Special Issue "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)

Special Issue Editor

Guest Editor
Dr. Davide Patti

Department of Computer Science and Telecommunications Engineering, University of Catania, 95124 Catania, Italy
Website | E-Mail
Interests: cyber–physical systems; networks on chip; design space exploration; artificial intelligence

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

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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 quarterly 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 350 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

Published Papers (3 papers)

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Research

Open AccessArticle Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors
Technologies 2018, 6(4), 89; https://doi.org/10.3390/technologies6040089
Received: 31 August 2018 / Revised: 25 September 2018 / Accepted: 26 September 2018 / Published: 29 September 2018
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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
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Open AccessArticle Applying Semantics to Reduce the Time to Analytics within Complex Heterogeneous Infrastructures
Technologies 2018, 6(3), 86; https://doi.org/10.3390/technologies6030086
Received: 15 August 2018 / Revised: 1 September 2018 / Accepted: 4 September 2018 / Published: 8 September 2018
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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
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Open AccessArticle An Effective Security Requirements Engineering Framework for Cyber-Physical Systems
Technologies 2018, 6(3), 65; https://doi.org/10.3390/technologies6030065
Received: 24 May 2018 / Revised: 28 June 2018 / Accepted: 5 July 2018 / Published: 12 July 2018
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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
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