Special Issue "Smart City Applications of Sensor Networks and Intelligent Systems"

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

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Dr. Jordi Mongay Batalla
E-Mail Website
Guest Editor
Institute of Telecommunications, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: cybersecurity (risk assessment, security enforcement, vulnerability management); IP technologies (radio: 5G and 6G; core: network service chain, SDN, AI); applications (DLT and blockchain, Internet of Things, smart cities, multimedia) for the Future Internet
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel H. De La Iglesia
E-Mail Website
Guest Editor
University of Salamanca, ESALAB Research Group, Plaza Caídos, 37008 Salamanca, Spain
Interests: IoT; smart cities; sensor networks; machine learning; expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, we live in a global world where people and objects are increasingly interconnected, regardless of their geographical location. Buildings, cities, vehicles, smartphones, and other devices are already equipped with digital sensors capable of recording large amounts of data. Sensor systems represent a fundamental element of the information age in which we live. Due to these systems, we are able to measure the real world and obtain precise values that were impossible to measure before. These new data have brought about a revolution in how users interact with the environment and provide new intelligent services.

The city plays a fundamental role in providing the basic infrastructure and access to the data necessary to manage these new intelligent services. The city can actively work toward the creation of an ecosystem from which citizens, companies, and society can benefit.

We therefore assume that the futures of our cities depend on technological investment in new infrastructure and global platforms that provide the basis for the development of smart services. Among these key technologies when designing advanced cities and environments, the rise of the Internet of Things (IoT), wireless communication systems, big data and services in the cloud or cloud services can be highlighted. All are fundamental elements when conceptualizing the nearest future in intelligent systems for urban environments or smart cities.

The IoT concept is one of the most used in recent years to refer to all those connected devices that make up smart environments. One of the main problems with IoT environments is the amount of data collected by their devices. These data are generally sent to central cloud servers where they are analyzed to obtain derived information or activate certain events. For this reason, concepts such as edge computing or fog computing have emerged that seek to change the passive behavior of IoT nodes and make devices an active part of the system. By equipping these devices with data analysis mechanisms, data are processed closer to where they are created instead of being sent over long distances to reach external data centers.

In this Special Issue, we look for studies that focus on innovative and disruptive solutions focused on wireless sensor networks and IoT environments in smart cities. This Special Issue invites industry and academic researchers in computer science and engineering, electrical engineering, and communications engineering, as well as engineers and professionals from the ICT industry, to contribute original articles on all aspects of the wireless sensor network and IoT systems for smart cities.

Dr. Jordi Mongay Batalla
Dr. Daniel H. De La Iglesia
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. Journal of Sensor and Actuator Networks is an international peer-reviewed open access quarterly 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

  • Wireless sensor networks in smart cities
  • Smart city sensing and IoT
  • IoT network technologies
  • Localization in IoT
  • Fog and edge computing in smart cities
  • Ubiquitous sensing
  • Big data in smart cities
  • Smart services
  • Smart home
  • Computer vision for smart cities
  • Reliability, security, privacy, and trust
  • Cryptography, security, and privacy

Published Papers (4 papers)

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Research

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Article
Capacity Control in Indoor Spaces Using Machine Learning Techniques Together with BLE Technology
J. Sens. Actuator Netw. 2021, 10(2), 35; https://doi.org/10.3390/jsan10020035 - 14 Jun 2021
Viewed by 638
Abstract
At present, capacity control in indoor spaces is critical in the current situation in which we are living in, due to the pandemic. In this work, we propose a new solution using machine learning techniques with BLE technology. This study presents a real [...] Read more.
At present, capacity control in indoor spaces is critical in the current situation in which we are living in, due to the pandemic. In this work, we propose a new solution using machine learning techniques with BLE technology. This study presents a real experiment in a university environment and we study three different prediction models using machine learning techniques—specifically, logistic regression, decision trees and artificial neural networks. As a conclusion, the study shows that machine learning techniques, in particular decision trees, together with BLE technology, provide a solution to the problem. The contribution of this research work shows that the prediction model obtained is capable of detecting when the COVID capacity of an enclosed space is exceeded. In addition, it ensures that no false negatives are produced, i.e., all the people inside the laboratory will be correctly counted. Full article
(This article belongs to the Special Issue Smart City Applications of Sensor Networks and Intelligent Systems)
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Article
Factor Optimization for the Design of Indoor Positioning Systems Using a Probability-Based Algorithm
J. Sens. Actuator Netw. 2021, 10(1), 16; https://doi.org/10.3390/jsan10010016 - 19 Feb 2021
Cited by 2 | Viewed by 809
Abstract
Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They advertise [...] Read more.
Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They advertise useful information, such as the received signal strength (RSS), that is processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing the arrangement of APs over the environment using an enhanced probability-based algorithm. From the assumption that a log-distance path loss model can reasonably describe, on average, the distribution of RSS throughout the environment, we build a simulation framework to analyze the impact, on the accuracy, of the main factors that constitute the positioning algorithm, such as the number of reference points (RPs) and the number of samples of RSS collected per test point. To demonstrate the applicability of the proposed solution, a real-world testbed dataset is used for validation. The obtained results for accuracy show that the trends verified via simulation strongly correlate to the verified in the dataset processing when allied with an optimal configuration of APs. This indicates our method is capable of providing an optimal factor combination—through early simulations—for the design of more efficient IPSs that rely on a probability-based positioning algorithm. Full article
(This article belongs to the Special Issue Smart City Applications of Sensor Networks and Intelligent Systems)
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Article
RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection
J. Sens. Actuator Netw. 2021, 10(1), 10; https://doi.org/10.3390/jsan10010010 - 30 Jan 2021
Cited by 3 | Viewed by 1233
Abstract
The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN). However, RF propagation channels in most WSN deployment environments, including dense cities and [...] Read more.
The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN). However, RF propagation channels in most WSN deployment environments, including dense cities and natural habitats, are commonly affected by shadowing due to obstructions caused by natural and man-made obstacles. RF signal attenuation from shadowing introduces uncharacteristically high errors in RSSI-based distance estimates, which result in large errors in RSSI-based localization schemes. This paper proposes the use of outlier detection methods for removing the effect of such disproportionately erroneous distance estimates in location estimation using RSSI. Three different localization schemes are proposed that apply outlier detection to effectively reduce localization errors in shadowed environments. Performance results of the proposed schemes are obtained using computer simulations and experimental tests. Full article
(This article belongs to the Special Issue Smart City Applications of Sensor Networks and Intelligent Systems)
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Review

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Review
A Review of Techniques for Implementing Elliptic Curve Point Multiplication on Hardware
J. Sens. Actuator Netw. 2021, 10(1), 3; https://doi.org/10.3390/jsan10010003 - 31 Dec 2020
Cited by 11 | Viewed by 1547
Abstract
Cryptography is considered indispensable among security measures applied to data concerning insecure means of transmission. Among various existent algorithms on asymmetric cryptography, we may cite Elliptic Curve Cryptography (ECC), which has been widely used due to its security level and reduced key sizes. [...] Read more.
Cryptography is considered indispensable among security measures applied to data concerning insecure means of transmission. Among various existent algorithms on asymmetric cryptography, we may cite Elliptic Curve Cryptography (ECC), which has been widely used due to its security level and reduced key sizes. When compared to Rivest, Shamir and Adleman (RSA), for example, ECC can maintain security levels with a shorter key. Elliptic Curve Point Multiplication (ECPM) is the main function in ECC, and is the component with the highest hardware cost. Lots of ECPM implementations have been applied on hardware targeting the acceleration of its calculus. This article presents a systematic review of literature on ECPM implementations on both Field-Programmable Gate Array (FPGA) and Application-Specific Integrated Circuit (ASIC). The obtained results show which methods and technologies have been used to implement ECPM on hardware and present some findings of the choices available to the hardware designers. Full article
(This article belongs to the Special Issue Smart City Applications of Sensor Networks and Intelligent Systems)
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