimization of the system in terms of geometrical dimensions, used materials, and processing algorithms will be of significant importance for the potential commercialization of the presented solution.

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Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes

Cited by 46 publications

References 26 publications

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Self-Adaptation of mHealth Devices: The Case of the Smart Cane Platform

Sensors in the Autoclave-Modelling and Implementation of the IoT Steam Sterilization Procedure Counter

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