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Evolution of Distributed Computing in Sensor Systems
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Evolution of Distributed Computing in Sensor Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 20436

Special Issue Editors

Dr. Sara Rodriguez

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Guest Editor
BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; distributed computing; machine learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco Herrera

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Guest Editor
Department of Computer Science and Artificial Intelligence, University of Granada, E-18071 Granada, Spain
Interests: artificial intelligence; computational intelligence; data science; data preprocessing; big data analytics
Dr. Alfonso González-Briones

E-Mail Website
Guest Editor
Department of Computer Science and Automation Control, University of Salamanca, 37007 Salamanca, Spain
Interests: smart cities; IoT; Industry 4.0; machine learning; artificial intelligence natural language processing; computational technologies; sentiment analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Yucheng Dong

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Guest Editor
Sichuan University

Special Issue Information

Today, most computer systems from laptops to cluster/network/cloud/fog/edge computing systems are available for parallel and distributed computing. Distributed computing plays an increasingly important role in modern signal and data sensors processing, information fusion, and electronic engineering. Cloud/fog/edge computing platforms are able to provide a unified interface over heterogenous resources found in the Internet of Things (IoT)-based applications, thereby improving the reliability of cloud services. Further, fog computing supports Internet of Things (IoT) devices such as mobile phones, sensors, and health monitoring devices.

Thanks to the evolution that some artificial intelligence techniques are undergoing in IoT environments, it is possible to provide these new computational environments with a high added value and economic potential. Distributed Intelligent Systems have matured over the last decade and now many effective applications have been deployed.

In this Special Issue, we aim to explore how two emerging paradigms (IoT and Artificial Intelligence) will influence future distributed computing systems (cloud/fog/edge environments). Furthermore, we try to identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of IoT.

The topics of interest for this issue include but are not limited to:

  • Internet of Things applications
  • Industrial Internet of things applications
  • Artificial Intelligence approaches in distributed environments
  • Fog Computing environments
  • Edge Computing environments
  • Intelligent environments
  • Distributed Algorithms
  • Distributed Databases
  • Virtual Organizations of Agents
  • Distributed Architectures
  • Multiagent Systems
  • High-performance distributed systems

Prof. Dr. Sara Rodríguez González
Dr. Francisco Herrera
Dr. Alfonso González-Briones
Prof. Yucheng Dong
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. 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 2600 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

  • Distributed Computing Models
  • Internet of Things
  • New Artificial Intelligence Approaches on IoT
  • Intelligent Environments
  • Distributed Algorithms

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Published Papers (3 papers)

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Research

22 pages, 5510 KiB  
Article
Discovering the Value Creation System in IoT Ecosystems
by Carlos Alberto Lopez, Luis Fernando Castillo and Juan M. Corchado
Sensors 2021, 21(2), 328; https://doi.org/10.3390/s21020328 - 6 Jan 2021
Cited by 6 | Viewed by 3887
Abstract
Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, [...] Read more.
Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, deciphering the operation of the value creation system and finding the balance between the digital strategy and the deployment of technological platforms, are the main motivations behind this research. To achieve the proposed objectives, systems theory has been adopted in the conceptualization stage, later, fuzzy logic has been used to structure a subsystem for the evaluation of input parameters. Subsequently, system dynamics have been used to build a computational representation and later, through dynamic simulation, the model has been adjusted according to iterations and the identified limits of the system. Finally, with the obtained set of results, different value creation and capture behaviors have been identified. The simulation model, based on the conceptualization of the system and the mathematical representation of the value function, allows to establish a frame of reference for the evaluation of the behaviour of IoT ecosystems in the context of the connected home. Full article
(This article belongs to the Special Issue Evolution of Distributed Computing in Sensor Systems)
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22 pages, 2582 KiB  
Article
Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis
by Ivan Kholod, Evgeny Yanaki, Dmitry Fomichev, Evgeniy Shalugin, Evgenia Novikova, Evgeny Filippov and Mats Nordlund
Sensors 2021, 21(1), 167; https://doi.org/10.3390/s21010167 - 29 Dec 2020
Cited by 86 | Viewed by 13011
Abstract
The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis [...] Read more.
The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use. Full article
(This article belongs to the Special Issue Evolution of Distributed Computing in Sensor Systems)
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23 pages, 952 KiB  
Article
Knowledge-Based Verification of Concatenative Programming Patterns Inspired by Natural Language for Resource-Constrained Embedded Devices
by Salvatore Gaglio, Giuseppe Lo Re, Gloria Martorella and Daniele Peri
Sensors 2021, 21(1), 107; https://doi.org/10.3390/s21010107 - 26 Dec 2020
Cited by 2 | Viewed by 2492
Abstract
We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into [...] Read more.
We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into executable code avoiding the recourse to ontologies, which would need to be shared, kept up to date, and synchronized across a set of devices. Moreover, the computational paradigm we use for effective distributed execution of symbolic code on resource-constrained devices encourages the adoption of such patterns. The methodology is supported by a rule-based system that permits runtime verification of Software Under Test (SUT) on board the target devices through automated oracle and test case generation. Moreover, verification extends from syntactic and semantic checks to the evaluation of the effects of SUT execution on target hardware. Additionally, by exploiting rules tying sensors and actuators to physical quantities, the effects of code execution on the physical environment can be verified. The system is also able to build test code to highlight software issues that may arise during repeated SUT execution on the target hardware. Full article
(This article belongs to the Special Issue Evolution of Distributed Computing in Sensor Systems)
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