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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 33307

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Special Issue Editors

Prof. Dr. Luis Hernández-Callejo

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Department of Agricultural and Forestry Engineering, University of Valladolid, Campus Duques de Soria, 42004 Soria, Spain
Interests: energy; engineering; computer science; photovoltaic systems; microgrids; distributed generation; smart metering
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Prof. Dr. Sergio Nesmachnow

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Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
Interests: distributed computing; parallel computing; metaheuristics; optimization
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Dr. Jaime Lloret

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Integrated Management Coastal Research Institute, Universitat Politecnica de Valencia, 46022 Valencia, Spain
Interests: network protocols; network algorithms; wireless sensor networks; ad hoc networks; multimedia streaming
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Prof. Dr. Pedro Moreno-Bernal

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Faculty of Accounting, Administration, and Informatics, Autonomous University of Morelos State (UAEM), Avenida Universidad 1001 Colonia Chamilpa, Cuernavaca C.P. 62209, Morelos, Mexico
Interests: smart cities; parallel computing; cloud computing; optimization; smart manufacturing
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Special Issue Information

Dear Colleagues,

Smart cities result from the increasingly urgent need to orient our lives towards sustainability. Therefore, these cities use infrastructure, innovation, and technology to improve the quality of life of their citizens. Citizens interact with smart city ecosystems in a variety of ways, using smartphones and mobile devices, sensor technology, as well as connected vehicles and homes. Pairing devices and data with a city's physical infrastructure and services can reduce costs and improve sustainability. With the help of IoT, communities can improve energy distribution, optimize garbage collection, decrease traffic congestion, and even improve air quality. Secure wireless connectivity and IoT technology are turning traditional elements of urban life, such as street lighting, into next-generation smart lighting platforms with extended capabilities. This includes integrating solar power and connecting to a central cloud-based control system that connects to other ecosystem assets.

In this sense, sensors and IoT deployed in cities are the key building blocks for the development of these cities. These technologies will make it possible to achieve sustainable, comprehensive, innovative, and forward-looking cities.

Prof. Dr. Luis Hernández-Callejo
Prof. Dr. Sergio Nesmachnow
Dr. Jaime Lloret
Prof. Dr. Pedro Moreno-Bernal
Guest Editors

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Keywords

IoT and ubiquitous devices
smart sensors
smart mobility
energy
smart grid
governance and citizenship
energy efficiency and sustainability
smart industry
smart environment
smart public services (waste management, health, public transportation, among others) urban informatics, Big Data, data management, analytics and artificial intelligence for smart cities
other developments for smart cities

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Published Papers (9 papers)

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Research

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18 pages, 453 KiB

Open AccessArticle

Efficient IoT-Assisted Waste Collection for Urban Smart Cities

by Sangrez Khan, Bakhtiar Ali, Abeer A. K. Alharbi, Salihah Alotaibi and Mohammed Alkhathami

Sensors 2024, 24(10), 3167; https://doi.org/10.3390/s24103167 - 16 May 2024

Viewed by 2532

Abstract

Waste management is one of the many major challenges faced by all urban cities around the world. With the increase in population, the current mechanisms for waste collection and disposal are under strain. The waste management problem is a global challenge that requires a collaborative effort from different stakeholders. Moreover, there is a need to develop technology-based solutions besides engaging the communities and establishing novel policies. While there are several challenges in waste management, the collection of waste using the current infrastructure is among the top challenges. Waste management suffers from issues such as a limited number of collection trucks, different types of household and industrial waste, and a low number of dumping points. The focus of this paper is on utilizing the available waste collection transportation capacity to efficiently dispose of the waste in a time-efficient manner while maximizing toxic waste disposal. A novel knapsack-based technique is proposed that fills the collection trucks with waste bins from different geographic locations by taking into account the amount of waste and toxicity in the bins using IoT sensors. Using the Knapsack technique, the collection trucks are loaded with waste bins up to their carrying capacity while maximizing their toxicity. The proposed model was implemented in MATLAB, and detailed simulation results show that the proposed technique outperforms other waste collection approaches. In particular, the amount of high-priority toxic waste collection was improved up to 47% using the proposed technique. Furthermore, the number of waste collection visits is reduced in the proposed scheme as compared to the conventional method, resulting in the recovery of the equipment cost in less than a year. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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17 pages, 4220 KiB

Open AccessArticle

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

by Himanshi Babbar, Shalli Rani, Dipak Kumar Sah, Salman A. AlQahtani and Ali Kashif Bashir

Sensors 2023, 23(16), 7256; https://doi.org/10.3390/s23167256 - 18 Aug 2023

Cited by 7 | Viewed by 2575

Abstract

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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17 pages, 1800 KiB

Open AccessArticle

IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances

by Leonardo Cardinale-Villalobos, Efren Jimenez-Delgado, Yariel García-Ramírez, Luis Araya-Solano, Luis Antonio Solís-García, Abel Méndez-Porras and Jorge Alfaro-Velasco

Sensors 2023, 23(15), 6749; https://doi.org/10.3390/s23156749 - 28 Jul 2023

Cited by 6 | Viewed by 2391

Abstract

Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater [...] Read more.

m^{2}

, making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/

m^{2}

. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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23 pages, 3461 KiB

Open AccessArticle

DT-RRNS: Routing Protocol Design for Secure and Reliable Distributed Smart Sensors Communication Systems

by Andrei Gladkov, Egor Shiriaev, Andrei Tchernykh, Maxim Deryabin, Mikhail Babenko and Sergio Nesmachnow

Sensors 2023, 23(7), 3738; https://doi.org/10.3390/s23073738 - 4 Apr 2023

Cited by 6 | Viewed by 2270

Abstract

A smart city has a complex hierarchical communication system with various components. It must meet the requirements of fast connection, reliability, and security without data compromise. Internet of Things technology is widely used to provide connectivity and control solutions for smart sensors and other devices using heterogeneous networking technologies. In this paper, we propose a routing solution for Wireless Sensor Networks (WSN) and Mobile Ad hoc NETworks (MANET) with increasing speed, reliability, and sufficient security. Many routing protocols have been proposed for WSNs and MANETs. We combine the Secret Sharing Schemes (SSS) and Redundant Residual Number Systems (RRNS) to provide an efficient mechanism for a Distributed dynamic heterogeneous network Transmission (DT) with new security and reliability routing protocol (DT-RRNS). We analyze the concept of data transmission based on RRNS that divides data into smaller encoded shares and transmits them in parallel, protecting them from attacks on routes by adaptive multipath secured transmission and providing self-correcting properties that improve the reliability and fault tolerance of the entire system. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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24 pages, 6664 KiB

Open AccessArticle

Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring

by Asmus Skar, Anders Vestergaard, Shahrzad M. Pour and Matteo Pettinari

Sensors 2023, 23(5), 2756; https://doi.org/10.3390/s23052756 - 2 Mar 2023

Cited by 4 | Viewed by 3005

Abstract

The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure’s impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle’s primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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16 pages, 8415 KiB

Open AccessArticle

Quality Management System for an IoT Meteorological Sensor Network—Application to Smart Seoul Data of Things (S-DoT)

by Moon-Soo Park and Kitae Baek

Sensors 2023, 23(5), 2384; https://doi.org/10.3390/s23052384 - 21 Feb 2023

Cited by 3 | Viewed by 2200

Abstract

Meteorological data with a high horizontal resolution are essential for user-specific weather application services, such as flash floods, heat waves, strong winds, and road ice, in urban areas. National meteorological observation networks, such as the Automated Synoptic Observing System (ASOS) and Automated Weather System (AWS), provide accurate but low horizontal resolution data to address urban-scale weather phenomena. Many megacities are constructing their own Internet of Things (IoT) sensor networks to overcome this limitation. This study investigated the status of the smart Seoul data of things (S-DoT) network and the spatial distribution of temperature on heatwave and coldwave event days. The temperature at above 90% of S-DoT stations was higher than that at the ASOS station, mainly because of different surface covers and surrounding local climate zones. A quality management system for an S-DoT meteorological sensor network (QMS-SDM) comprising pre-processing, basic quality control, extended quality control, and data reconstruction using spatial gap-filling was developed. The upper threshold temperatures for the climate range test were set higher than those adopted by the ASOS. A 10-digit flag for each data point was defined to discriminate between normal, doubtful, and erroneous data. Missing data at a single station were imputed using the Stineman method, and the data with spatial outliers were filled with values at three stations within 2 km. Using QMS-SDM, irregular and diverse data formats were changed to regular and unit-format data. QMS-SDM application increased the amount of available data by 20–30%, and significantly improved data availability for urban meteorological information services. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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25 pages, 9006 KiB

Open AccessArticle

Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation

by Kanwalpreet Kour, Deepali Gupta, Kamali Gupta, Divya Anand, Dalia H. Elkamchouchi, Cristina Mazas Pérez-Oleaga, Muhammad Ibrahim and Nitin Goyal

Sensors 2022, 22(22), 8905; https://doi.org/10.3390/s22228905 - 17 Nov 2022

Cited by 29 | Viewed by 4463

Abstract

The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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16 pages, 5492 KiB

Open AccessArticle

Measuring Relative Wind Speeds in Stratospheric Balloons with Cup Anemometers: The TASEC-Lab Mission

by Daniel Alfonso-Corcuera, Mikel Ogueta-Gutiérrez, Alejandro Fernández-Soler, David González-Bárcena and Santiago Pindado

Sensors 2022, 22(15), 5575; https://doi.org/10.3390/s22155575 - 26 Jul 2022

Cited by 10 | Viewed by 2196

Abstract

This paper shows wind speed measurements from the TASEC-Lab experiment in a stratospheric balloon mission. The mission was launched in July 2021 from León (Spain) aerodrome. Measurements of horizontal wind speed in relation to the balloon gondola were successfully carried out with a cup anemometer. According to the available literature, this is the first time a cup anemometer has been used in a stratospheric balloon mission. The results indicate the need to consider the horizontal wind speed from the balloon ascent phase for thermal calculations of the mission. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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Review

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25 pages, 2486 KiB

Open AccessReview

Cybersecurity and Cyber Forensics for Smart Cities: A Comprehensive Literature Review and Survey

by Kyounggon Kim, Istabraq Mohammed Alshenaifi, Sundaresan Ramachandran, Jisu Kim, Tanveer Zia and Abdulrazaq Almorjan

Sensors 2023, 23(7), 3681; https://doi.org/10.3390/s23073681 - 2 Apr 2023

Cited by 11 | Viewed by 9858

Abstract

Smart technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are being adopted in cities and transforming them into smart cities. In smart cities, various network technologies, such as the Internet and IoT, are combined to exchange real-time information, making the everyday lives of their residents more convenient. However, there is a lack of systematic research on cybersecurity and cyber forensics in smart cities. This paper presents a comprehensive review and survey of cybersecurity and cyber forensics for smart cities. We analysed 154 papers that were published from 2015 to 2022 and proposed a new framework based on a decade of related research papers. We identified four major areas and eleven sub-areas for smart cities. We found that smart homes and the IoT were the most active research areas within the cybersecurity field. Additionally, we found that research on cyber forensics for smart cities was relatively limited compared to that on cybersecurity. Since 2020, there have been many studies on the IoT (which is a technological component of smart cities) that have utilized machine learning and deep learning. Due to the transmission of large-scale data through IoT devices in smart cities, ML and DL are expected to continue playing critical roles in smart city research. Full article

(This article belongs to the Special Issue Smart Cities: Sensors and IoT)

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