Joint Task Offloading and QoS-Aware Resource Allocation in Fog-Enabled Internet-of-Things Networks

Huang, Xiaoge; Cui, Yifan; Chen, Qianbin; Zhang, Jie

doi:10.1109/jiot.2020.2982670

Cited by 47 publications

(13 citation statements)

References 24 publications

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“…There are 20 IoT devices, 4 gateways, and 16 multiplexed channels. The simulation parameters are summarised in Table 2 [19,20,25]. The proposed algorithm is compared with two algorithms.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

et al. 2022

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Multi‐mode green internet of things (IoT) network that integrates multiple communication media can well meet data transmission and processing demands of low‐carbon smart building. However, network resource management optimisation including joint optimisation of gateway and channel selection still faces technical challenges such as differentiated quality of service (QoS) demand guarantee, coupling between optimisation problems with different timescales, and adversary caused by multi‐device competition. To address these challenges, an adversarial learning‐based multi‐timescale network resource management algorithm for multi‐mode green IoT is proposed. Specifically, the minimisation problem of weighted difference between energy consumption and throughput under the long‐term queuing delay constraints is formulated to achieve differentiated QoS guarantee. Large‐timescale gateway selection is decoupled from small‐timescale channel selection by establishing matching preferences based on empirical performance, and optimised by using bilateral matching with quota. Finally, an exponential‐weight algorithm for exploration and exploitation (EXP3)‐based small‐timescale channel selection algorithm is proposed to achieve adversary awareness. Simulation results demonstrate that compared with asynchronous greedy matching algorithm and auction‐based many‐to‐many matching algorithm, the proposed algorithm performs superior in terms of energy consumption and throughput.

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“…There are 20 IoT devices, 4 gateways, and 16 multiplexed channels. The simulation parameters are summarised in Table 2 [19,20,25]. The proposed algorithm is compared with two algorithms.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…In ref. [20], a QoS‐aware many‐to‐one matching game was proposed to optimise channel selection between devices and base stations. Nevertheless, the multi‐timescale resource optimisation as well as the impact of small‐timescale channel selection on large‐timescale gateway selection are neglected in those works.…”

Section: Introductionmentioning

confidence: 99%

Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

et al. 2022

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“…Simulation results show that the proposed method effectively acquires matching schemes in acceptable processing times. A similar method is proposed in Huang et al 121 They proposed a QoS‐aware task offloading and resource allocation method to minimize processing delay and energy consumption under QoS constraints. For this purpose, AHP is used to prioritize QoS preferences, and a bilateral matching game is used to associate fog devices with IoT devices.…”

Section: Delivering Qos In Iot Networkmentioning

confidence: 99%

QoS‐aware IoT networks and protocols: A comprehensive survey

Dilek

Irgan

Guzel

et al. 2022

Int J Communication

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Summary The vast number of smart cloud applications that communicate with the “things” over a variety of physical networks and communication protocols contribute to the rise of complexity in Internet of Things (IoT) systems. The diversity of end‐user requirements related to the volume of generated data, its variety, and the velocity of its transmission makes quality of service (QoS) provisioning even more crucial and challenging in IoT. This paper provides a comprehensive and up‐to‐date survey of QoS support in IoT networks and communication protocols. An analysis of the QoS‐aware IoT architectures, layer‐dependent QoS metrics, and network resource optimization methods utilized in IoT systems are given. The limitations of the current state‐of‐the‐art studies for efficient delivery of QoS metrics are discussed. The paper concludes with future research directions on end‐to‐end QoS provisioning in IoT.

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“…The main aim is always to reduce energy consumption and task completion time. An important issue in IoT networks is quality of service, it has requirements like transmission rate, delay, throughput, and bit error rate 16 . The problem with the IoT‐fog environment is the exponential growth of smart devices.…”

Section: Introductionmentioning

confidence: 99%

“…An important issue in IoT networks is quality of service, it has requirements like transmission rate, delay, throughput, and bit error rate. 16 The problem with the IoT-fog environment is the exponential growth of smart devices. Thus, it is challenging for them to process this huge data through conventional greedy methods.…”

mentioning

confidence: 99%

e‐TOALB: An efficient task offloading in IoT‐fog networks

Lone,

Sofi

2023

Concurrency and Computation

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SummarySmart devices are concerned about the processing and computation of tasks due to their tiny nature. They prefer to offload their tasks to the cloud for processing and computation. Due to the huge amount of data being generated by smart devices, the cloud becomes inefficient in terms of huge delay. Thus, Processing tasks in the cloud can add latency and finally needs to be addressed. Thus, fog computing is an alternative to the latency issue. The tasks are offloaded to fog instead of the cloud. In this paper, e‐TOALB (enhanced task offloading and load balancing), a modified and enhanced nature‐inspired and meta‐heuristic ant colony optimization is used to offload tasks in a fog environment. The results obtained by the proposed method are compared with Particle swarm optimization (PSO), round robin (RR), and ant colony optimization. The numerical results clearly show an improvement in average response time and load sharing among all fog nodes. The results of the proposed model produce low response time, low average service time, and low standard deviation. The proposed scheme aims to find the best possible decision for offloading tasks to nearby fog devices and to find an optimal route for offloading with the least communication cost and average service time.

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Joint Task Offloading and QoS-Aware Resource Allocation in Fog-Enabled Internet-of-Things Networks

Cited by 47 publications

References 24 publications

Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

QoS‐aware IoT networks and protocols: A comprehensive survey

e‐TOALB: An efficient task offloading in IoT‐fog networks

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