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3 pages, 188 KiB

Open AccessEditorial

Special Issue on the Internet of Things (IoT) in Smart Cities

by Pierfrancesco Bellini and Gianni Pantaleo

Appl. Sci. 2023, 13(7), 4392; https://doi.org/10.3390/app13074392 - 30 Mar 2023

Viewed by 2659

Abstract

In recent years, smart cities have significantly developed and greatly expanded their potential [...] Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

Research

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17 pages, 5693 KiB

Open AccessArticle

Artificial Jellyfish Optimization with Deep-Learning-Driven Decision Support System for Energy Management in Smart Cities

by A. Al-Qarafi, Hadeel Alsolai, Jaber S. Alzahrani, Noha Negm, Lubna A. Alharbi, Mesfer Al Duhayyim, Heba Mohsen, M. Al-Shabi and Fahd N. Al-Wesabi

Appl. Sci. 2022, 12(15), 7457; https://doi.org/10.3390/app12157457 - 25 Jul 2022

Cited by 9 | Viewed by 1866

Abstract

A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of [...] Read more.

A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of energy models and their important role. The recent developments of machine learning (ML) and deep learning (DL) models pave the way to design effective energy management schemes. In this respect, this study introduces an artificial jellyfish optimization with deep learning-driven decision support system (AJODL-DSSEM) model for energy management in smart cities. The proposed AJODL-DSSEM model predicts the energy in the smart city environment. To do so, the proposed AJODL-DSSEM model primarily performs data preprocessing at the initial stage to normalize the data. Besides, the AJODL-DSSEM model involves the attention-based convolutional neural network-bidirectional long short-term memory (CNN-ABLSTM) model for the prediction of energy. For the hyperparameter tuning of the CNN-ABLSTM model, the AJO algorithm was applied. The experimental validation of the proposed AJODL-DSSEM model was tested using two open-access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the improved outcomes of the AJODL-DSSEM model over recent approaches. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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15 pages, 3488 KiB

Open AccessArticle

EmotIoT: An IoT System to Improve Users’ Wellbeing

by Javier Navarro-Alamán, Raquel Lacuesta, Iván García-Magariño and Jaime Lloret

Appl. Sci. 2022, 12(12), 5804; https://doi.org/10.3390/app12125804 - 7 Jun 2022

Cited by 10 | Viewed by 2677

Abstract

IoT provides applications and possibilities to improve people’s daily lives and business environments. However, most of these technologies have not been exploited in the field of emotions. With the amount of data that can be collected through IoT, emotions could be detected and [...] Read more.

IoT provides applications and possibilities to improve people’s daily lives and business environments. However, most of these technologies have not been exploited in the field of emotions. With the amount of data that can be collected through IoT, emotions could be detected and anticipated. Since the study of related works indicates a lack of methodological approaches in designing IoT systems from the perspective of emotions and smart adaption rules, we introduce a methodology that can help design IoT systems quickly in this scenario, where the detection of users is valuable. In order to test the methodology presented, we apply the proposed stages to design an IoT smart recommender system named EmotIoT. The system allows anticipating and predicting future users’ emotions using parameters collected from IoT devices. It recommends new activities for the user in order to obtain a final state. Test results validate our recommender system as it has obtained more than 80% accuracy in predicting future user emotions. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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45 pages, 9366 KiB

Open AccessArticle

Energy Centers in a Smart City as a Platform for the Application of Artificial Intelligence and the Internet of Things

by Bohumir Garlik

Appl. Sci. 2022, 12(7), 3386; https://doi.org/10.3390/app12073386 - 26 Mar 2022

Cited by 4 | Viewed by 3520

Abstract

A fundamental strategy of addressing the energy performance of buildings (EPB) and creating conditions for energy sustainability in applying renewable energy sources (RES) is the effective management of building technical equipment. The buildings in question are buildings within a cluster of buildings. The [...] Read more.

A fundamental strategy of addressing the energy performance of buildings (EPB) and creating conditions for energy sustainability in applying renewable energy sources (RES) is the effective management of building technical equipment. The buildings in question are buildings within a cluster of buildings. The building cluster can be considered as a unit that is, in the sense of the system concept, a part of a subdivision of the city and subsequently of the whole urban agglomeration. We are talking about smart cities. The control system of a building and, subsequently, smart cities is a process management system using artificial intelligence (AI). In order to achieve the desired effects, such as “near-zero energy buildings”, as required by the European Energy Performance of Buildings Directive (EPBD 3), the application of AI needs to be shifted towards wireless network connection, i.e., “Internet of Things” (IoT) application. The aim is to create conditions for reducing energy consumption, improving environmental comfort in buildings and reducing CO₂ emissions. This paper further analyzes the current state of IoT and the implementation thereof in the process management of sustainable energy at the smart city level as a basic element of a smart city system applied to building management. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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19 pages, 1886 KiB

Open AccessArticle

Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments

by Nikos Mitro, Maria Krommyda and Angelos Amditis

Appl. Sci. 2022, 12(5), 2315; https://doi.org/10.3390/app12052315 - 23 Feb 2022

Cited by 19 | Viewed by 3864

Abstract

The building materials of Cultural Heritage monuments are subjected to continuous degradation throughout the years, mainly due to their exposure to harsh and unexpected weather phenomena related to Climate Change. The specific climatic conditions at their vicinity, especially when there are local peculiarities [...] Read more.

The building materials of Cultural Heritage monuments are subjected to continuous degradation throughout the years, mainly due to their exposure to harsh and unexpected weather phenomena related to Climate Change. The specific climatic conditions at their vicinity, especially when there are local peculiarities such as onshore breeze, are of crucial importance for studying the deterioration rate and the identification of proper mitigation actions. Generalized models that are based on climate data can provide an insight on the deterioration but fail to offer a deeper understanding of this phenomenon. To this end, in the context of the EU-funded HYPERION project a distributed smart sensor network will be deployed at the Cultural Heritage monuments in four study areas as the solution to this problem. The developed system, which is demonstrated in this paper, includes smart IoT devices, called Smart Tags, designed to provide environmental measurements close to monuments, a middle-ware to facilitate the communication and a visualization platform where the collected information is presented. Last but not least, special focus is given to the device’s NB-IoT connectivity and its power efficiency by conducting various tests and extract useful conclusions. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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23 pages, 1338 KiB

Open AccessArticle

A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics

by Yazed Alsaawy, Ahmad Alkhodre, Adnan Abi Sen, Abdullah Alshanqiti, Wasim Ahmad Bhat and Nour Mahmoud Bahbouh

Appl. Sci. 2022, 12(4), 2043; https://doi.org/10.3390/app12042043 - 16 Feb 2022

Cited by 19 | Viewed by 5910

Abstract

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that [...] Read more.

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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12 pages, 799 KiB

Open AccessArticle

ECU-IoFT: A Dataset for Analysing Cyber-Attacks on Internet of Flying Things

by Mohiuddin Ahmed, David Cox, Benjamin Simpson and Aseel Aloufi

Appl. Sci. 2022, 12(4), 1990; https://doi.org/10.3390/app12041990 - 14 Feb 2022

Cited by 15 | Viewed by 5189

Abstract

There has been a significant increase in the adoption of unmanned aerial vehicles (UAV) within science, technology, engineering, and mathematics project-based learning. However, the risks that education providers place their student and staff under is often unknown or undocumented. Low-end consumer drones used [...] Read more.

There has been a significant increase in the adoption of unmanned aerial vehicles (UAV) within science, technology, engineering, and mathematics project-based learning. However, the risks that education providers place their student and staff under is often unknown or undocumented. Low-end consumer drones used within the education sector are vulnerable to state-of-the-art cyberattacks. Therefore, datasets are required to conduct further research to establish cyber defenses for UAVs used within the education sector. This paper showcases the development of the ECU-IoFT dataset, documenting three known cyber-attacks targeting Wi-Fi communications and the lack of security in an affordable off-the-shelf drone. At present, there are no publicly available labeled datasets that reflect cyberattacks on the Internet of Flying Things (IoFT). The majority of the publicly available network traffic datasets are emulated and do not reflect the scenarios/attacks from a real test setup. This dataset will be beneficial for both cybersecurity researchers to develop defense strategies and UAV manufacturers to design more secure products. In the future, endeavors will be taken to incorporate newer attacks and create datasets appropriate for big data analysis. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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16 pages, 1440 KiB

Open AccessArticle

Deep Reinforcement Learning-Based Spectrum Allocation Algorithm in Internet of Vehicles Discriminating Services

by Zheng Guan, Yuyang Wang and Min He

Appl. Sci. 2022, 12(3), 1764; https://doi.org/10.3390/app12031764 - 8 Feb 2022

Cited by 10 | Viewed by 2966

Abstract

With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which [...] Read more.

With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which a number of co-existing cellular users (CUs) and prioritized device-to-device (D2D) users are equipped in a single antenna vehicle-mounted communication network. To ensure a business-aware spectrum access mechanism with delay granted in a complex dynamic environment, we consider optimizing a metric that maintains a trade off between maximizing the total capacity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) links and minimizing the interference of high priority links. A low complexity priority-based spectrum allocation scheme based on the deep reinforcement learning method is developed to solve the proposed formulation. We trained our algorithm using the deep Q-learning network (DQN) over a set of public bandwidths. Simulation results show that the proposed scheme can allocate spectrum resources quickly and effectively in a high dynamic vehicle network environment. Concerning improved channel transmission rate, the V2V link rate in this scheme is 2.54 times that of the traditional random spectrum allocation scheme, and the V2I link rate is 13.5% higher than that of the traditional random spectrum allocation scheme. The average total interference received by priority links decreased by 14.2 dB compared to common links, realized service priority distinction and has good robustness to communication noise. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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17 pages, 611 KiB

Open AccessArticle

The Role of IoT Devices in Sustainable Car Expenses in the Context of the Intelligent Mobility: A Comparative Approach

by Javier Goikoetxea-Gonzalez, Diego Casado-Mansilla and Diego López-de-Ipiña

Appl. Sci. 2022, 12(3), 1080; https://doi.org/10.3390/app12031080 - 20 Jan 2022

Cited by 3 | Viewed by 3640

Abstract

Connected cars have often been defined as vehicles that can provide some services and information without human intervention. Several scholars have examined the factors that promote the purchase or adoption of such augmented vehicles. However, little emphasis has been placed on the determinants [...] Read more.

Connected cars have often been defined as vehicles that can provide some services and information without human intervention. Several scholars have examined the factors that promote the purchase or adoption of such augmented vehicles. However, little emphasis has been placed on the determinants for reducing car expenditures when a driver owns a car with an Internet of Things (IoT) device or a smart assistant in the context of smart mobility. Therefore, this article analyzes whether emerging technology such as IoT plays a key factor for a driver concerning the expenses related to the car (e.g., insurance, maintenance, and repairs). To this extent, a methodology based on exploratory (i) and confirmatory analysis (ii) was carried out. The authors initially conducted an exploratory phase by means of a Delphi method in which a group of vehicle experts (n = 25) were recruited to give their opinions and reach an agreement defining the determinants that they believed affected vehicle expenditures the most. Secondly, and taking into consideration that the salient determinant from the Delphi method was the use of technology and the warnings and alerts it triggers, a questionnaire was delivered to 556 drivers to analyze the everyday spending on their cars. Specifically, the survey aimed to compare the responses of people who own connected cars or have any kind of built-in IoT infrastructure (n = 302) with those of people with non-connected cars (n = 254). The main conclusion obtained for this latter approach was that drivers with a connected car have remarkably lower car expenses than those driving a conventional car. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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19 pages, 1366 KiB

Open AccessArticle

Smart Digital Forensic Readiness Model for Shadow IoT Devices

by Funmilola Ikeolu Fagbola and Hein S. Venter

Appl. Sci. 2022, 12(2), 730; https://doi.org/10.3390/app12020730 - 12 Jan 2022

Cited by 15 | Viewed by 3562

Abstract

Internet of Things (IoT) is the network of physical objects for communication and data sharing. However, these devices can become shadow IoT devices when they connect to an existing network without the knowledge of the organization’s Information Technology team. More often than not, [...] Read more.

Internet of Things (IoT) is the network of physical objects for communication and data sharing. However, these devices can become shadow IoT devices when they connect to an existing network without the knowledge of the organization’s Information Technology team. More often than not, when shadow devices connect to a network, their inherent vulnerabilities are easily exploited by an adversary and all traces are removed after the attack or criminal activity. Hence, shadow connections pose a challenge for both security and forensic investigations. In this respect, a forensic readiness model for shadow device-inclusive networks is sorely needed for the purposes of forensic evidence gathering and preparedness, should a security or privacy breach occur. However, the hidden nature of shadow IoT devices does not facilitate the effective adoption of the most conventional digital and IoT forensic methods for capturing and preserving potential forensic evidence that might emanate from shadow devices in a network. Therefore, this paper aims to develop a conceptual model for smart digital forensic readiness of organizations with shadow IoT devices. This model will serve as a prototype for IoT device identification, IoT device monitoring, as well as digital potential evidence capturing and preservation for forensic readiness. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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14 pages, 1653 KiB

Open AccessArticle

Urban Open Platform for Borderless Smart Cities

by Ralf-Martin Soe, Timo Ruohomäki and Henry Patzig

Appl. Sci. 2022, 12(2), 700; https://doi.org/10.3390/app12020700 - 11 Jan 2022

Cited by 7 | Viewed by 3394

Abstract

As a network of connected sensors to transform data into knowledge, Urban Platforms have been rooted in several smart city projects. However, this has often resulted in them being no more than IoT dashboards. More recently, there has been an increased interest in [...] Read more.

As a network of connected sensors to transform data into knowledge, Urban Platforms have been rooted in several smart city projects. However, this has often resulted in them being no more than IoT dashboards. More recently, there has been an increased interest in supporting the data governance and distributed architecture of Urban Platforms in order to adjust these with the administrative structure in a specific city. In addition, Urban Platforms also deal with data roaming between different stakeholders including other cities, different government levels, companies and citizens. Nevertheless, the first deployments have led to an inflexible “smart cities in a box” approach that does not help with building digital skills and causes vendor lock-in to products that do not scale. There is a need to start with simple and widespread urban services through a collaborative joint cross-border, hands-on effort. In order to meet the level of interoperability, international standards should be adopted. The aim of an Urban Open Platform (UOP), introduced in this paper, is to support not only data acquisition but also various types of data processing: data is aggregated, processed, manipulated and extended within the city context. Conceptually, special attention has been put on scalability, roaming and reliability in urban environments. This article introduces the UOP uniquely in the cross-border data exchange context of two European capital cities, Helsinki and Tallinn, and validates it with 10 real-life urban use cases. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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17 pages, 1825 KiB

Open AccessArticle

Embedded AI-Based Digi-Healthcare

by Zarlish Ashfaq, Rafia Mumtaz, Abdur Rafay, Syed Mohammad Hassan Zaidi, Hadia Saleem, Sadaf Mumtaz, Adnan Shahid, Eli De Poorter and Ingrid Moerman

Appl. Sci. 2022, 12(1), 519; https://doi.org/10.3390/app12010519 - 5 Jan 2022

Cited by 22 | Viewed by 6455

Abstract

Healthcare is an indispensable part of human life and chronic illnesses like cardiovascular diseases (CVD) have a deeply negative impact on the healthcare sector. Since the ever-growing population of chronic patients cannot be managed at hospitals, therefore, there is an urgent need for [...] Read more.

Healthcare is an indispensable part of human life and chronic illnesses like cardiovascular diseases (CVD) have a deeply negative impact on the healthcare sector. Since the ever-growing population of chronic patients cannot be managed at hospitals, therefore, there is an urgent need for periodic monitoring of vital parameters and apposite treatment of these patients. In this paper, an Internet of Medical Things (IoMT) -based remote patient monitoring system is proposed which is based on Artificial Intelligence (AI) and edge computing. The primary focus of this paper is to develop an embedded prototype that can be used for remote monitoring of cardiovascular patients. The system will continuously monitor physiological parameters like body temperature, heart rate, and blood oxygen saturation, and then report the health status to the authenticated users. The system employs edge computing to perform multiple functionalities including health status inference using a Machine Learning (ML) model which makes predictions on real-time data, alert notifications in case of an emergency, and transferring data between the sensor network and the cloud. A web-based application is developed for the depiction of raw data and ML results and to provide a direct communication channel between the patient and the doctor. The ML module achieved an accuracy of 96.26% on the test set using the K-Nearest Neighbors (KNNs) algorithm. This solution aims to address the sense of emergency due to the alarming statistics that highlight the mortality rate of cardiovascular patients. The project will enable a smart option based on IoT and ML to improve standards of living and prove crucial in saving human lives. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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14 pages, 1338 KiB

Open AccessArticle

Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm

by Yingying Fan, Haichao Wang, Xinyue Zhao, Qiaoran Yang and Yi Liang

Appl. Sci. 2021, 11(24), 12014; https://doi.org/10.3390/app112412014 - 16 Dec 2021

Cited by 13 | Viewed by 2147

Abstract

Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), [...] Read more.

Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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17 pages, 1097 KiB

Open AccessArticle

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

by Preeti Sirohi, Fahd N. Al-Wesabi, Haya Mesfer Alshahrani, Piyush Maheshwari, Amit Agarwal, Bhupesh Kumar Dewangan, Anwer Mustafa Hilal and Tanupriya Choudhury

Appl. Sci. 2021, 11(20), 9394; https://doi.org/10.3390/app11209394 - 10 Oct 2021

Cited by 25 | Viewed by 2406

Abstract

The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the [...] Read more.

The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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19 pages, 843 KiB

Open AccessArticle

Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application

by Sewoong Hwang, Yungyeong Song and Jonghyuk Kim

Appl. Sci. 2021, 11(6), 2707; https://doi.org/10.3390/app11062707 - 17 Mar 2021

Cited by 11 | Viewed by 5002

Abstract

This study indirectly verifies the possibility of telemedicine for humans through a mobile application (app) targeting pets. It examined the perception of telemedicine services and the current status of the companion animal industry, the app platform, and its applied technology by industry domain, [...] Read more.

This study indirectly verifies the possibility of telemedicine for humans through a mobile application (app) targeting pets. It examined the perception of telemedicine services and the current status of the companion animal industry, the app platform, and its applied technology by industry domain, and four representative types of artificial intelligence (AI) technologies applicable in the medical field. A survey was conducted through an app implementing pet telemedicine, and hypotheses were established and statistically tested based on the adoption period of pets, health status, mobile service utilization (as an index measuring the ease of use of recent AI functions), and positive and negative perceptions of telemedicine services. As revealed by prospect theory, users with a negative perception of pet telemedicine tended to maintain negative perceptions about telemedicine for humans. This study proved that the severity of pet diseases and the ease of use of recent AI technologies act as a moderating effect on the perception of telemedicine services through the verification of reinforcement and additional hypotheses. It suggests a plan to overcome sanctions against telemedicine by utilizing AI technology. A positive effect on changing the medical paradigm to telemedicine and the improvement of the medical legal system were also observed. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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Review

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19 pages, 268 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Internet of Things Platforms for Academic Research and Development: A Critical Review

by Luca De Nardis, Alireza Mohammadpour, Giuseppe Caso, Usman Ali and Maria-Gabriella Di Benedetto

Appl. Sci. 2022, 12(4), 2172; https://doi.org/10.3390/app12042172 - 19 Feb 2022

Cited by 18 | Viewed by 6631

Abstract

Tens of different IoT platforms are currently available on the market as a result of the high interest in IoT, characterized by very different characteristics in terms of utilization models, features and availability. This paper provides a review of existing platforms, both adopting [...] Read more.

Tens of different IoT platforms are currently available on the market as a result of the high interest in IoT, characterized by very different characteristics in terms of utilization models, features and availability. This paper provides a review of existing platforms, both adopting a closed source and an open source access model, focusing on five evaluation criteria: communication protocols, data visualization, data processing, integration with external services and security. Afterward, the paper focuses on ten open source platforms, that are deemed more suitable for research and development activities in academia, and provides an evaluation of such platforms according to the five criteria previously defined, combined with two criteria specific to open source platforms: installation procedure and documentation. The evaluation indicates that the FIWARE platform is the best suited platform when taking into account the combination of the seven criteria; other platforms might, however, be preferred, depending on the context, thanks to specific features such as native support for a programming language, or ease and flexibility in the installation procedure. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

21 pages, 1468 KiB

Open AccessReview

IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies

by Pierfrancesco Bellini, Paolo Nesi and Gianni Pantaleo

Appl. Sci. 2022, 12(3), 1607; https://doi.org/10.3390/app12031607 - 3 Feb 2022

Cited by 196 | Viewed by 29089

Abstract

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and [...] Read more.

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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16 pages, 1155 KiB

Open AccessReview

Smart Warehouses: Rationale, Challenges and Solution Directions

by Maarten van Geest, Bedir Tekinerdogan and Cagatay Catal

Appl. Sci. 2022, 12(1), 219; https://doi.org/10.3390/app12010219 - 27 Dec 2021

Cited by 29 | Viewed by 11433

Abstract

Smart warehouses aim to increase the overall service quality, productivity, and efficiency of the warehouse while minimizing costs and failures. In recent years, several studies have proposed and discussed different types of smart warehouses, identified key challenges, and proposed several solution directions for [...] Read more.

Smart warehouses aim to increase the overall service quality, productivity, and efficiency of the warehouse while minimizing costs and failures. In recent years, several studies have proposed and discussed different types of smart warehouses, identified key challenges, and proposed several solution directions for coping with these challenges. The objective of this article is to identify, evaluate, and synthesize the relevant studies discussing the design of smart warehouses and the transition to these new types of warehouses. We applied a systematic literature review (SLR) protocol to select primary studies. The SLR resulted in the identification of the domains in which smart warehouses are applied, key motivations for adopting smart warehouses, current distinctive characteristics of smart warehouses, currently adopted technologies for realizing smart warehouses, and challenges and strategies for transitioning to smart warehouses. To the best of our knowledge, no SLR paper has been published yet on smart warehouses, and therefore, this is timely research as organizations are nowadays transitioning to smart warehouses. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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23 pages, 1514 KiB

Open AccessReview

A Survey of GIS and IoT Integration: Applications and Architecture

by Jalal Safari Bazargani, Abolghasem Sadeghi-Niaraki and Soo-Mi Choi

Appl. Sci. 2021, 11(21), 10365; https://doi.org/10.3390/app112110365 - 4 Nov 2021

Cited by 12 | Viewed by 8319

Abstract

IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. [...] Read more.

IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. Specifically, this paper starts with investigating the role of GIS and IoT separately and jointly in different domains. Then, a review of GIS and IoT integration studies is provided to examine how GIS could be used in IoT architecture. The results showed that the capabilities of GIS in dealing with geospatial data and attributes along with offering visualization and analyzing tools make it possible to develop an integrated system benefiting from real-time data collection and real-time monitoring provided by IoT. The presented details would assist researchers in future studies on utilizing GIS and IoT at the same time. Full article

(This article belongs to the Special Issue Internet of Things (IoT) in Smart Cities)

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