Intelligent and Energy Efficient Mobile Smartphone Gateway for Healthcare Smart Devices Based on 5G

Sigwele, Tshiamo; Hu, Yim Fun; Ali, Muhammad; Hou, Jiachen; Susanto, Misfa; Fitriawan, Helmy

doi:10.1109/glocom.2018.8648031

Cited by 29 publications

(10 citation statements)

References 14 publications

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“…To analyse the performance of the proposed 5GREM framework, one MRRHs overlaid by 12 PRRHs per MRRH is considered. The proposed 5GREM framework is compared with: 1) Local Computation scheme denoted LC in the results where all health tasks are processed locally in the gateway, 2) our previous work in [5] introduced in Section I) the EECO scheme introduced in Section II in [11]. In EECO, the MEC servers are of limited processing capacity and standalone.…”

Section: A Results Simulation Toolsmentioning

confidence: 99%

“…There are many VMs in the MEC cloud that can process tasks of an application in parallel. The total energy consumed by k via offloading through the MRRH can then be calculated as (5) Similarly, for offloading via the PRRH, 73. Nearby D2D offloading: In case the UE offload a task to a nearby device, the time of execution denoted is equivalent to the local computation execution time in (5) plus transmission time ( = + /13.5Mbps) where 13.5Mbps is D2D uplink data rate.…”

Section: Mec Computationmentioning

confidence: 99%

“…However, using a smartphone as a healthcare gateway is computer intensive and will drain a lot of energy due to computation and transmission power consumption. This paper is an extension of our previous works in [4] and [5] called IEE5GG, where a 5G and Mobile Edge Computing (MEC) based mobile gateway connected to body sensors was proposed for minimising energy consumption, task failure rate and health service time. The extension in this paper is the introduction of mobile to mobile task offloading using device to device (D2D) communication to argument the gateway processing power and reduce the gateway energy I consumption.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficient Healthcare Monitoring System using 5G Task Offloading

Sigwele

Naveed

et al. 2019

JESR

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Healthcare expenses can be significantly reduced, and lives saved by enabling the continuous monitoring of patient health remotely using Wireless Body Sensor Networks (WBSN). However, an energy efficient mobile gateway (e.g. 5G smartphone) is required which moves with the patient in real time to process the data from the bio sensors without depleting the battery. This paper proposes a 5G based healthcare cardiovascular disease REmote Monitoring system called 5GREM using Electrocardiogram (ECG) bio sensor as a BSN device. The aim is to monitor and analyse the patient’s heart rhythms and send emergency alerts during irregularities to the nearest caregivers, ambulance or physician to minimise heart attacks and heart failures while saving energy. Since ECG signal execution is computer intensive, requests from the ECG sensor are either executed locally on the gateway, offloaded to nearby mobile devices or to the 5G edge while considering the battery level, CPU level, transmission power, delays and task fail rate

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Section: A Results Simulation Toolsmentioning

confidence: 99%

Section: Mec Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficient Healthcare Monitoring System using 5G Task Offloading

Sigwele

Naveed

et al. 2019

JESR

Self Cite

View full text Add to dashboard Cite

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“…In [22], the author proposed an intelligent and energy efficient 5G‐based smartphone Gateway for health care smart devices (IEE5GG). Patient's smartphone is used as a gateway where multiple smart devices are connected, e.g.…”

Section: Multi‐access Edge Computing (Mec)mentioning

confidence: 99%

Survey on health care applications in 5G networks

2020

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“…Nowadays, with the rapid evolution of communication technology and the enormous popularity of high-demand applications (e.g., the Internet of vehicles, mobile augmented reality, map navigation, face/fingerprint/iris recognition, mobile healthcare, web browsing, cloud gaming, image identification), a huge number of devices are attached to the Internet of Things (IoT) infrastructure [ 1 , 2 , 3 , 4 , 5 ]. In conventional networking infrastructures, the demand poses an enormous burden due to the generation of huge volumes of data from using these devices.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Hossain

Sultana

Hossain

et al. 2021

Sensors

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Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

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Intelligent and Energy Efficient Mobile Smartphone Gateway for Healthcare Smart Devices Based on 5G

Cited by 29 publications

References 14 publications

Energy Efficient Healthcare Monitoring System using 5G Task Offloading

Energy Efficient Healthcare Monitoring System using 5G Task Offloading

Survey on health care applications in 5G networks

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

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