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Special Issue "Intelligent Systems in Sensor Networks and Internet of Things"

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

Deadline for manuscript submissions: 15 September 2019

Special Issue Editors

Guest Editor
Prof. Dr. Francesco Palmieri

Department of Computer Science, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy
Website | E-Mail
Interests: Wireless and wireline networking protocols; Network architectures; IoT; Routing algorithms; Network and communications security; distributed systems security; applied cryptography
Guest Editor
Dr. Gianni D’Angelo

Department of Law, Economics, Management and Quantitative Methods (DEMM), University of Sannio, Palazzo de Simone, Benevento, Italy
Website | E-Mail
Interests: Soft Computing algorithms; Data Mining and Machine Learning; Deep Learning; Knowledge Discovery; Optimization Problems; Pervasive Computing; Trustworthiness modeling; High Performance Machines, Parallel Computing, Big data analytics
Guest Editor
Dr. Chang Choi

IT Research Institute, Chosun Uiniversity, Gwangju, Korea
Website | E-Mail
Interests: Intelligent Information Processing; Information Security; Smart Sensor Networks

Special Issue Information

Dear Colleagues,

In the last decade, the rapid developments in hardware, software, and communication technologies have facilitated the spread of sensors, actuators and heterogeneous devices connected via the Internet (referred to as the Internet of Things, IoT), which collect and exchange huge amounts of data to offer a new class of advanced services characterized by being available anywhere, at any time and to anyone. Nevertheless, without intelligence, the IoT systems and, in general, sensor networks (SN) can act only as ordinary information systems based on predefined rules. On the contrary, adding artificial intelligence (AI) to the mix may allow services to be provided according to users’ habits, activities, and real-world contexts. Combining AI with the IoT opens the world to unlimited technological potential.

The intelligent processing of IoT data, and the building of intelligent systems able to make autonomous decisions are the keys to developing smart IoT applications and services. The combination of different scientific fields that uses data mining (DM), machine learning (ML), and other AI techniques have proven to be effective in exploring and handling the huge amount of data generated by IoT systems and SNs. In addition, other intelligences based on heuristic approaches, such as simulated annealing, genetic algorithms, evolutionary algorithms, ant colony optimization, and particle swarm optimization, have also proven to be effective in making the IoT systems and SNs aware of events and contexts, especially when dealing with large amounts of incomplete or inconsistent data.

The central theme of this Special Issue is to investigate novel methodologies, theories, systems, and applications for the creation of such intelligence in IoT systems and SNs.

Topics of Interest:

This Special Issue aims to present the most important and relevant advances in creating high-performance and intelligent IoT systems and sensor networks.

We seek original and high quality submissions related to, but not limited to, one or more of the following topics:

  • Data Mining for IoT and Sensor Networks
  • Intelligent Data Analysis in IoT and Sensor Networks
  • Intelligent Real-Time Data Processing of IoT and Sensor Networks
  • Soft Computing Applications for IoT and Sensor Networks
  • Machine-Learning and Artificial Intelligence for IoT and Sensors Networks
  • Nature-Inspired Evolutionary Algorithms and Systems for Pattern Recognition, Data Analysis, and Modeling in IoT and Sensor Networks
  • Heuristic Algorithms for IoT and Sensor Networks
  • Pattern Recognition and Classification for Multivariate Time Series
  • Intelligent Network Technologies for IoT and Sensor Networks
  • Intelligent Applications of IoT and Sensor Networks
  • Intelligent Decision-Making in IoT and Sensor Networks
  • Algorithms and Optimization Technologies for IoT and Sensor Network Scenarios
  • Adaptive Heterogeneous Sensor Networks
  • Cognitive Applications and Intelligence in IoT and Sensor Networks
  • Intelligent Intrusion Detection Techniques for IoT and Sensor Networks
  • Disaster Recovery for IoT and Sensor Networks
  • Learning from Data Streams in IoT and Sensor Networks
  • Adaptive Quality of Service (QoS) Provisioning in IoT and Sensor Networks
  • Deep Learning-Based Solutions for IoT Systems and Sensor Networks
  • Applications and Use-Cases of Smart IoT Systems, and Real-Time Sensor Networks
  • Distributed Computing Frameworks for IoT and Sensor Networks
  • Tools and Frameworks for Designing, Deploying and Maintaining Intelligent IoT Infrastructure

Prof. Dr. Francesco Palmieri
Dr. Gianni D’Angelo
Dr. Chang Choi
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 papers will be 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 1800 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.

Published Papers (6 papers)

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Research

Open AccessArticle A Practical Neighbor Discovery Framework for Wireless Sensor Networks
Sensors 2019, 19(8), 1887; https://doi.org/10.3390/s19081887 (registering DOI)
Received: 3 April 2019 / Revised: 17 April 2019 / Accepted: 17 April 2019 / Published: 20 April 2019
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Abstract
Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all [...] Read more.
Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all the critical concerns stemming from real WSNs, including communication collisions, latency constraints and energy consumption limitations. In this paper, we propose Spear, the first practical neighbor discovery framework to meet all these requirements. Spear offers two new methods to reduce communication collisions, thus boosting the discovery rate of existing neighbor discovery protocols. Spear also takes into consideration latency constraints and facilitates timely adjustments in order to reduce the discovery latency. Spear offers two practical energy management methods that evidently prolong the lifetime of sensor nodes. Most importantly, Spear automatically improves the discovery results of existing discovery protocols, on which no modification is required. Beyond reporting details of different Spear modules, we also present experiment evaluations on several notable neighbor discovery protocols. Results show that Spear greatly improves the discovery rate from 33.0 % to 99.2 % , and prolongs the sensor nodes lifetime up to 6.47 times. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks
Sensors 2019, 19(5), 1215; https://doi.org/10.3390/s19051215
Received: 21 January 2019 / Revised: 26 February 2019 / Accepted: 4 March 2019 / Published: 10 March 2019
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Abstract
With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization [...] Read more.
With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
Sensors 2019, 19(5), 1040; https://doi.org/10.3390/s19051040
Received: 1 February 2019 / Revised: 21 February 2019 / Accepted: 22 February 2019 / Published: 28 February 2019
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Abstract
In wireless sensor networks, clustering routing algorithms have been widely used owing to their high energy-efficiency and scalability. In clustering schemes, the nodes are organized in the form of clusters, and each cluster is governed by a cluster head. Once the cluster heads [...] Read more.
In wireless sensor networks, clustering routing algorithms have been widely used owing to their high energy-efficiency and scalability. In clustering schemes, the nodes are organized in the form of clusters, and each cluster is governed by a cluster head. Once the cluster heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station using minimum energy (cost) routing. This approach significantly improves the network lifetime. Therefore, a new cluster head selection method that uses a weighted sum method to calculate the weight of each node in the cluster and compare it with the standard weight of that particular cluster is proposed in this paper. The node with a weight closest to the standard cluster weight becomes the cluster head. This technique balances the load distribution and selects the nodes with highest residual energy in the network. Additionally, a data routing scheme is proposed to determine an energy-efficient path from the source to the destination node. This algorithm assigns a weight function to each link on the basis of a fuzzy membership function and intra-cluster communication cost within a cluster. As a result, a minimum weight path is selected using Dijkstra’s algorithm that improves the energy efficiency of the overall system. The experimental results show that the proposed algorithm shows better performance than some existing representative methods in the aspects of energy consumption, network lifetime, and system throughput. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics
Sensors 2019, 19(4), 935; https://doi.org/10.3390/s19040935
Received: 5 January 2019 / Revised: 18 February 2019 / Accepted: 20 February 2019 / Published: 22 February 2019
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Abstract
This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been [...] Read more.
This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle An Intelligent Driver Training System Based on Real Cars
Sensors 2019, 19(3), 630; https://doi.org/10.3390/s19030630
Received: 23 November 2018 / Revised: 23 January 2019 / Accepted: 30 January 2019 / Published: 2 February 2019
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Abstract
In driver training, the correct observation of the trainees’ operation is the key to ensure the training quality. The operation of the vehicle can be expressed by the vehicle state changes. This paper proposes a driver training model based on a multiple-embedded-sensor net. [...] Read more.
In driver training, the correct observation of the trainees’ operation is the key to ensure the training quality. The operation of the vehicle can be expressed by the vehicle state changes. This paper proposes a driver training model based on a multiple-embedded-sensor net. Six vehicle state parameters are identified as the critical features of the reverse parking machine learning model and represented quantitatively. A multiple-embedded-sensor net-based system mounted on a real vehicle is developed to collect the actual data of the six critical features. The data collected at the same time are bound together and encapsulated into a vector and sequenced by time with a label given by the multiple-embedded-sensor net. All vectors are evaluated by subjective assessment conclusions from experienced driving instructors and the positive ones are used as the training data of the model. The trained model can remind the driver of the next correct operation during training, and can also analyze the improvements after the training. The model has achieved good results in practical application. The experiments prove the validity and reliability of the proposed driver training model. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle Wireless Network for Assessing Temperature Load of Large-Scale Structures Under Fire Hazards
Sensors 2019, 19(1), 65; https://doi.org/10.3390/s19010065
Received: 27 November 2018 / Revised: 12 December 2018 / Accepted: 17 December 2018 / Published: 25 December 2018
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Abstract
While the construction of high-rise buildings has become popular in big cities, an average of over 15,000 structure fires in those buildings are being reported in the United States. Especially because the fire in a building can result in a failure or even [...] Read more.
While the construction of high-rise buildings has become popular in big cities, an average of over 15,000 structure fires in those buildings are being reported in the United States. Especially because the fire in a building can result in a failure or even the collapse of the structure, assessing its integrity during and after the fire is of importance. Thus, in this paper, a framework with temperature sensors using wireless communication technology has been proposed. Associated hardware and software are carefully chosen and developed to provide an easy and effective solution for measuring fire load on large-scale structures during a fire. With an autonomous measurement system enabled, the key functions of the framework have been validated in a fire testing laboratory, using a real-scale steel column subject to standard fire. Unlike existing solutions of wireless temperature networks, the proposed solution can provide the user definable sampling frequencies based on the surface temperature and the means to assess the load redistribution of the structure due to fire loading in real-time. The results of the study show the great potential of using the developed framework for monitoring fire in a structure, allowing more accurate estimations of fire load in the design criteria, and advancing fire safety engineering. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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