Quality attributes in edge computing for the Internet of Things: A systematic mapping study

Ashouri, Majid; Davidsson, Paul; Spalazzese, Romina

doi:10.1016/j.iot.2020.100346

Cited by 21 publications

(6 citation statements)

References 60 publications

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“…Ashouri et al [4] 2021 The paper investigated quality attributes that are described in the literature for assessing IoT systems using EC. Mijuskovic et al [5] 2021 This paper discussed resource allocation challenges, such as 5G deployment, serverless computing, energy consumption, data management, trust models, business and service models, mobility, and industrial IoT, for cloud, fog, and EC.…”

Section: Ref Year Descriptionmentioning

confidence: 99%

Non-functional requirements elicitation for edge computing

Gupta

2022

Internet of Things

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Section: Ref Year Descriptionmentioning

confidence: 99%

Non-functional requirements elicitation for edge computing

Gupta

2022

Internet of Things

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“…Let L t i is latency of the i th node to its parent node where t is the tier in which the i th device is located. The design method used in the application deployment based on the fog paradigm in this paper is the Distributed Data Flow Model (DDF) [6]. The DDF method can better understand the various components of the application and is the best way to deploy the application in a distributed computing environment.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…When the arrival rate of the input becomes higher, it will increase the overhead on the parent node. However, fog devices are energy-constrained devices, and their computing resources are limited, so they cannot bear such a large overhead for a long enough time [6].…”

Section: Introductionmentioning

confidence: 99%

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

Hassan¹,

Ahmad²,

Nebhen³

et al. 2022

Computers, Materials &Amp; Continua

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The modern paradigm of the Internet of Things (IoT) has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing. However, such applications cannot meet strict quality of service (QoS) requirements. The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data. Generally, cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers. Fog devices with different computing capabilities must process the data generated by the end device, so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task. In addition, when there is an inconsistent connection delay between the fog and the terminal device, the deployment of such applications becomes more complicated. In this article, we propose an algorithm that can effectively place application modules on network nodes while considering connection delay, processing power, and sensing data volume. Compared with traditional cloud computing deployment, we conducted simulations in iFogSim to confirm the effectiveness of the algorithm. The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption. Therein, latency and execution time is insensitive to the number of sensors.

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“…This research redefines and simulates the intelligent surveillance application using distributed camera networks [28] to demonstrate the effectiveness of the proposed scheme. For the better understanding and representation of application components in a distributed computing environment, Distributed Data Flow Model (DDF) [29] was used in this research for the deployment of applications on the fogcloud paradigm. Figure 2 presents the directed acyclic graph (DAG) of the intelligent surveillance application based on a distributed network of cameras deployed for the evaluation of the proposed algorithm.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

Hassan

Ahmad

Rehman

et al. 2022

Wireless Communications and Mobile Computing

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The architecture employed by most of the researchers for the deployment of latency-sensitive Internet of Things (IoT) applications is fog computing. Fog computing architecture offers less delay as compared to the cloud computing paradigm by providing resource constraint fog devices close to the edge of the network. Fog nodes process the incoming data by utilizing available resources which reduces the volume of data to be sent to the cloud server. Fog devices having dissimilar processing capabilities are present in a system. The connection of suitable sensor nodes to the parent fog node plays an essential role in achieving the optimum performance of the system. In this paper, we have designed an algorithm that dynamically assigns appropriate sensor devices to fog nodes to achieve a reduction in network utilization and latency. The proposed algorithm estimates the volume of information detected by an edge device from the rate of sensing frequency of the sensor attached to the edge device. The proposed policy while connecting the network nodes takes into account the heterogeneity and processing capability of the devices. Several evaluations are performed on multiple scales for the evaluation of the proposed algorithm. The outcomes of the evaluations confirm the effectiveness of the proposed algorithm in achieving a reduction in network consumption and end-to-end delay.

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Quality attributes in edge computing for the Internet of Things: A systematic mapping study

Cited by 21 publications

References 60 publications

Non-functional requirements elicitation for edge computing

Non-functional requirements elicitation for edge computing

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

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