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Intelligent Sensors for Smart City

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 38912

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Special Issue Editor

Prof. Dr. Subhas Mukhopadhyay

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Guest Editor

School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: smart sensors; sensing technology; WSN; IoT; ICT; smart grid; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to Wikipedia (https://en.wikipedia.org/wiki/Smart_city), a smart city is an urban area that uses different types of electronic Internet of things (IoT)-enabled sensors to measure different parameters and then uses the measured data to manage assets and resources in a very effective and efficient way. This includes data collected from citizens, devices, and assets that are processed and analyzed to monitor and manage traffic and transportation systems, electric supply, water supply networks, waste management, crime detection, information systems, schools, libraries, hospitals, and other community services. With the advancements of sensors and sensing technologies and the advent of 5G communications technologies, we are witnessing an increasing number of sophisticated intelligent sensors deployed in smart city environments, which can greatly improve the life condition of their inhabitants both now and in the near future.

This Special Issue encourages the submission of high-quality unpublished papers that aim to solve open technical problems and challenges typical of IoT-oriented smart city scenarios. The main aim is to integrate novel approaches efficiently, focusing on performance evaluation and comparison with existing solutions. Both theoretical and experimental studies for typical IoT-oriented smart city scenarios are encouraged. Furthermore, high-quality review and survey papers are also welcomed. Papers considered for possible publication may focus on (but not necessarily be limited to) the following areas:

Intelligent sensors for smart cities;
Wireless sensors networks for smart cities;
IoT-enabled sensors for smart cities;
Green communications for smart cities;
Energy management systems and networks for smart cities;
Smart environment monitoring and control;
Smart management of appliances and resources;
Smart utility for cities;
Smart integration of heterogeneous sensors for cities.

Prof. Dr. Subhas Mukhopadhyay
Guest Editor

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.

Keywords

smart sensors
smart homes
internet of things
sensor networks
wireless sensor networks
sensors modeling, AI and IoT-enabled smart healthcare systems

Published Papers (10 papers)

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Research

25 pages, 6951 KiB

Open AccessArticle

Automatic Placement of Visual Sensors in a Smart Space to Ensure Required PPM Level in Specified Regions of Interest

by Iaroslav Khutornoi, Aleksandr Kobyzhev and Irina Vatamaniuk

Sensors 2022, 22(20), 7806; https://doi.org/10.3390/s22207806 - 14 Oct 2022

Cited by 1 | Viewed by 1548

Abstract

This work is devoted to a cost-effective method for the automatic placement of visual sensors within a smart room to ensure the requirements for its design. Various unique conditions make the process of manually placing sensors time consuming and can also lead to a decrease in system efficiency. To automate the design process, we solve a multi-objective optimization problem known as the art gallery problem in 3D, modified as follows. For the specified regions of interest within a smart room, the required pixels per meter level (PPM) should be ensured. The optimization criteria are visibility of the room and the cost of equipment. To meet these criteria, we describe a room model with doors, windows, and obstacles represented in such a way as to consider their impact on the field of view of the sensors. To model sensor placement, a genetic algorithm is used. The optimal solution is selected from the Pareto front by means of the technique for order of preference by similarity to ideal solution (TOPSIS). The developed method’s effectiveness has been tested on modeling real premises of various types. The method is flexible because of the assignment of weights to certain aspects when placing sensors. Further, it can be scalable to other types of sensors. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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30 pages, 3175 KiB

Open AccessArticle

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

by Huda M. Abdulwahid and Alok Mishra

Sensors 2022, 22(14), 5094; https://doi.org/10.3390/s22145094 - 07 Jul 2022

Cited by 7 | Viewed by 2907

Abstract

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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18 pages, 2474 KiB

Open AccessEditor’s ChoiceArticle

A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions

by Pedro Andrade, Ivanovitch Silva, Marianne Silva, Thommas Flores, Jordão Cassiano and Daniel G. Costa

Sensors 2022, 22(10), 3838; https://doi.org/10.3390/s22103838 - 19 May 2022

Cited by 24 | Viewed by 3444

Abstract

Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO₂ and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over time, although accuracy has been an important design issue when performing this task due the nature of the retrieved data. In this scenario, soft-sensor approaches can be adopted to process engine combustion data such as fuel injection and mass air flow, processing them to estimate pollution and transmitting the results for further analyses. Therefore, this article proposes a soft-sensor solution based on an embedded system designed to retrieve data from vehicles through their OBD-II interface, processing different inputs to provide estimated values of CO₂ emissions over time. According to the type of data provided by the vehicle, two different algorithms are defined, and each follows a comprehensive mathematical formulation. Moreover, an unsupervised TinyML approach is also derived to remove outliers data when processing the computed data stream, improving the accuracy of the soft sensor as a whole while not requiring any interaction with cloud-based servers to operate. Initial results for an embedded implementation on the Freematics ONE+ board have shown the proposal’s feasibility with an acquisition frequency equal to 1Hz and emission granularity measure of gCO₂/km. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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23 pages, 7843 KiB

Open AccessArticle

Characterization System for Heat-Energy to Electric-Energy Conversion from Concrete by Means of a Thermoelectric Module

by Luis C. Félix-Herrán, Alejandro García-Juárez, Luis Arturo García-Delgado, Pablo Said González-Aguayo, Jorge de-J. Lozoya-Santos and José R. Noriega

Sensors 2022, 22(5), 1881; https://doi.org/10.3390/s22051881 - 28 Feb 2022

Cited by 3 | Viewed by 2412

Abstract

The present work describes the implementation of a prototype to characterize thermoelectric modules (TEM). The goal is to study the energy conversion by means of thermoelectric modules mounted on concrete structures. The proposed experimental system is used for the electrical characterization of a commercially available thermoelectric module TEC1-12710 to prove its operation while embedded in a concrete slab, typical of building constructions. In this case, the parameters that define thermal energy conversion into electrical energy are open-circuit voltage generation, loaded circuit voltage generation, and load current. A known external load is connected to the terminals of the TEM for the purpose of its electric characterization. An electrical heating element on the hot side and a thermoelectric cooler on the cold side produce a temperature difference on the concrete slab. This arrangement allows the emulation of a temperature gradient produced by sunlight over a concrete structure. The objective is to measure the resulting electrical energy produced by the combination of concrete slab and the thermoelectric module. By controlling the temperature difference between the sides of the thermoelectric module under test, it is possible to simulate the effect of the temperature gradient under different sunlight conditions. Two digital PI controllers regulate the temperature conditions, thus providing controlled conditions for the experiments. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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15 pages, 2051 KiB

Open AccessArticle

Estimating Congestion in a Fixed-Route Bus by Using BLE Signals

by Yuji Kanamitsu, Eigo Taya, Koki Tachibana, Yugo Nakamura, Yuki Matsuda, Hirohiko Suwa and Keiichi Yasumoto

Sensors 2022, 22(3), 881; https://doi.org/10.3390/s22030881 - 24 Jan 2022

Cited by 4 | Viewed by 3080

Abstract

Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper’s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers’ privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers’ mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%). Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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18 pages, 10672 KiB

Open AccessArticle

Acoustic Detector of Road Vehicles Based on Sound Intensity

by Grzegorz Szwoch and Józef Kotus

Sensors 2021, 21(23), 7781; https://doi.org/10.3390/s21237781 - 23 Nov 2021

Cited by 8 | Viewed by 2420

Abstract

A method of detecting and counting road vehicles using an acoustic sensor placed by the road is presented. The sensor measures sound intensity in two directions: parallel and perpendicular to the road. The sound intensity analysis performs acoustic event detection. A normalized position of the sound source is tracked and used to determine if the detected event is related to a moving vehicle and to establish the direction of movement. The algorithm was tested on a continuous 24-h recording made in real-world conditions. The overall results were: recall 0.95, precision 0.95, F-score 0.95. In the analysis of one-hour slots, the worst results obtained in dense traffic were: recall 0.9, precision 0.93, F-score 0.91. The proposed method is intended for application in a network of traffic monitoring sensors, such as a smart city system. Its advantages include using a small, low cost and passive sensor, low algorithm complexity, and satisfactory detection accuracy. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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16 pages, 2605 KiB

Open AccessArticle

Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network

by Zhe Chen, Bin Zhao, Yuehan Wang, Zongtao Duan and Xin Zhao

Sensors 2020, 20(13), 3776; https://doi.org/10.3390/s20133776 - 05 Jul 2020

Cited by 34 | Viewed by 4788

Abstract

The accurate forecasting of urban taxi demands, which is a hot topic in intelligent transportation research, is challenging due to the complicated spatial-temporal dependencies, the dynamic nature, and the uncertainty of traffic. To make full use of the global and local correlations between traffic flows on road sections, this paper presents a deep learning model based on a graph convolutional network, long short-term memory (LSTM), and multitask learning. First, an undirected graph model was formed by considering the spatial pattern distribution of taxi trips on road networks. Then, LSTMs were used to extract the temporal features of traffic flows. Finally, the model was trained using a multitask learning strategy to improve the model’s generalizability. In the experiments, the efficiency and accuracy were verified with real-world taxi trajectory data. The experimental results showed that the model could effectively forecast the short-term taxi demands on the traffic network level and outperform state-of-the-art traffic prediction methods. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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15 pages, 17841 KiB

Open AccessArticle

Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images

by Marco A. Moreno-Armendáriz, Hiram Calvo, Carlos A. Duchanoy, Anayantzin P. López-Juárez, Israel A. Vargas-Monroy and Miguel Santiago Suarez-Castañon

Sensors 2019, 19(23), 5287; https://doi.org/10.3390/s19235287 - 30 Nov 2019

Cited by 8 | Viewed by 5156

Abstract

Nowadays, more than half of the world’s population lives in urban areas, and this number continues increasing. Consequently, there are more and more scientific publications that analyze health problems of people associated with living in these highly urbanized locations. In particular, some of the recent work has focused on relating people’s health to the quality and quantity of urban green areas. In this context, and considering the huge amount of land area in large cities that must be supervised, our work seeks to develop a deep learning-based solution capable of determining the level of health of the land and to assess whether it is contaminated. The main purpose is to provide health institutions with software capable of creating updated maps that indicate where these phenomena are presented, as this information could be very useful to guide public health goals in large cities. Our software is released as open source code, and the data used for the experiments presented in this paper are also freely available. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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30 pages, 16013 KiB

Open AccessArticle

Temperature Impact in LoRaWAN—A Case Study in Northern Sweden

by Níbia Souza Bezerra, Christer Åhlund, Saguna Saguna and Vicente A. de Sousa

Sensors 2019, 19(20), 4414; https://doi.org/10.3390/s19204414 - 12 Oct 2019

Cited by 26 | Viewed by 5145

Abstract

LoRaWAN has become popular as an IoT enabler. The low cost, ease of installation and the capacity of fine-tuning the parameters make this network a suitable candidate for the deployment of smart cities. In northern Sweden, in the smart region of Skellefteå, we have deployed a LoRaWAN to enable IoT applications to assist the lives of citizens. As Skellefteå has a subarctic climate, we investigate how the extreme changes in the weather happening during a year affect a real LoRaWAN deployment in terms of SNR, RSSI and the use of SF when ADR is enabled. Additionally, we evaluate two propagation models (Okumura-Hata and ITM) and verify if any of those models fit the measurements obtained from our real-life network. Our results regarding the weather impact show that cold weather improves the SNR while warm weather makes the sensors select lower SFs, to minimize the time-on-air. Regarding the tested propagation models, Okumura-Hata has the best fit to our data, while ITM tends to overestimate the RSSI values. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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19 pages, 4913 KiB

Open AccessArticle

IoT Enabled Intelligent Sensor Node for Smart City: Pedestrian Counting and Ambient Monitoring

by Fowzia Akhter, Sam Khadivizand, Hasin Reza Siddiquei, Md Eshrat E. Alahi and Subhas Mukhopadhyay

Sensors 2019, 19(15), 3374; https://doi.org/10.3390/s19153374 - 01 Aug 2019

Cited by 63 | Viewed by 6423

Abstract

An Internet of Things (IoT) enabled intelligent sensor node has been designed and developed for smart city applications. The fabricated sensor nodes count the number of pedestrians, their direction of travel along with some ambient parameters. The Field of View (FoV) of Fresnel lens of commercially available passive infrared (PIR) sensors has been specially tuned to monitor the movements of only humans and no other domestic animals such as dogs, cats etc. The ambient parameters include temperature, humidity, pressure, Carbon di Oxide (CO₂) and total volatile organic component (TVOC). The monitored data are uploaded to the Internet server through the Long Range Wide Area Network (LoRaWAN) communication system. An intelligent algorithm has been developed to achieve an accuracy of 95% for the pedestrian count. There are a total of 74 sensor nodes that have been installed around Macquarie University and continued working for the last six months. Full article

(This article belongs to the Special Issue Intelligent Sensors for Smart City)

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