RETRACTED ARTICLE: Resource Management and Task Scheduling for IoT using Mobile Edge Computing

Quasim, Mohammad Tabrez

doi:10.1007/s11277-021-09087-7

Cited by 13 publications

(12 citation statements)

References 26 publications

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“…The tasks are processed before they are assigned. With relation to the load and bandwidth of the cloud holdings, the allocation is performed using BAR optimization and associated BATS algorithms [17] investigates EE for combining BSas well as beamforming in multicell situations. Ref.…”

Section: Related Workmentioning

confidence: 99%

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Ahmed

Garg

Rai³

et al. 2022

Electronics

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For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

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Section: Related Workmentioning

confidence: 99%

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

Ahmed

Garg

Rai³

et al. 2022

Electronics

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“…Quasim, M.T. [16] employed a mobile edge computing-based resource management and task scheduling (MEC-RMTS) framework to deliver an efficient task-offloading-based data delivery mechanism with low latency and scalability for IoT-network-enabled smartphone handling. The developed module uses power usage (PU) to decrease energy consumption, providing a convex technique of gradient-dependent game model (GM) data acquisition.…”

Section: Related Workmentioning

confidence: 99%

“…Increasing user loads within IoT connections raises latency-based delays toward gateway endpoints [10][11][12]. When distributed processes have been merged and aligned to continue [13,14] through the network gateway, they occur due to delayed package delivery and inconsistent planning of processes for high memory gain at the edge of the network nodes [15,16].…”

Section: Introductionmentioning

confidence: 99%

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

et al. 2021

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The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

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“…The Edge AI paradigm is critical to undertake the processing of all big data generated for the applications being run by the ever growing amount of IoT devices [4], hence allowing these to obtain responses with much lower latency and a far smaller amount of bandwidth compared to those being obtained if cloud servers were used [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling an Edge Computing Arithmetic Framework for IoT Environments

Roig

Alcaraz

Gilly

et al. 2022

Sensors

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IoT environments are forecasted to grow exponentially in the coming years thanks to the recent advances in both edge computing and artificial intelligence. In this paper, a model of remote computing scheme is presented, where three layers of computing nodes are put in place in order to optimize the computing and forwarding tasks. In this sense, a generic layout has been designed so as to easily achieve communications among the diverse layers by means of simple arithmetic operations, which may result in saving resources in all nodes involved. Traffic forwarding is undertaken by means of forwarding tables within network devices, which need to be searched upon in order to find the proper destination, and that process may be resource-consuming as the number of entries in such tables grow. However, the arithmetic framework proposed may speed up the traffic forwarding decisions as relaying on integer divisions and modular arithmetic, which may result more straightforward. Furthermore, two diverse approaches have been proposed to formally describe such a design by means of coding with Spin/Promela, or otherwise, by using an algebraic approach with Algebra of Communicating Processes (ACP), resulting in a explosion state for the former and a specified and verified model in the latter.

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RETRACTED ARTICLE: Resource Management and Task Scheduling for IoT using Mobile Edge Computing

Cited by 13 publications

References 26 publications

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

Modeling an Edge Computing Arithmetic Framework for IoT Environments

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