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Computational Intelligence Powered Edge Computing for Internet of Things
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Computational Intelligence Powered Edge Computing for Internet of Things

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 11105

Special Issue Editors

Dr. Ali Safaa Sadiq

E-Mail Website
Guest Editor
School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1LY, UK
Interests: wireless communication; AI applications in networking and security; IoT and IoV
Special Issues, Collections and Topics in MDPI journals
Dr. Khaled Rabie

E-Mail Website
Guest Editor
Department of Engineering, Manchester Metropolitan University, Manchester M15GD, UK
Interests: wireless communications; IoT; 6G; smart manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Seyed Mohammad Mirjalili

E-Mail Website
Guest Editor
Department of Engineering Physics, Polytechnique Montréal, Montreal, QC H3C 3A7, Canada
Interests: photonics; electronics and optoelectronics devices; fiber-optic communication; photonic crystals; engineering optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Kayhan Zrar Ghafoor

E-Mail
Guest Editor
Department of Software Engineering, Salahaddin University-Erbil, Iraq
Interests: vehicle-to-everything (V2X), Internet of Things; cybersecurity; smart city
Dr. Shahrzad Saremi

E-Mail
Guest Editor
Torrense University, Australia
Interests: augmented reality; gesture recognition; human–computer interaction; optimization; information technology

Special Issue Information

Dear Colleagues,

In the last two decades, the field of computational intelligence (CI) has become very popular in both science and industry. As one of the fastest growing subfields of artificial intelligence, a wide range of problem solving techniques have been proposed so far, inspired from nature. On the other hand, the fast growth along with the deployment of fixed and mobile devices/machines that are connected to Internet has formulated the recently emerged concept of Internet of Things (IoT). Such interconnected billions of things that continuously produce data at high volumes and velocity have been pushing towards a new paradigm called edge computing. Edge computing has been widely known as a clever way out to fulfill the requirements of low latency, high scalability, High security and energy efficiency, in addition to producing less network traffic load. Nevertheless, with the development of various IoT applications (e.g., smart home, smart city, industrial automation, connected vehicles), it tends to be challenging for edge computing to deal with such heterogeneity in IoT environments. Therefore, CI along with its algorithms and application has provided a wide spectrum of promising solutions to empower edge computing for IoT. Such CI techniques are divided into three classes of artificial neural networks, evolutionary computation, and fuzzy logic.

The scope of the special issue includes, but is not limited to, the following topics:

  • Neural Networks and its applications for Edge CI
  • Resource Management for Edge CI
  • Big Data Analytics for Edge CI
  • 5G-enabled services for Edge CI
  • Security and Privacy Issues for IoT based CI
  • Deep learning for IoT Edge CI
  • Self-learning systems for IoT Edge CI
  • Supervised and unsupervised learning methods powered Edge computing in Internet of Connected Vehicles
  • Evolutionary Algorithm for IoT Edge CI
  • Meta-heuristics Algorithms for IoT Edge CI
  • Swarm Intelligence for IoT Edge CI
  • Hybrid intelligent systems for IoT Edge CI
  • Fuzzy logic for IoT Edge CI
  • Neuro-fuzzy systems for IoT Edge CI

Dr. Ali Safaa Sadiq
Dr. Khaled Rabie
Dr. Seyed Mohammad Mirjalili
Dr. Kayhan Zrar Ghafoor
Dr. Shahrzad Saremi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

17 pages, 3923 KiB  
Article
Auto-Tiler: Variable-Dimension Autoencoder with Tiling for Compressing Intermediate Feature Space of Deep Neural Networks for Internet of Things
by Jeongsoo Park, Jungrae Kim and Jong Hwan Ko
Sensors 2021, 21(3), 896; https://doi.org/10.3390/s21030896 - 29 Jan 2021
Cited by 3 | Viewed by 3115
Abstract
Due to limited resources of the Internet of Things (IoT) edge devices, deep neural network (DNN) inference requires collaboration with cloud server platforms, where DNN inference is partitioned and offloaded to high-performance servers to reduce end-to-end latency. As data-intensive intermediate feature space at [...] Read more.
Due to limited resources of the Internet of Things (IoT) edge devices, deep neural network (DNN) inference requires collaboration with cloud server platforms, where DNN inference is partitioned and offloaded to high-performance servers to reduce end-to-end latency. As data-intensive intermediate feature space at the partitioned layer should be transmitted to the servers, efficient compression of the feature space is imperative for high-throughput inference. However, the feature space at deeper layers has different characteristics than natural images, limiting the compression performance by conventional preprocessing and encoding techniques. To tackle this limitation, we introduce a new method for compressing DNN intermediate feature space using a specialized autoencoder, called auto-tiler. The proposed auto-tiler is designed to include the tiling process and provide multiple input/output dimensions to support various partitioned layers and compression ratios. The results show that auto-tiler achieves 18% to 67% higher percent point accuracy compared to the existing methods at the same bitrate while reducing the process latency by 73% to 81%. The dimension variability of an auto-tiler also reduces the storage overhead by 62% with negligible accuracy loss. Full article
(This article belongs to the Special Issue Computational Intelligence Powered Edge Computing for Internet of Things)
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17 pages, 2829 KiB  
Article
Optimization of Time Synchronization and Algorithms with TDOA Based Indoor Positioning Technique for Internet of Things
by Kun Zhao, Tiantian Zhao, Zhengqi Zheng, Chao Yu, Difeng Ma, Khaled Rabie and Rupak Kharel
Sensors 2020, 20(22), 6513; https://doi.org/10.3390/s20226513 - 14 Nov 2020
Cited by 23 | Viewed by 3064
Abstract
To provide high-precision positioning for Internet of Things (IoT) scenarios, we optimize the indoor positioning technique based on Ultra-Wideband (UWB) Time Difference of Arrival (TDOA) equipment. This paper analyzes sources of positioning error and improves the time synchronization algorithm based on the synchronization [...] Read more.
To provide high-precision positioning for Internet of Things (IoT) scenarios, we optimize the indoor positioning technique based on Ultra-Wideband (UWB) Time Difference of Arrival (TDOA) equipment. This paper analyzes sources of positioning error and improves the time synchronization algorithm based on the synchronization packet. Then we use the labels of the known position to further optimize the time synchronization performance, and hence improve TDOA measurements. After time synchronization optimization, a Weighted Least Square (WLS) and Taylor coordination algorithm is derived. Experiments show that our optimization reduces the average positioning error from 54.8 cm to 12.6 cm. Full article
(This article belongs to the Special Issue Computational Intelligence Powered Edge Computing for Internet of Things)
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29 pages, 6813 KiB  
Article
A Holistic Systems Security Approach Featuring Thin Secure Elements for Resilient IoT Deployments
by Soodamani Ramalingam, Hock Gan, Gregory Epiphaniou and Emilio Mistretta
Sensors 2020, 20(18), 5252; https://doi.org/10.3390/s20185252 - 14 Sep 2020
Cited by 7 | Viewed by 4057
Abstract
IoT systems differ from traditional Internet systems in that they are different in scale, footprint, power requirements, cost and security concerns that are often overlooked. IoT systems inherently present different fail-safe capabilities than traditional computing environments while their threat landscapes constantly evolve. Further, [...] Read more.
IoT systems differ from traditional Internet systems in that they are different in scale, footprint, power requirements, cost and security concerns that are often overlooked. IoT systems inherently present different fail-safe capabilities than traditional computing environments while their threat landscapes constantly evolve. Further, IoT devices have limited collective security measures in place. Therefore, there is a need for different approaches in threat assessments to incorporate the interdependencies between different IoT devices. In this paper, we run through the design cycle to provide a security-focused approach to the design of IoT systems using a use case, namely, an intelligent solar-panel project called Daedalus. We utilise STRIDE/DREAD approaches to identify vulnerabilities using a thin secure element that is an embedded, tamper proof microprocessor chip that allows the storage and processing of sensitive data. It benefits from low power demand and small footprint as a crypto processor as well as is compatible with IoT requirements. Subsequently, a key agreement based on an asymmetric cryptographic scheme, namely B-SPEKE was used to validate and authenticate the source. We find that end-to-end and independent stand-alone procedures used for validation and encryption of the source data originating from the solar panel are cost-effective in that the validation is carried out once and not several times in the chain as is often the case. The threat model proved useful not so much as a panacea for all threats but provided the framework for the consideration of known threats, and therefore appropriate mitigation plans to be deployed. Full article
(This article belongs to the Special Issue Computational Intelligence Powered Edge Computing for Internet of Things)
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