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Special Issue "Computing and Networking in Internet-of-Things and Cyber-Physical Systems"

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

Deadline for manuscript submissions: closed (31 May 2020).

Special Issue Editors

Prof. Dr. Hiroyuki Tomiyama
E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Japan
Interests: embedded systems; cyberphysical systems; systems-on-a-chip; design automation; design methodology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lin Meng
E-Mail Website
Co-Guest Editor
Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Japan
Interests: processor architecture; high-performance computing; AI-based IoT; underwater drones; cultural heritage preservation and protection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Feng Zeng
E-Mail Website
Co-Guest Editor
School of Computer Science and Engineering, Central South University, Changsha 410083, China
Interests: wireless network; edge computing; artificial intelligence; big data processing; software engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhangbing Zhou
E-Mail Website1 Website2
Co-Guest Editor
School of Information Engineering, China University of Geosciences (Beijing), China
Interests: computer networks; mobile networks; network routing; Internet-of-Things; service computing; fog computing

Special Issue Information

Dear Colleagues,

Physical-world devices and cyber-world computers are being tightly integrated with each other through the Internet in order to realize high-quality and human-friendly systems, so-called cyber-physical Systems (CPS). Internet of Things (IoT) technology is one of the key enablers of CPS, and IoT is in turn realized by a variety of computing and networking technologies. This Special Issue is devoted to advanced computing and networking technologies to enable intelligent and efficient IoT and CPS. 

This Special Issue will be an open call but also invites selected papers from the Second International Symposium on Advanced Technologies and Applications in the Internet of Things (ATAIT 2019).

Prof. Dr. Hiroyuki Tomiyama
Prof. Dr. Lin Meng
Prof. Dr. Feng Zeng
Prof. Dr. Zhangbing Zhou
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 papers will be 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 2400 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.

Keywords

  • Processors, GPUs, and FPGAs for IoT and CPS
  • Embedded software for IoT and CPS
  • IoT and CPS networks
  • Edge and fog computing for IoT and CPS
  • Security and privacy in IoT and CPS
  • Data analysis, computer vision, and machine learning in IoT and CPS
  • Mechatronics and robotics in IoT and CPS
  • Emerging IoT and CPS applications
  • Culture heritage protection in IoT and CPS

Published Papers (4 papers)

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Research

Article
A Panorama of Cloud Platforms for IoT Applications Across Industries
Sensors 2020, 20(9), 2701; https://doi.org/10.3390/s20092701 - 09 May 2020
Cited by 4 | Viewed by 1040
Abstract
Internet of Things (IoT) applications can play a critical role in business and industry. Industrial IoT (IIoT) refers to the use of IoT technologies in manufacturing. Enabling IIoT applications in cloud environments requires the design of appropriate IIoT Platform as-a-Service (IIoT PaaS) to [...] Read more.
Internet of Things (IoT) applications can play a critical role in business and industry. Industrial IoT (IIoT) refers to the use of IoT technologies in manufacturing. Enabling IIoT applications in cloud environments requires the design of appropriate IIoT Platform as-a-Service (IIoT PaaS) to support and ease their provisioning (i.e., development, deployment and management). This paper critically reviews the IIoT PaaS architectures proposed so far in the relevant literature. It only surveys the architectures that are suitable for IIoT applications provisioning and it excludes regular IoT solutions from its scope. The evaluation is based on a set of well-defined architectural requirements. It also introduces and discusses the future challenges and the research directions. The critical review discusses the PaaS solutions that focus on the whole spectrum of IoT verticals and also the ones dealing with specific IoT verticals. Existing limitations are identified and hints are provided on how to tackle them. As critical research directions, the mechanisms that enable the secure provisioning, and IIoT PaaS interaction with virtualized IoT Infrastructure as-a-Service (IaaS) and fog computing layer are discussed. Full article
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Article
Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems
Sensors 2020, 20(3), 844; https://doi.org/10.3390/s20030844 - 05 Feb 2020
Cited by 2 | Viewed by 1476
Abstract
Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed [...] Read more.
Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed in IoT applications, the implementation of hardware security is crucial. The identity of electronic circuits measured in terms of device parameters serves as a fingerprint. Estimating the parameters of this fingerprint assists the identification and prevention of Trojan attacks in a CPS. We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods. The bio-inspired principles of pattern identification use a Spiking Neural Network (SNN), and glial cells form the basis of this work. When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicating the presence of a Trojan. This network is tunable to accommodate natural variations in device parameters and to avoid false triggering of Trojan alerts. The tolerance is tuned using bio-inspired principles for various security requirements, such as forming high-alert systems for safety-critical missions. The Trojan detection circuit is resilient to a range of faults and attacks, both intentional and unintentional. Also, we devise a design-for-trust architecture by developing a bio-inspired device-locking mechanism. The proposed architecture is implemented on a Xilinx Artix-7 Field Programmable Gate Array (FPGA) device. Results demonstrate the suitability of the proposal for resource-constrained environments with minimal hardware and power dissipation profiles. The design is tested with a wide range of device parameters to demonstrate the effectiveness of Trojan detection. This work serves as a new approach to enable secure CPSs and to employ bio-inspired unsupervised machine intelligence. Full article
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Article
Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services
Sensors 2020, 20(1), 122; https://doi.org/10.3390/s20010122 - 24 Dec 2019
Cited by 7 | Viewed by 1228
Abstract
The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since [...] Read more.
The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy. Full article
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Article
Optimal Offloading Decision Strategies and Their Influence Analysis of Mobile Edge Computing
Sensors 2019, 19(14), 3231; https://doi.org/10.3390/s19143231 - 23 Jul 2019
Cited by 4 | Viewed by 1455
Abstract
Mobile edge computing (MEC) has become more popular both in academia and industry. Currently, with the help of edge servers and cloud servers, it is one of the substantial technologies to overcome the latency between cloud server and wireless device, computation capability and [...] Read more.
Mobile edge computing (MEC) has become more popular both in academia and industry. Currently, with the help of edge servers and cloud servers, it is one of the substantial technologies to overcome the latency between cloud server and wireless device, computation capability and storage shortage of wireless devices. In mobile edge computing, wireless devices take responsibility with input data. At the same time, edge servers and cloud servers take charge of computation and storage. However, until now, how to balance the power consumption of edge devices and time delay has not been well addressed in mobile edge computing. In this paper, we focus on strategies of the task offloading decision and the influence analysis of offloading decisions on different environments. Firstly, we propose a system model considering both energy consumption and time delay and formulate it into an optimization problem. Then, we employ two algorithms—Enumerating and Branch-and-Bound—to get the optimal or near-optimal decision for minimizing the system cost including the time delay and energy consumption. Furthermore, we compare the performance between two algorithms and draw the conclusion that the comprehensive performance of Branch-and-Bound algorithm is better than that of the other. Finally, we analyse the influence factors of optimal offloading decisions and the minimum cost in detail by changing key parameters. Full article
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