Towards Convergence of Internet of Things and Cyber-Physical Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 25479

Special Issue Editors


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Guest Editor
School of Geomatic Engineering, Technical University of Madrid, 28040 Madrid, Spain
Interests: service composition; prosumer; VGI; machine learning; Internet of Things; blockchain
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Information Systems, Universidad Politécnica de Madrid, 28031 Madrid, Spain
Interests: cybersecurity; blockchain; cyberphysical systems; 5G; Industry 4.0

Special Issue Information

Dear Colleagues,

Cyberphysical systems (CPS) are computer systems in which a mechanism is controlled or monitored by computer-based algorithms. Physical and software components are deeply intertwined, and their interaction describes many of the challenges from various fields including smart grids, autonomous automobile systems, medical monitoring, industrial control systems, and robotics systems, among others.

This concept produces synergies with the more established concept of the Internet of Things, describing the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet.

Currently, both concepts have evolved towards a convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems in addition to the traditional fields of wireless sensor networks, control systems, automation (including home and building automation).

This Special Issue aims to cover the most recent technical advances in CPS and IoT aspects, including theory, tools, applications, systems, testbeds, and field deployments, within a unified perspective comprising interacting logical, physical, and human components engineered for function through integrated logic and physics.

This Special Issue especially welcomes proposals of hybrid discrete and continuous methods and engineering foundations for CPS/IoT design, operation, and assurance considering the tight logical–physical linkage as the basis for the transformational nature of CPS/IoT systems.

Appropriate topics include, but are not limited to:

CPS/IoT in environmental monitoring

CPS/IoT in healthcare

CPS/IoT in smart environments (home, offices, cities)

CPS/IoT in transportation and logistics (automotive)

CPS/IoT enabling technologies and semantic web technologies

CPS/IoT and cloud computing/big data

Testing techniques for CPS/IoT

Formal verification techniques for CPS/IoT

Middleware and service development for CPS/IoT

Tools and infrastructure for CPS/IoT testing, analysis, or verification

Using simulation to support testing for CPS/IoT

As-a-service for CPS/IoT

Modeling techniques for CPS/IoT to support testing and verification

Dr. Ramon Alcarria
Dr. Borja Bordel
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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

  • Internet of Things (IoT)
  • cybersecurity
  • cyberphysical systems
  • mobile and cloud computing
  • big data
  • machine learning

Published Papers (4 papers)

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Research

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18 pages, 1722 KiB  
Article
A Fairness-Aware Peer-to-Peer Decentralized Learning Framework with Heterogeneous Devices
by Zheyi Chen, Weixian Liao, Pu Tian, Qianlong Wang  and Wei Yu
Future Internet 2022, 14(5), 138; https://doi.org/10.3390/fi14050138 - 30 Apr 2022
Cited by 6 | Viewed by 2833
Abstract
Distributed machine learning paradigms have benefited from the concurrent advancement of deep learning and the Internet of Things (IoT), among which federated learning is one of the most promising frameworks, where a central server collaborates with local learners to train a global model. [...] Read more.
Distributed machine learning paradigms have benefited from the concurrent advancement of deep learning and the Internet of Things (IoT), among which federated learning is one of the most promising frameworks, where a central server collaborates with local learners to train a global model. The inherent heterogeneity of IoT devices, i.e., non-independent and identically distributed (non-i.i.d.) data, and the inconsistent communication network environment results in the bottleneck of a degraded learning performance and slow convergence. Moreover, most weight averaging-based model aggregation schemes raise learning fairness concerns. In this paper, we propose a peer-to-peer decentralized learning framework to tackle the above issues. Particularly, each local client iteratively finds a learning pair to exchange the local learning model. By doing this, multiple learning objectives are optimized to advocate for learning fairness while avoiding small-group domination. The proposed fairness-aware approach allows local clients to adaptively aggregate the received model based on the local learning performance. The experimental results demonstrate that the proposed approach is capable of significantly improving the efficacy of federated learning and outperforms the state-of-the-art schemes under real-world scenarios, including balanced-i.i.d., unbalanced-i.i.d., balanced-non.i.i.d., and unbalanced-non.i.i.d. environments. Full article
(This article belongs to the Special Issue Towards Convergence of Internet of Things and Cyber-Physical Systems)
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29 pages, 8636 KiB  
Article
Ambalytics: A Scalable and Distributed System Architecture Concept for Bibliometric Network Analyses
by Klaus Kammerer, Manuel Göster, Manfred Reichert and Rüdiger Pryss
Future Internet 2021, 13(8), 203; https://doi.org/10.3390/fi13080203 - 4 Aug 2021
Cited by 2 | Viewed by 2962
Abstract
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can [...] Read more.
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability. Full article
(This article belongs to the Special Issue Towards Convergence of Internet of Things and Cyber-Physical Systems)
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21 pages, 997 KiB  
Article
Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
by Xiaohui Li and Andrey V. Savkin
Future Internet 2021, 13(7), 174; https://doi.org/10.3390/fi13070174 - 2 Jul 2021
Cited by 47 | Viewed by 6830
Abstract
As a typical cyber-physical system, networked unmanned aerial vehicles (UAVs) have received much attention in recent years. Emerging communication technologies and high-performance control methods enable networked UAVs to operate as aerial sensor networks to collect more complete and consistent information with significantly improved [...] Read more.
As a typical cyber-physical system, networked unmanned aerial vehicles (UAVs) have received much attention in recent years. Emerging communication technologies and high-performance control methods enable networked UAVs to operate as aerial sensor networks to collect more complete and consistent information with significantly improved mobility and flexibility than traditional sensing platforms. One of the main applications of networked UAVs is surveillance and monitoring, which constitute essential components of a well-functioning public safety system and many industrial applications. Although the existing literature on surveillance and monitoring UAVs is extensive, a comprehensive survey on this topic is lacking. This article classifies publications on networked UAVs for surveillance and monitoring using the targets of interest and analyzes several typical problems on this topic, including the control, navigation, and deployment optimization of UAVs. The related research gaps and future directions are also presented. Full article
(This article belongs to the Special Issue Towards Convergence of Internet of Things and Cyber-Physical Systems)
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Review

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25 pages, 2331 KiB  
Review
Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions
by Cheng Qian, Xing Liu, Colin Ripley, Mian Qian, Fan Liang and Wei Yu
Future Internet 2022, 14(2), 64; https://doi.org/10.3390/fi14020064 - 21 Feb 2022
Cited by 50 | Viewed by 11379
Abstract
The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical [...] Read more.
The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical systems can be monitored and controlled effectively. Along with the development of IoT and data analysis technologies, a number of CPS (smart grid, smart transportation, smart manufacturing, smart cities, etc.) adopt IoT and data analysis technologies to improve their performance and operations. Nonetheless, directly manipulating or updating the real system has inherent risks. Thus, creating a digital clone of a real physical system, denoted as a Digital Twin (DT), is a viable strategy. Generally speaking, a DT is a data-driven software and hardware emulation platform, which is a cyber replica of physical systems. Meanwhile, a DT describes a specific physical system and tends to achieve the functions and use cases of physical systems. Since DT is a complex digital system, finding a way to effectively represent a variety of things in timely and efficient manner poses numerous challenges to the networking, computing, and data analytics for IoT. Furthermore, the design of a DT for IoT systems must consider numerous exceptional requirements (e.g., latency, reliability, safety, scalability, security, and privacy). To address such challenges, the thoughtful design of DTs offers opportunities for novel and interdisciplinary research efforts. To address the aforementioned problems and issues, in this paper, we first review the architectures of DTs, data representation, and communication protocols. We then review existing efforts on applying DT into IoT data-driven smart systems, including the smart grid, smart transportation, smart manufacturing, and smart cities. Further, we summarize the existing challenges from CPS, data science, optimization, and security and privacy perspectives. Finally, we outline possible future research directions from the perspectives of performance, new DT-driven services, model and learning, and security and privacy. Full article
(This article belongs to the Special Issue Towards Convergence of Internet of Things and Cyber-Physical Systems)
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