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Advance Tools and Techniques for Edge Computing in Dynamic Internet of Things Environment

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

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 4030

Special Issue Editors


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Guest Editor
School of Engineering, National University of Ireland, H91 TK33 Galway, Ireland
Interests: ML & XAI; digital twin and industrial IoT; blockchain for energy and health

grade E-Mail Website
Guest Editor
Department of Computing and Mathematics, The Manchester Metropolitan University, Manchester, UK
Interests: Internet of Things/cyber physical systems; wireless communication; cyber security; smart infrastructures; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The explosion of data necessitates an increase in processing at the edge for reasons such as latency, bandwidth savings, security, privacy, and autonomy. However, because edge computing is inherently distributed, has low computational power, and has a heterogeneous landscape, energy optimization and dynamic load distribution are major concerns for the research community. Edge computing devices are fundamentally low computational devices; however, in the dynamic IoT environment, they may perform high-processing tasks, such as ML model restraining in real-time, automatic data annotation, and so on. Distributed resource allocation is an important solution for transferring their extra loads to similar network edges. In such a scenario, all edge nodes in the same network should be able to share their computational resources in order to complete any high-processing task. In addition, their heterogeneous nature also requires more advanced methods and tools, such as adaptive approaches. For scale edge computing, more energy-efficient distributed edge computing approaches are required. We must counteract this complexity by supporting a variety of deployment energy-efficient resource allocation models in a more standardized and open manner, while also allowing legacy systems to be used.

Dr. Syed Muslim Jameel
Prof. Dr. Ali Kashif Bashir
Guest Editors

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Keywords

  • adaptive machine learning
  • energy optimization
  • dynamic load distribution
  • dynamic Internet of Things

Published Papers (2 papers)

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Research

22 pages, 457 KiB  
Article
Risk-Aware Distributionally Robust Optimization for Mobile Edge Computation Task Offloading in the Space–Air–Ground Integrated Network
by Zhiyuan Li and Pinrun Chen
Sensors 2023, 23(12), 5729; https://doi.org/10.3390/s23125729 - 20 Jun 2023
Cited by 1 | Viewed by 932
Abstract
As an emerging network paradigm, the space–air–ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortage of computing and [...] Read more.
As an emerging network paradigm, the space–air–ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortage of computing and storage resources in mobile devices greatly impacts the quality of experiences for intelligent applications. Hence, we plan to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MECs). To facilitate efficient processing, we need to solve the optimal task offloading decisions. In contrast to existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capabilities for edge computing nodes, the uncertainty of transmission latency caused by heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we first describe the task offloading decision problem in environments characterized by these new challenges. However, we cannot use standard robust optimization and stochastic optimization methods to obtain optimal results under uncertain network environments. In this paper, we propose the ‘condition value at risk-aware distributionally robust optimization’ algorithm for task offloading, denoted as RADROO, to solve the task offloading decision problem. RADROO combines the distributionally robust optimization and the condition value at risk model to achieve optimal results. We evaluated our approach in simulated SAGIN environments, considering confidence intervals, the number of mobile task offloading instances, and various parameters. We compare our proposed RADROO algorithm with state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the Brute algorithm. The experimental results show that RADROO can achieve a sub-optimal mobile task offloading decision. Overall, RADROO is more robust than others to the new challenges mentioned above in SAGIN. Full article
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16 pages, 2251 KiB  
Article
A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer
by Reyazur Rashid Irshad, Shahid Hussain, Shahab Saquib Sohail, Abu Sarwar Zamani, Dag Øivind Madsen, Ahmed Abdu Alattab, Abdallah Ahmed Alzupair Ahmed, Khalid Ahmed Abdallah Norain and Omar Ali Saleh Alsaiari
Sensors 2023, 23(6), 2932; https://doi.org/10.3390/s23062932 - 08 Mar 2023
Cited by 11 | Viewed by 2169
Abstract
Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks [...] Read more.
Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor’s judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models. Full article
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