An IoT-Based Fog Computing Model

Ma, Kun; Bagula, Antoine; Nyirenda, Clement N.; Ajayi, Olasupo

doi:10.3390/s19122783

Cited by 48 publications

(31 citation statements)

References 28 publications

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“…Fog computing involves data processing in the middle layer between the cloud server and smart end devices at the edge. There is much research available on energy efficiency in fog computing in wireless networks [144][145][146]. Although there is considerable research on fog computing based energy efficiency for NOMA [147][148][149][150], it can be utilized further to mitigate the prevalent challenges such as resource optimization, low processing power and optimal placing of servers [145,146].…”

Section: Fog Computingmentioning

confidence: 99%

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

et al. 2020

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Energy efficiency is a major concern in the emerging mobile cellular wireless networks since massive connectivity is to be expected with high energy requirements from the network operators. Non-orthogonal multiple access (NOMA) being the frontier multiple access scheme for 5G, there exists numerous research attempts on enhancing the energy efficiency of NOMA enabled wireless networks while maintaining its outstanding performance metrics such as high throughput, data rates and capacity maximized optimally.The concept of green NOMA is introduced in a generalized manner to identify the energy efficient NOMA schemes. These schemes will result in an optimal scenario in which the energy generated for communication is managed sustainably. Hence, the effect on the environment, economy, living beings, etc is minimized. The recent research developments are classified for a better understanding of areas which are lacking attention and needs further improvement. Also, the performance comparison of energy efficient, NOMA schemes against conventional NOMA is presented. Finally, challenges and emerging research trends, for energy efficient NOMA are discussed.

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Section: Fog Computingmentioning

confidence: 99%

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

et al. 2020

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“…More importantly, this study considered the computation capability of fog node middleware to be larger than that of the end devices, but smaller than that of the cloud center. Ma et al [25] proposed an IoT-based fog computing model. Based on the model, a genetic algorithm was proposed for reducing the failure node and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay

Guo

et al. 2019

Sensors

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Fog computing has recently emerged as an extension of cloud computing in providing high-performance computing services for delay-sensitive Internet of Things (IoT) applications. By offloading tasks to a geographically proximal fog computing server instead of a remote cloud, the delay performance can be greatly improved. However, some IoT applications may still experience considerable delays, including queuing and computation delays, when huge amounts of tasks instantaneously feed into a resource-limited fog node. Accordingly, the cooperation among geographically close fog nodes and the cloud center is desired in fog computing with the ever-increasing computational demands from IoT applications. This paper investigates a workload allocation scheme in an IoT–fog–cloud cooperation system for reducing task service delay, aiming at satisfying as many as possible delay-sensitive IoT applications’ quality of service (QoS) requirements. To this end, we first formulate the workload allocation problem in an IoT-edge-cloud cooperation system, which suggests optimal workload allocation among local fog node, neighboring fog node, and the cloud center to minimize task service delay. Then, the stability of the IoT-fog-cloud queueing system is theoretically analyzed with Lyapunov drift plus penalty theory. Based on the analytical results, we propose a delay-aware online workload allocation and scheduling (DAOWA) algorithm to achieve the goal of reducing long-term average task serve delay. Theoretical analysis and simulations have been conducted to demonstrate the efficiency of the proposal in task serve delay reduction and IoT-fog-cloud queueing system stability.

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“…This is because the cloud server is usually remote and spatially distant from the IoT devices, which leads to high transmission latency, downgraded performance of latency-sensitive applications and network congestion [2]. To address this issue, fog computing (FC)considered an extension of cloud computing (CC) [3,4] has been introduced by CISCO [5] which acts as an intermediary between the cloud and end-user devices. This brings processing, storage, and networking services spatially closer to end devices to reduce latency and network congestion.…”

Section: Introductionmentioning

confidence: 99%

“…The main idea behind FC is to deploy computing resources, i.e. fog nodes (FNs), at the network edge [3,6]. FNs can be routers, gateways, and access points to which end devices offload their computationally intensive tasks.…”

Section: Introductionmentioning

confidence: 99%

Minimising Delay and Energy in Online Dynamic Fog Systems

Alenizi¹,

Rana²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The increasing use of Internet of Things (IoT) devices generates a greater demand for data transfers and puts increased pressure on networks. Additionally, connectivity to cloud services can be costly and inefficient. Fog computing provides resources in proximity to user devices to overcome these drawbacks. However, optimisation of quality of service (QoS) in IoT applications and the management of fog resources are becoming challenging problems. This paper describes a dynamic online offloading scheme in vehicular traffic applications that require execution of delay-sensitive tasks. This paper proposes a combination of two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC) that aim to minimise overall delay, enhance throughput of user tasks and minimise energy consumption at the fog layer while maximising the use of resource-constrained fog nodes. Compared to other schemes, our experimental results show that these algorithms can reduce the delay by up to 80.79% and reduce energy consumption by up to 66.39% in fog nodes. Additionally, this approach enhances task execution throughput by 40.88%.

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An IoT-Based Fog Computing Model

Cited by 48 publications

References 28 publications

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

A New Green Prospective of Non-orthogonal Multiple Access (NOMA) for 5G

Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay

Minimising Delay and Energy in Online Dynamic Fog Systems

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