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

Deadline for manuscript submissions: closed (31 January 2020)

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Prof. Dr. Himadri Nath Saha

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Institute of Engineering and Management Department of Electrical and Electronics Engineering , Gurukul, Y-12, Block -EP, Salt Lake Electronics Complex, Sector V, Kolkata, West Bengal 700091, India
Interests: IoT; machine learning; algorithm,security; wireless communication; robotics; brain computing interface; MANET; sensors; computer networking
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Victor C.M. Leung

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Department of Electrical & Computer Engineering, University of British Columbia, Vancouver, BC V6T1Z4, Canada
Interests: blockchain systems; telecommunication networks information systems personal communications networking cloud and edge computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Satyajit Chakrabarti

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Guest Editor

Institute of Engineering and Management Department of Computer Science Engineering, Gurukul, Y-12, Block -EP, Salt Lake Electronics Complex, Sector V, Kolkata, West Bengal 700091, India
Interests: machine learning; IoT; big data analytics; data mining; algorithms; robotics; sensors; human computer interface; networking and MANET; wireless communication
Special Issues, Collections and Topics in MDPI journals

Prof. Bob Gill

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Guest Editor

British Columbia Institute of Technology, Department of Electrical and Computer Engineering, 3700 Willingdon Ave, Burnaby, BC V5G 3H2, Canada
Interests: electrical and computer engineering; DSP; network security; bio-medical engineering; computer systems

Special Issue Information

Dear Colleagues,

This special issue aims to discuss and exchange ideas on issues, trends, and developments in Information Technology, Electronics and Mobile Communication. Contributed papers are solicited describing original works in the above mentioned fields and related technologies.

Topics and technical areas of interest include but are not limited to the following:

Information Technology

Computer Network
Evolutionary Computation and Algorithms
Intelligent Information Processing
Information System Integration and Decision Support
Image Processing and Multimedia Technology
Information Security and Encoding Technology; Information Retrieval
Signal Detection and Processing
Data Mining; Data Analytics and Big Data
Mobile Computing
Artificial Intelligence
Visualization and Computer Graphic
Natural Language Processing
Machine Learning
Internet of Things

Electronics

VLSI and Microelectronic Circuit Embedded Systems
System on Chip (SoC) Design
FPGA (Field Programmable Gate Array) Design and Applications
Electronic Instrumentations
Electronic Power Converters and Inverters
Electric Vehicle Technologies
Intelligent Control; Optimal Control; Robust Control
Linear and Nonlinear Control Systems
Complex Adaptive Systems
Industrial Automation and Control Systems Technology
Modern Electronic Devices

Mobile Communication

Ad hoc networks
Body and personal area networks
Cloud and virtual networks
Cognitive radio networks
Cooperative communications
Delay tolerant networks
Future wireless Internet
Green wireless networks
Local dependent networks; Location management
Mobile and wireless IP; Mobile computing
Multi-hop networks
Network architectures; Network Security
Routing, QoS and scheduling
Telecommunication Systems
Vehicular networks
Wireless multicasting, broadcasting and geocasting
Wireless sensor networks

Prof. Dr. Himadri Nath Saha

Prof. Dr. Victor C.M. Leung

Prof. Dr. Satyajit Chakrabarti
Prof. Bob Gill
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.

Published Papers (6 papers)

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Research

16 pages, 3364 KiB

Open AccessArticle

A Dynamic Plane Prediction Method Using the Extended Frame in Smart Dust IoT Environments

by Joonsuu Park and KeeHyun Park

Sensors 2020, 20(5), 1364; https://doi.org/10.3390/s20051364 - 02 Mar 2020

Cited by 5 | Viewed by 2482

Abstract

Internet of Things (IoT) technologies are undeniably already all around us, as we stand at the cusp of the next generation of IoT technologies. Indeed, the next-generation of IoT technologies are evolving before IoT technologies have been fully adopted, and smart dust IoT technology is one such example. The concept of smart dust IoT technology, which features very small devices with low computing power, is a revolutionary and innovative concept that enables many things that were previously unimaginable, but at the same time creates unresolved problems. One of the biggest problems is the bottlenecks in data transmission that can be caused by this large number of devices. The bottleneck problem was solved with the Dual Plane Development Kit (DPDK) architecture. However, the DPDK solution created an unexpected new problem, which is called the mixed packet problem. The mixed packet problem, which occurs when a large number of data packets and control packets mix and change at a rapid rate, can slow a system significantly. In this paper, we propose a dynamic partitioning algorithm that solves the mixed packet problem by physically separating the planes and using a learning algorithm to determine the ratio of separated planes. In addition, we propose a training data model eXtended Permuted Frame (XPF) that innovatively increases the number of training data to reflect the packet characteristics of the system. By solving the mixed packet problem in this way, it was found that the proposed dynamic partitioning algorithm performed about 72% better than the general DPDK environment, and 88% closer to the ideal environment. Full article

(This article belongs to the Special Issue Sensors for Information Technology, Electronics and Mobile Communication)

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23 pages, 5332 KiB

Open AccessArticle

Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization

by Ahmed Salih AL-Khaleefa, Mohd Riduan Ahmad, Azmi Awang Md Isa, Mona Riza Mohd Esa, Yazan Aljeroudi, Mohammed Ahmed Jubair and Reza Firsandaya Malik

Sensors 2019, 19(10), 2397; https://doi.org/10.3390/s19102397 - 25 May 2019

Cited by 16 | Viewed by 3423

Abstract

Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc. Full article

(This article belongs to the Special Issue Sensors for Information Technology, Electronics and Mobile Communication)

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12 pages, 479 KiB

Open AccessArticle

Management and Monitoring of IoT Devices Using Blockchain

by Kristián Košťál, Pavol Helebrandt, Matej Belluš, Michal Ries and Ivan Kotuliak

Sensors 2019, 19(4), 856; https://doi.org/10.3390/s19040856 - 19 Feb 2019

Cited by 88 | Viewed by 12381

Abstract

Nowadays, we are surrounded by a large number of IoT (Internet of Things) devices and sensors. These devices are designed to make life easier and more comfortable. Blockchain technology, especially its mass application, is becoming a term number one. Adoption of blockchain into enterprise networks still has a few challenges that need to be tackled. Utilizing blockchain can bring increased security and efficiency of network maintenance. The key feature of the blockchain, immutability, brings resistance to unauthorized modifications. The whole history of device configuration changes is stored in the blockchain, hence recovery after incidents is very straightforward. This paper extends our previous studies. We are introducing an improved architecture for management and monitoring of IoT devices using a private blockchain. The majority of the system is built on a chaincode, which handles CRUD (Create, Read, Update, Delete) operations as well as encryption and access control. Device configuration files are stored in the blockchain. When a modification occurs, the device downloads a new configuration in a simple manner. The chaincode receives notification whether setup was successful and this history is available for administrators. Our results show that such a system is possible and dissemination of configuration changes to IoT devices can be secured by the blockchain. The key novelty of our solution is a distributed management of configuration files of IoT devices in enterprise networks utilizing blockchain technology. This is essentially improving security and storage options for configurations in the blockchain. Full article

(This article belongs to the Special Issue Sensors for Information Technology, Electronics and Mobile Communication)

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12 pages, 2309 KiB

Open AccessArticle

Blind Estimation of the PN Sequence of A DSSS Signal Using A Modified Online Unsupervised Learning Machine

by Yangjie Wei, Shiliang Fang, Xiaoyan Wang and Shuxia Huang

Sensors 2019, 19(2), 354; https://doi.org/10.3390/s19020354 - 16 Jan 2019

Cited by 13 | Viewed by 3983

Abstract

Direct sequence spread spectrum (DSSS) signals are now widely used in air and underwater acoustic communications. A receiver which does not know the pseudo-random (PN) sequence cannot demodulate the DSSS signal. In this paper, firstly, the principle of principal component analysis (PCA) for PN sequence estimation of the DSSS signal is analyzed, then a modified online unsupervised learning machine (LEAP) is introduced for PCA. Compared with the original LEAP, the modified LEAP has the following improvements: (1) By normalizing the system state transition matrices, the modified LEAP can obtain better robustness when the training errors occur; (2) with using variable learning steps instead of a fixed one, the modified LEAP not only converges faster but also has excellent estimation performance. When the modified LEAP is converging, we can utilize the network connection weights which are the eigenvectors of the autocorrelation matrix of the DSSS signal to estimate the PN sequence. Due to the phase ambiguity of the eigenvectors, a novel approach which is based on the properties of the PN sequence is proposed here to exclude the wrong estimated PN sequences. Simulation results showed that the methods mentioned above can estimate the PN sequence rapidly and robustly, even when the DSSS signal is far below the noise level. Full article

(This article belongs to the Special Issue Sensors for Information Technology, Electronics and Mobile Communication)

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12 pages, 841 KiB

Open AccessArticle

A Method for Analyzing the Impact of Intra-System and Inter-System Interference on DME Based on Queueing Theory

by Guofeng Jiang and Yangyu Fan

Sensors 2019, 19(2), 348; https://doi.org/10.3390/s19020348 - 16 Jan 2019

Cited by 13 | Viewed by 3346

Abstract

In order to use Distance Measuring Equipment (DME) properly, the impact of intra-system and inter-system electromagnetic interference must be analyzed firstly. However, the error of interference analysis using present methods based on pulse overlap is large when there are more aircraft. The aim of this article is to study a method of analyzing interference on DME whether the number of aircraft is small or not. According to the flow chart of DME signal, we studied the limitations of present methods; then constructed a model of analyzing the collision between duration of desired signal and dead time of receiver based on M/M/1/0 queueing system. Combing this model with other methods, we present a analytic model of analyzing intra-system and inter-system interference on DME. Using this analytic model, we analyzed reply efficiency (RE) and capacity of DME under intra-system and Joint Tactical Information Distribution System (JTIDS) interference. The result shows that the calculation for the probability of overlap between DME dead time and subsequent signals using queueing model agrees well with simulation. Consequently, the analytic model is more accurate than using a single method to analyze interference on DME. Full article

(This article belongs to the Special Issue Sensors for Information Technology, Electronics and Mobile Communication)

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20 pages, 745 KiB

Open AccessArticle

Road Anomalies Detection System Evaluation

by Nuno Silva, Vaibhav Shah, João Soares and Helena Rodrigues

Sensors 2018, 18(7), 1984; https://doi.org/10.3390/s18071984 - 21 Jun 2018

Cited by 39 | Viewed by 5168

Abstract

Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities. Full article

(This article belongs to the Special Issue Sensors for Information Technology, Electronics and Mobile Communication)

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