Time and resource constrained offloading with multi-task in a mobile edge computing node

Ghmary, Mohamed El; Hmimz, Youssef; Chanyour, Tarik; Malki, Mohammed Ouçamah Cherkaoui

doi:10.11591/ijece.v10i4.pp3757-3766

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…Before transmission, data must be filtered and compressed to an ideal size. 3) Edge IT data processing [67], [94], [100], [109], [122], [133], [140], [168]- [170], [216], [220]- [223] Before reaching the cloud center, the digitized and aggregated IoT data is processed further. Edge devices do sophisticated analytics and preprocessing, which may include machine learning and visual representation.…”

Section: Architecture Stagesmentioning

confidence: 99%

“…One way that IoT edge computing may be used to protect humans is to automatically shut down machines when someone enters a prohibited area at a factory. Autonomous cars require data in order to make critical real-time decisions that might mean the difference between life and death on the road [24], [26], [67], [100], [109], [133], [145], [184], [186], [216], [220], [221], [267], [268], [293], [297], [335].…”

Section: Edge Computing: Low Latency and Securitymentioning

confidence: 99%

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Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

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Section: Architecture Stagesmentioning

confidence: 99%

Section: Edge Computing: Low Latency and Securitymentioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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“…The researchers [10] looked at three different types of offloading decisions including partial offloading, complete offloading, as well as local execution. The goal of this study is to optimize computational resources and decrease the amount of time needed to complete an independent task within the MEC system.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Multi-User Offloading in Mobile Edge Computing

Moussammi¹,

Ghmary²,

Idrissi³

2022

IJACSA

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Mobile Edge Computing (MEC) is a new method to overcome the resource limitations of mobile devices by enabling Computation Offloading (CO) with low latency. This paper proposes a multi-user multi-task effective system to offload computations for MEC that guarantees in terms of energy, latency for MEC. To begin, radio and computation resources are integrated to ensure the efficient utilization of shared resources when there are multiple users. The energy consumed is positively correlated with the power of transmission and the local CPU frequency. The values can be adjusted to accommodate multi-tasking in order to minimize the amount of energy consumed. The current methods for offloading aren't appropriate when multiple tasks and multiple users have high computing density. Additionally, this paper proposes a multi-user system that includes multiple tasks and high-density computing that is efficient. Simulations have confirmed the Multi-User Multi-Task Offloading Algorithm (MUMTOD). The results in terms of execution time and energy consumption are extremely positive. This improves the effectiveness of offloading as well as reducing energy consumption.

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“…The edge servers manage several edge devices and gateway by handling the context data. It is a generic term that captures associated computing paradigms such as fog computing, cloudlet, or mobile access edge computing [2].…”

Section: Introductionmentioning

confidence: 99%

A data quarantine model to secure data in edge computing

Mahadevappa

Murugesan

2022

IJECE

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<p>Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.</p>

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Time and resource constrained offloading with multi-task in a mobile edge computing node

Cited by 8 publications

References 22 publications

Insights on the internet of things: past, present, and future directions

Insights on the internet of things: past, present, and future directions

Multi-Task Multi-User Offloading in Mobile Edge Computing

A data quarantine model to secure data in edge computing

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