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Smart Cyberphysical Systems and Cloud–Edge Engineering
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Smart Cyberphysical Systems and Cloud–Edge Engineering

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 15396

Special Issue Editors

Prof. Dr. Flavia C. Delicato

E-Mail Website
Guest Editor
Department of Computer Science, Fluminense Federal University, Niteroi 24210-310, RJ, Brazil
Interests: Internet of Things; middleware; edge computing; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo F. Pires

E-Mail Website
Guest Editor
Fluminense Federal University, Brazil
Interests: IoT sytems; cloud/edge computing; software architecture; model-based software development
Dr. Kevin I-Kai Wang

E-Mail Website
Guest Editor
Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1010, New Zealand
Interests: IoT-based ambient intelligence; pervasive healthcare systems; human activity recognition; predictive data analytics and bio-cybernetic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, computers, smart sensors and devices are pervasive, and computational processes are increasingly becoming transparent to humans, making up the very fabric of contemporary societies. In this context, the cyberphysical system (CPS) paradigm is thriving. CPS refers to the integration of computation with physical processes. A CPS uses sensors and actuators to link the computational systems to the physical world. Through the instrumentation of the physical world, it is possible to monitor several variables and transfer data to cyberspace, where applications and services use such data to make decisions that affect and control physical processes, in a feedback loop. The ultimate goal of this integration is to improve processes, making them more effective and optimized, increasing productivity for companies and quality of life for citizens.

The recent advancement of computational intelligence techniques has leveraged the new generation of CPS, which is known as smart cyberphysical systems (sCPS). sCPS are distributed and software-intensive systems that, from heterogeneous data sources, both physical and virtual, and from their processing using computational intelligence paradigms, are able to efficiently and autonomously manage real-world processes. Such systems have the potential to optimize and support a wide range of application domains.

The growing scale, in terms of devices and data, of modern sCPS and the need to perform complex processing and provide fast responses makes cloud–edge platforms natural candidates for integrating such systems. By applying the model and principles of cloud computing to sCPS, any virtual or physical device, including sensors and actuators, is available as a service and can quickly and autonomously be provisioned for usage to meet the user and application demands. By leveraging the edge computing paradigm, time constraints of CPS can be dealt with.

In this context, this Special Issue intends to explore the multiple aspects of sCPS and the role of cloud–edge computing as an integrating and enabling platform for this paradigm. Potential topics of interest include but are not limited to the following:

  • Cloud/edge/cloud–edge computing and services for cyberphysical systems;
  • Resource management in cloud–edge-based sCPS;
  • sCPS as a service;
  • Design, implementation, and operation of cloud/edge/cloud–edge platforms for sCPS;
  • Practical design issues in building cloud–edge-based sCPS;
  • Managing big data for sCPS;
  • Programming models, benchmarks, and tools for cloud-based sCPS;
  • Virtualization models for sCPS;
  • Architectures and frameworks for sCPS;
  • Interoperability issues in cloud/edge/cloud–edge-based sCPS;
  • Solutions for reliability and resilience in cloud/edge/cloud–edge-based sCPS;
  • Self-adaptation, self-healing, and self-configuration in sCPS;
  • Real-time data analytics and data stream processing for sCPS;
  • Context-aware event processing for sCPS;
  • Internet of Things and real-time services for sCPS;
  • Security challenges in cloud/edge/cloud–edge-based sCPS.

Prof. Dr. Flavia C. Delicato
Prof. Dr. Paulo F. Pires
Dr. Kevin I-Kai Wang
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. Information is an international peer-reviewed open access monthly 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 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

  •  Cyberphysical systems
  •  Cloud–edge computing
  •  Big data
  •  Smart systems
  •  Adaptive and real time systems

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Published Papers (4 papers)

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Research

22 pages, 762 KiB  
Article
Short-Term Load Forecasting Based on the Transformer Model
by Zezheng Zhao, Chunqiu Xia, Lian Chi, Xiaomin Chang, Wei Li, Ting Yang and Albert Y. Zomaya
Information 2021, 12(12), 516; https://doi.org/10.3390/info12120516 - 10 Dec 2021
Cited by 32 | Viewed by 5283
Abstract
From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, [...] Read more.
From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93. Full article
(This article belongs to the Special Issue Smart Cyberphysical Systems and Cloud–Edge Engineering)
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26 pages, 1206 KiB  
Article
Data-Driven Multi-Agent Vehicle Routing in a Congested City
by Alex Solter, Fuhua Lin, Dunwei Wen and Xiaokang Zhou
Information 2021, 12(11), 447; https://doi.org/10.3390/info12110447 - 27 Oct 2021
Cited by 3 | Viewed by 1997
Abstract
Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we can predict the effects of congestion, it may be possible to [...] Read more.
Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we can predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This paper studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. These data are made available to all users, such that they may be able to learn and predict the effects of congestion for building a route adaptively. This method is further enhanced by combining the traffic information system data with previous routing experiences to determine the fastest route with less exploration. We test our method using a multi-agent simulation, demonstrating that this method produces a lower total route time for all vehicles than when using either a centralized traffic information system or direct experience alone. Full article
(This article belongs to the Special Issue Smart Cyberphysical Systems and Cloud–Edge Engineering)
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29 pages, 2371 KiB  
Article
Resource Recommender for Cloud-Edge Engineering
by Amirmohammad Pasdar, Young Choon Lee, Tahereh Hassanzadeh and Khaled Almi’ani
Information 2021, 12(6), 224; https://doi.org/10.3390/info12060224 - 25 May 2021
Cited by 2 | Viewed by 2690
Abstract
The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources [...] Read more.
The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge resources, the edge utility cost plus public cloud billing cycles are adopted for scheduling, and jobs are profiled in the cloud-edge environment to facilitate scheduling through resource recommendations. These recommendations are outputted by the MLP neural network and LSTM for runtime estimation and resource recommendation, respectively. CECBS-R is trained with the scheduling outputs of Facebook and grid workload traces. The experimental results based on unseen workloads show that CECBS-R recommendations achieve a ∼65% cost saving in comparison to an online cost-efficient scheduler (BOS), resource management service (RMS), and an adaptive scheduling algorithm with QoS satisfaction (AsQ). Full article
(This article belongs to the Special Issue Smart Cyberphysical Systems and Cloud–Edge Engineering)
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26 pages, 872 KiB  
Article
Leveraging Edge Intelligence for Video Analytics in Smart City Applications
by Aluizio Rocha Neto, Thiago P. Silva, Thais Batista, Flávia C. Delicato, Paulo F. Pires and Frederico Lopes
Information 2021, 12(1), 14; https://doi.org/10.3390/info12010014 - 31 Dec 2020
Cited by 20 | Viewed by 4135
Abstract
In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time [...] Read more.
In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach. Full article
(This article belongs to the Special Issue Smart Cyberphysical Systems and Cloud–Edge Engineering)
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