Low-latency and energy-efficient scheduling in fog-based IoT applications

Rahbari, Dadmehr; Nickray, Mohsen

doi:10.3906/elk-1810-47

Cited by 70 publications

(56 citation statements)

References 47 publications

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“…The data demanding higher storage and processing resources than available at the fog node are transferred to the cloud. The execution cost at cloud includes the cost required for the execution of application modules in the cloud [ 57 , 58 , 59 ]. Fog computing adaptation reduces the execution cost in the cloud by minimizing the amount of data transfer to the cloud server.…”

Section: Simulation Setup and Resultsmentioning

confidence: 99%

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

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Section: Simulation Setup and Resultsmentioning

confidence: 99%

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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“…Managing street lights is important to reduce the wastage of energy and resources. The work by Rahbari and Nickray [13] proposed energy-efficient scheduling which can allocate resources appropriately on the basis of energy consumptions in a fog network by using a greedy knapsack-based scheduling algorithm for Internet of Things (IoT) applications. It is useful to be applied in various IoT devices, vehicles and street lights in smart cities.…”

Section: Literature Reviewmentioning

confidence: 99%

Placement accuracy algorithm for smart street lights

Ishak¹,

Ahmad²,

Radzi³

et al. 2021

Turk J Elec Eng & Comp Sci

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“…The authors claimed to get better results as compared to Bee Life Algorithm (BLA) and Modified Particle Swarm Optimization (MPSO). Recently, in 2019, Rahbari and Nickray implemented greedy knapsack‐based scheduling (GKS) algorithm for job scheduling using iFogSim simulator . They applied their algorithm on video surveillance (VSOT) and EEG tractor beam game (EEGTBG).…”

Section: Related Workmentioning

confidence: 99%

“…Ni et al 15 algorithm for job scheduling using iFogSim simulator. 24 They applied their algorithm on video surveillance (VSOT) and EEG tractor beam game (EEGTBG). They compared their execution cost and energy consumption with FCFS, concurrent and delay priority scheduling, and claimed to get better results.…”

Section: Related Workmentioning

confidence: 99%

A job scheduling algorithm for delay and performance optimization in fog computing

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

103

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Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

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Low-latency and energy-efficient scheduling in fog-based IoT applications

Cited by 70 publications

References 47 publications

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Placement accuracy algorithm for smart street lights

A job scheduling algorithm for delay and performance optimization in fog computing

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