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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: 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; cyber-physical systems; systems-on-chip; design automation; design methodology
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 and Collections in MDPI journals
Prof. Dr. Feng Zeng
E-Mail Website
Co-Guest Editor
School of Software, Central South University, Changsha, China
Interests: computer networks; mobile networks; network routing; Internet-of-Things; smart cities
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 2000 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 (2 papers)

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Research

Open AccessArticle
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
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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Open AccessArticle
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
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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