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Smart IoT Systems
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Smart IoT Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2021) | Viewed by 11770

Special Issue Editors

Dr. Jean-Yves Tigli

E-Mail Website
Guest Editor
Computer Science and Technology, University Côte D’azur, Provence, France
Interests: self-adaptive systems; complex systems modeling; context-aware computing; software engineering; ambient intelligence; Internet of Things; cyberphysical systems
Dr. Nicolas Ferry

E-Mail Website
Guest Editor
SINTEF Digital, 0373 Oslo, Norway
Interests: model-driven engineering; Models@Run.time; dynamic adaptation; IoT; cloud computing

Special Issue Information

Dear Colleagues,

Smart IoT systems are typically complex, large-scale, distributed, and running in open contexts. They involve interconnected things that sense and act on the physical environment, as well as control loops distributed all along the Internet continuum spanning across the Cloud, Edge, and IoT spaces. These control loops assimilate data from sensors, build their own representation of the surrounding environment, plan reactions, and enact these, possibly through actuators. When properly coordinated, they result in smart and autonomous behaviors that form the core of a Smart IoT System. With the recent democratization of sensors and actuators and the increased connectivity and interoperability capabilities, Smart IoT Systems are being adopted in numerous application domains such as Smart City, Smart Building, Smart Health, Smart Farming and Agriculture, Smart Grid, Smart Factory, Intelligent Transport Systems, Smart Supply Chain, etc. However, many challenges remain to be solved, related to:

  • Their development (e.g., function as a service, microservices);
  • Their smart behaviors (e.g., machine learning, integration with big data, human interaction);
  • Their architecture (e.g., Cloud–Edge–IoT device architectures, systems of systems, hierarchical control systems);
  • Their design methodologies (e.g., DevOps for IoT);
  • Their security, privacy, and quality of service management;
  • Their operation in large-scale, complex and open Context/Environments;
  • Their impact on Human and Social Sciences, Ethics, Economics, Society, etc.

The aim of this Special Issue is to publish the most recent scientific results, both theoretical and applied, which highlight the latest advances in the field of Smart IoT Systems.

Dr. Jean-Yves Tigli
Dr. Nicolas Ferry
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

  • Security and privacy for the IoT
  • IoT, Edge, and Cloud Architectures
  • Human interaction in the IoT
  • Self-* IoT Systems
  • Software engineering and methodology for smart IoT Systems
  • Human and social impact of IoT
  • Large-scale data processing in IoT
  • Smart IoT applications (Smart City, Smart Building, Smart Health, Smart Farming and Agriculture, Smart Grid, Smart Factory, Intelligent Transport Systems, Smart Supply Chain)

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Published Papers (2 papers)

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10 pages, 6802 KiB  
Article
Edge-Based Missing Data Imputation in Large-Scale Environments
by Davide Andrea Guastella, Guilhem Marcillaud and Cesare Valenti
Information 2021, 12(5), 195; https://doi.org/10.3390/info12050195 - 29 Apr 2021
Cited by 13 | Viewed by 2996
Abstract
Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices [...] Read more.
Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis; on the other hand, it involves different challenges such as intermittent sensors and integrity of acquired data. To this effect, edge computing emerges as a methodology to distribute computation among different IoT devices to analyze data locally. We present here a new methodology for imputing environmental information during the acquisition step, due to missing or otherwise out of order sensors, by distributing the computation among a variety of fixed and mobile devices. Numerous experiments have been carried out on real data to confirm the validity of the proposed method. Full article
(This article belongs to the Special Issue Smart IoT Systems)
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Review

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23 pages, 1431 KiB  
Review
Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review
by Ahmed Bahaa, Ahmed Abdelaziz, Abdalla Sayed, Laila Elfangary and Hanan Fahmy
Information 2021, 12(4), 154; https://doi.org/10.3390/info12040154 - 7 Apr 2021
Cited by 29 | Viewed by 7589
Abstract
In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local [...] Read more.
In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring. Full article
(This article belongs to the Special Issue Smart IoT Systems)
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