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Technologies
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Cyber-Physical Systems: Data Processing and Communication Architectures
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Cyber-Physical Systems: Data Processing and Communication Architectures

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: closed (15 August 2018) | Viewed by 42359

Special Issue Editor

Dr. Davide Patti

E-Mail Website
Guest Editor
Department of Computer Science and Telecommunications Engineering, University of Catania, 95124 Catania, Italy
Interests: network-on-chip; blockchains; machine learning; hardware accelerators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Last generation computing architectures have evolved from traditional stand-alone embedded systems to complex environments, where computational elements tightly interact with physical entities, such as sensors networks and I/O devices. These systems, usually referred as cyber-physical Systems (CPS), enabled a flourishing ecosystem of architectures and platforms where smart objects, users and communication infrastructures interact to support intelligent context-aware services and applications. Smart grids, medical monitoring, smart cities, distributed pollution and tracking are just a few examples of concrete applications that are gaining attraction among industries and institutions. However, the mobility and pervasiveness requirements of such environments impose energy consumption constraints that must be met in a context of increasing computational needs, due the processing of large amounts of data coming from sensing and input devices.

This Special Issue aims at exploring emerging approaches, ideas and contributions to address the challenges in the design of energy efficient computational-centric smart objects in CPS. Potential topics include, but are not limited to:

  • Design Platforms and Tools for IoT-based ecosystems for optimizing energy/performance tradeoffs
  • Deep Learning and Deep Computation for CPS
  • Novel architectures for embedded low power computing in CPS
  • Approximate Computing for energy-efficient applications
  • Network-on-Chip Architectures
  • Wireless Sensor Networks
Dr. Davide Patti
Guest Editor

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. Technologies 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

  • cyber-physical systems
  • network-on-chip
  • deep learning
  • approximate computing

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Published Papers (5 papers)

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Research

27 pages, 3954 KiB  
Article
Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era
by Matheus E. Leusin, Enzo M. Frazzon, Mauricio Uriona Maldonado, Mirko Kück and Michael Freitag
Technologies 2018, 6(4), 107; https://doi.org/10.3390/technologies6040107 - 16 Nov 2018
Cited by 79 | Viewed by 13991
Abstract
Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex [...] Read more.
Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Data Processing and Communication Architectures)
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20 pages, 795 KiB  
Article
Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems
by Ankur Vora and Kyoung-Don Kang
Technologies 2018, 6(4), 105; https://doi.org/10.3390/technologies6040105 - 15 Nov 2018
Cited by 11 | Viewed by 7693
Abstract
In emerging Cyber-Physical Systems (CPS), the demand for higher communication performance and enhanced wireless connectivity is increasing fast. To address the issue, in our recent work, we proposed a dynamic programming algorithm with polynomial time complexity for effective cross-layer downlink Scheduling and Resource [...] Read more.
In emerging Cyber-Physical Systems (CPS), the demand for higher communication performance and enhanced wireless connectivity is increasing fast. To address the issue, in our recent work, we proposed a dynamic programming algorithm with polynomial time complexity for effective cross-layer downlink Scheduling and Resource Allocation (SRA) considering the channel and queue state, while supporting fairness. In this paper, we extend the SRA algorithm to consider 5G use-cases, namely enhanced Machine Type Communication (eMTC), Ultra-Reliable Low Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB). In a simulation study, we evaluate the performance of our SRA algorithm in comparison to an advanced greedy cross-layer algorithm for eMTC, URLLC and LTE (long-term evolution). For eMTC and URLLC, our SRA method outperforms the greedy approach by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness, respectively. In the case of LTE, our approach outperforms the greedy method by 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness compared with tested baseline. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Data Processing and Communication Architectures)
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20 pages, 527 KiB  
Article
Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors
by Florian Grützmacher, Albert Hein, Thomas Kirste and Christian Haubelt
Technologies 2018, 6(4), 89; https://doi.org/10.3390/technologies6040089 - 29 Sep 2018
Cited by 4 | Viewed by 4510
Abstract
The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a [...] Read more.
The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Data Processing and Communication Architectures)
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29 pages, 1754 KiB  
Article
Applying Semantics to Reduce the Time to Analytics within Complex Heterogeneous Infrastructures
by André Pomp, Alexander Paulus, Andreas Kirmse, Vadim Kraus and Tobias Meisen
Technologies 2018, 6(3), 86; https://doi.org/10.3390/technologies6030086 - 8 Sep 2018
Cited by 31 | Viewed by 7347
Abstract
In today’s age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which should now be used for [...] Read more.
In today’s age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which should now be used for this purpose are very heterogeneous and complex. As a result, tasks for analyzing data, such as collecting, searching, understanding and processing data, become very time-consuming. This makes it difficult to realize visions, such as the Internet of Production, which pursues the goal of guaranteeing the availability of real-time information at any time and place in an industrial setting. To reduce the time to analytics in such scenarios, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline as well as a semantic data platform named ESKAPE. The ingestion pipeline provides an abstraction to all tasks related to data acquisition. The main goal is, therefore, the controllable access to data and meta information contained in machines and other systems on the shop floor. Additionally, it provides the possibility to forward the collected data to a configurable endpoint, such as a data lake. ESKAPE acts as one of those endpoints enabling semantic data integration and processing. By annotating data sets with semantic models originating from the Semantic Web, data analysts are able to understand, process and discover these data sets more efficiently. ESKAPE features a three-layered information storage architecture consisting of a data layer for storing integrated raw data sets, a layer containing user-defined semantic models to describe the contextual knowledge necessary to interpret the stored data and a top layer formed by a continuously evolving knowledge graph, combining semantic information from all present semantic models. Based on this storage system, ESKAPE enables the flexible annotation as well as efficient search and processing of data sources without losing the ability of analyzing and querying the underlying raw data with analytic tools. We present and discuss our approach and its benefits and limitations based on a real-world industrial use case. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Data Processing and Communication Architectures)
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20 pages, 2027 KiB  
Article
An Effective Security Requirements Engineering Framework for Cyber-Physical Systems
by Shafiq Ur Rehman and Volker Gruhn
Technologies 2018, 6(3), 65; https://doi.org/10.3390/technologies6030065 - 12 Jul 2018
Cited by 39 | Viewed by 7530
Abstract
Context and motivation: Cyber-Physical Systems (CPSs) are gaining priority over other systems. The heterogeneity of these systems increases the importance of security. Both the developer and the requirement analyst must consider details of not only the software, but also the hardware perspective, including [...] Read more.
Context and motivation: Cyber-Physical Systems (CPSs) are gaining priority over other systems. The heterogeneity of these systems increases the importance of security. Both the developer and the requirement analyst must consider details of not only the software, but also the hardware perspective, including sensor and network security. Several models for secure software engineering processes have been proposed, but they are limited to software; therefore, to support the processes of security requirements, we need a security requirements framework for CPSs. Question/Problem: Do existing security requirements frameworks fulfil the needs of CPS security requirements? The answer is no; existing security requirements frameworks fail to accommodate security concerns outside of software boundaries. Little or even no attention has been given to sensor, hardware, network, and third party elements during security requirements engineering in different existing frameworks. Principal Ideas/results: We have proposed, applied, and assessed an incremental security requirements evolution approach, which configures the heterogeneous nature of components and their threats in order to generate a secure system. Contribution: The most significant contribution of this paper is to propose a security requirements engineering framework for CPSs that overcomes the issue of security requirements elicitation for heterogeneous CPS components. The proposed framework supports the elicitation of security requirements while considering sensor, receiver protocol, network channel issues, along with software aspects. Furthermore, the proposed CPS framework has been evaluated through a case study, and the results are shown in this paper. The results would provide great support in this research direction. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Data Processing and Communication Architectures)
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