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Intelligent Cloud, Fog, and Edge Computing in the Internet of Things (IoT)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2884

Special Issue Editors


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Guest Editor
Department of Computer Science, The University of Sheffield, Sheffield S10 2TN, UK
Interests: multimodal perception; ubiquitous computing; mobile sensing; applied machine learning; indoor positioning system; Wi-Fi

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Guest Editor
Faculty of Automatics and Computers, University Politehnica of Bucharest, Bucuresti, Romania
Interests: wireless sensor networks; wearables

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is not limited to the cloud anymore, with many applications and services being distributed between the edge, the fog, and the cloud. This increasingly complex architecture is driven by privacy concerns, to avoid compromising sensitive data due to potential leaks when communicating on the internet, computation resource availability on the edge and in the fog, and latency–accuracy trade-off of inference models. It is not clear what the best design approach is, so research is needed to study this new computation paradigm in intelligent systems. This issue aims to establish best practice on these topics:

  • Distributed training of machine learning models in heterogeneous systems;
  • Efficient mapping of computations to the edge, fog, and cloud;
  • Adaptive offloading of computation to the fog and cloud;
  • Data-driven decision making in the cloud–fog–edge continuum;
  • Collaborative training and inference across distributed devices.

Dr. Valentin Radu
Dr. Emilian Radoi
Guest Editors

Manuscript Submission Information

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Keywords

  • cloud–fog–edge continuum
  • cloud and fog offloading
  • intelligent edge computing
  • distributed AI
  • partitioned training and inference

Published Papers (1 paper)

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Research

16 pages, 415 KiB  
Article
Digital-Twin-Assisted Edge-Computing Resource Allocation Based on the Whale Optimization Algorithm
by Shaoming Qiu, Jiancheng Zhao, Yana Lv, Jikun Dai, Fen Chen, Yahui Wang and Ao Li
Sensors 2022, 22(23), 9546; https://doi.org/10.3390/s22239546 - 6 Dec 2022
Cited by 8 | Viewed by 2459
Abstract
With the rapid increase of smart Internet of Things (IoT) devices, edge networks generate a large number of computing tasks, which require edge-computing resource devices to complete the calculations. However, unreasonable edge-computing resource allocation suffers from high-power consumption and resource waste. Therefore, when [...] Read more.
With the rapid increase of smart Internet of Things (IoT) devices, edge networks generate a large number of computing tasks, which require edge-computing resource devices to complete the calculations. However, unreasonable edge-computing resource allocation suffers from high-power consumption and resource waste. Therefore, when user tasks are offloaded to the edge-computing system, reasonable resource allocation is an important issue. Thus, this paper proposes a digital-twin-(DT)-assisted edge-computing resource-allocation model and establishes a joint-optimization function of power consumption, delay, and unbalanced resource-allocation rate. Then, we develop a solution based on the improved whale optimization scheme. Specifically, we propose an improved whale optimization algorithm and design a greedy initialization strategy to improve the convergence speed for the DT-assisted edge-computing resource-allocation problem. Additionally, we redesign the whale search strategy to improve the allocation results. Several simulation experiments demonstrate that the improved whale optimization algorithm reduces the resource allocation and allocation objective function value, the power consumption, and the average resource allocation imbalance rate by 12.6%, 15.2%, and 15.6%, respectively. Overall, the power consumption with the assistance of the DT is reduced to 89.6% of the power required without DT assistance, thus, improving the efficiency of the edge-computing resource allocation. Full article
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