Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.6
3.5
Journals
JSAN
Special Issues
Smart Systems: Challenges, Enabling Technologies and Software Solutions
share announcement

Smart Systems: Challenges, Enabling Technologies and Software Solutions

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Wireless Control Networks".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 11588

Special Issue Editors

Prof. Dr. Sanja Lazarova-Molnar

E-Mail Website
Guest Editor
Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark
Interests: modelling and simulation; decision support systems; data analytics; artificial intelligence; Industry 4.0; cyber-physical systems; reliability analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jameela Al-Jaroodi

E-Mail Website
Guest Editor
Department of Engineering, Robert Morris University, Moon Township, PA 15108, USA
Interests: distributed systems; middleware; software engineering; Healthcare 4.0; smart systems; CPS and IoT/IIoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nader Mohamed

E-Mail Website
Guest Editor
California University of Pennsylvania, PA, United States
Interests: industry 4.0; cybersecurity; middleware; cloud and fog computing; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Gear Colleagues,

Our reality nowadays is being flooded with all kinds of smart systems, ranging from mundane mobile-based smart applications all the way up to industrial highly complex smart systems. Smart systems are used currently to address social, economic, personal, and environmental issues, such as limited resources, climate change, sustainability, and population aging. The objectives are to increase efficiency and improve performance in addition to creating autonomous systems, thus reducing the need for human interventions whenever possible. For instance, they are used to address transportation issues in urban areas; improve healthcare services access and quality; enhance energy production utilization and environmental impact; streamline, optimize and automate logistics operations, and reduce manual labor and enhance decision making in manufacturing. Smart systems incorporate functions of sensing, actuation, analysis and control for describing and analyzing various situations, and consequently making decisions based on the available data in a predictive or adaptive manner. These decisions further result in smart systems performing smart actions. In most cases, the “smartness” of the system can be attributed to autonomous operations based on closed loop controls, supported by advanced hardware, networking, software, analytics, and autonomous decision making capabilities. The focus of this Special Issue is on the challenges associated with smart systems; the enabling technologies in hardware devices, networking, sensing and control tools; and the software solution approaches including data analytics, decision making algorithms, optimization techniques, and intelligent algorithms.

The main topics of interest include, but are not limited to:

  • Sensors, Actuators, and Control Devices and Techniques
  • Integration Challenges Solution Approaches
  • Interoperability Issues and Solutions
  • Decision Support Mechanisms
  • Data Management and Data Analytics
  • Intelligent Algorithms
  • Modeling and Simulation Techniques
  • Internet of Things (IoT)
  • Cyber Physical systems (CPS)
  • Smart Systems Applications and Domains
  • Theoretical and Practical Challenges
  • Security and Privacy Issues
  • Blockchain for Smart Systems Applications
  • Risks and Risk Management
  • Software Engineering Solutions
  • Legal and Ethical Challenges
  • Industry 4.0 and Smart Manufacturing
  • Platforms for Smart Systems
  • Cases Studies on Current Implementations of Smart Systems

Prof. Dr. Sanja Lazarova-Molnar
Prof. Dr. Jameela Al-Jaroodi
Prof. Dr. Nader Mohamed
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. Journal of Sensor and Actuator Networks 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 2000 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

  • Smart Systems
  • Internet of Things
  • Industry 4.0
  • Decision Support
  • Sensors and Actuators
  • Artificial intelligence
  • Modeling and Simulation
  • Security and Privacy
  • Ethics

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 2194 KiB  
Article
Smart-Contract-Based Automation for OF-RAN Processes: A Federated Learning Use-Case
by Jofina Jijin, Boon-Chong Seet and Peter Han Joo Chong
J. Sens. Actuator Netw. 2022, 11(3), 53; https://doi.org/10.3390/jsan11030053 - 13 Sep 2022
Cited by 1 | Viewed by 1692
Abstract
The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation [...] Read more.
The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation offloading services to resource-limited Internet-of-Things (IoT) devices from vertical industrial applications such as smart transportation, tourism, mobile healthcare, and public safety. However, the current OF-RAN design is lacking a trusted and distributed mechanism for automating its processes such as v-FAP formation and service execution. Motivated by the recent emergence of blockchain, with smart contracts as an enabler of trusted and distributed systems, we propose an automated mechanism for OF-RAN processes using smart contracts. To demonstrate how our smart-contract-based automation for OF-RAN could apply in real life, a federated deep learning (DL) use-case where a resource-limited client offloads the resource-intensive training of its DL model to a v-FAP is implemented and evaluated. The results validate the DL and blockchain performances of the proposed smart-contract-enabled OF-RAN. The appropriate setting of process parameters to meet the often competing requirements is also demonstrated. Full article
(This article belongs to the Special Issue Smart Systems: Challenges, Enabling Technologies and Software Solutions)
►▼ Show Figures

Figure 1

15 pages, 2367 KiB  
Article
Hybrid Spectrum Sensing Using MD and ED for Cognitive Radio Networks
by Kavita Bani and Vaishali Kulkarni
J. Sens. Actuator Netw. 2022, 11(3), 36; https://doi.org/10.3390/jsan11030036 - 21 Jul 2022
Cited by 5 | Viewed by 1892
Abstract
Day by day, the demand for wireless systems is increasing while the available spectrum resources are not sufficient. To fulfil the demand for wireless systems, the spectrum hole (spectrum vacant) should be found and utilised very effectively. Cognitive radio (CR) is a device [...] Read more.
Day by day, the demand for wireless systems is increasing while the available spectrum resources are not sufficient. To fulfil the demand for wireless systems, the spectrum hole (spectrum vacant) should be found and utilised very effectively. Cognitive radio (CR) is a device which intelligently senses the spectrum through various spectrum-sensing detectors. Based on the complexity and licensed user’s information present with CR, the appropriate detector should be utilised for spectrum sensing. In this paper, a hybrid detector (HD) is proposed to determine the spectrum hole from the available spectrum resources. HD is designed based on an energy detector (ED) and matched detector (MD). Unlike a single detector such as ED or MD, HD can sense the signal more precisely. Here, HD can work on both conditions whether the primary user (PU) information is available or not. HD is analysed under heterogeneous environments with and without cooperative spectrum sensing (CSS). For CSS, four users were used to implement OR, AND, and majority schemes under low SNR walls. To design the HD, specifications were chosen based on the IEEE Wireless Regional Area Network (WRAN) 802.22 standard for accessing TV spectrum holes. For the HD model, we achieved the best results through OR rule. Under the low SNR circumstances at −20 dB SNR, the probability of detection (PD) is maximised to 1 and the probability of a false alarm (PFA) is reduced to 0 through the CSS environment. Full article
(This article belongs to the Special Issue Smart Systems: Challenges, Enabling Technologies and Software Solutions)
►▼ Show Figures

Figure 1

14 pages, 863 KiB  
Article
Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things
by Kuburat Oyeranti Adefemi Alimi, Khmaies Ouahada, Adnan M. Abu-Mahfouz, Suvendi Rimer and Oyeniyi Akeem Alimi
J. Sens. Actuator Netw. 2022, 11(3), 32; https://doi.org/10.3390/jsan11030032 - 01 Jul 2022
Cited by 25 | Viewed by 3305
Abstract
The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart [...] Read more.
The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works. Full article
(This article belongs to the Special Issue Smart Systems: Challenges, Enabling Technologies and Software Solutions)
►▼ Show Figures

Figure 1

16 pages, 2053 KiB  
Article
Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology
by Gilson Augusto Helfer, Jorge Luis Victória Barbosa, Douglas Alves, Adilson Ben da Costa, Marko Beko and Valderi Reis Quietinho Leithardt
J. Sens. Actuator Netw. 2021, 10(3), 40; https://doi.org/10.3390/jsan10030040 - 25 Jun 2021
Cited by 16 | Viewed by 3805
Abstract
The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical [...] Read more.
The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences. Full article
(This article belongs to the Special Issue Smart Systems: Challenges, Enabling Technologies and Software Solutions)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop