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Emerging IoT Technologies for Smart Environments Ⅱ

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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 (30 July 2022) | Viewed by 24158

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Special Issue Editors

Prof. Dr. Luigi Patrono

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Department of Innovation Engineering, University of Salento, Via Monteroni, 73100 Lecce, Italy
Interests: Internet of Things; computer networks; cloud networks; RFID and BLE technologies; localization; smart environments; AAL systems
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Prof. Dr. Cosimo Distante

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Institute of Applied Sciences and Intelligent Systems “ScienceApp", Consiglio Nazionale delle Ricerche, c/o Dhitech Campus Universitario Ecotekne, Via Monteroni s/n, 73100 Lecce, Italy
Interests: computer vision; pattern recognition; video surveillance; object tracking; deep learning; audience measurements; visual interaction; human–robot interaction
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Dr. Luigi Atzori

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Guest Editor

Associate Professor, Department of Electric and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Interests: multimedia communications; computer networking (wireless and wireline); QoS management; next-generation network (NGN); wireless sensor networks; Internet of Things
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Dr. Aitor Almeida

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DeustoTech-Deusto Foundation, University of Deusto, Av. Universidades 24, 48007 Bilbao, Spain
Interests: social network analysis; data mining; machine learning; pervasive computing; context-aware computing; semantic web
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The next generation of the Internet is expected to interconnect and to make heterogeneous and smart objects interoperable in order to realize the Internet of Things (IoT). It aims to diffuse smart and pervasive environments able to offer innovative services in heterogeneous applicative scenarios, such as environmental monitoring, building automation, healthcare, smart cities, smart grids, logistics, and tourism. The combination among emerging wireless communication technologies, cloud-based software architecture, embedded systems, and artificial intelligence systems based on machine learning or deep learning, promises to carry out the digital transformation anywhere.

This Special Issue invites authors to submit original research or review articles mainly focused on the Internet of Things and smart environments. Potential interesting topics for this Special Issue include, but are not limited to the following:

IoT-aware systems based on wireless and wearable devices;
Embedded systems in IoT-aware system architectures;
Protocols performance analysis in IoT architectures;
Smart environments based on IoT Technologies;
Mobile applications and rapid prototyping in the IoT;
Middleware, semantic web, and ontology in the IoT;
Fog computing in the IoT;
Innovative AAL systems;
Microservices architectures;
Innovative solutions for industrial Internet of Things;
Big data and data analytics;
Intelligent transport systems;
Localization systems;
Safety and emergency systems based on IoT technologies;
Artificial intelligence systems based on machine learning or deep learning;
Case studies, field trials, and industrial applications.

Dr. Luigi Patrono
Dr. Cosimo Distante
Dr. Luigi Atzori
Dr. Aitor Almeida
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (9 papers)

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Research

24 pages, 3633 KiB

Open AccessArticle

CETS: Enabling Sustainable IoT with Cooperative Energy Transfer Schedule towards 6G Era

by Raja Sravan Kumar Kovvali and Gopikrishnan Sundaram

Sensors 2022, 22(17), 6584; https://doi.org/10.3390/s22176584 - 31 Aug 2022

Viewed by 1533

Abstract

The large scale of the Internet of Things necessitates using long-lasting physical layer devices for data collection. Deploying large numbers of Wi-Fi-enabled devices is expensive, so the Internet of Everything (IoE) is equipped with multiple communication modules to collect data where Wi-Fi is unavailable. However, because of their extended communication capabilities, IoE devices face energy limitations. As a result, IoE devices must be provided with the necessary energy resources. This paper introduces a novel multi-hop cooperation communication mechanism for Wireless Energy Transfer (WET) in the Wireless Powered-Internet of Everything (WP-IoE). IoE devices are outfitted here with various communication devices such as RF, Bluetooth, and Wi-Fi. This research proposes a two-phase energy transmission schedule to address the energy requirements. For data collection, the first phase provides a distributed tree-based data communication plan. The proposed model’s second phase used the reverse data collection protocol to implement wireless energy transmission. By combining these two phases, an optimized WET framework was created without unmanned aerial vehicles or robots. The experimental findings show that the proposed method in this research increases the average lifetime of the network and has a more significant charge latency and average charge throughput than other models. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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25 pages, 1485 KiB

Open AccessArticle

Smart and Adaptive Architecture for a Dedicated Internet of Things Network Comprised of Diverse Entities: A Proposal and Evaluation

by Shailesh Pratap Singh, Nauman Bin Ali and Lars Lundberg

Sensors 2022, 22(8), 3017; https://doi.org/10.3390/s22083017 - 14 Apr 2022

Cited by 3 | Viewed by 2062

Abstract

Advances in 5G and the Internet of Things (IoT) have to cater to the diverse and varying needs of different stakeholders, devices, sensors, applications, networks, and access technologies that come together for a dedicated IoT network for a synergistic purpose. Therefore, there is a need for a solution that can assimilate the various requirements and policies to dynamically and intelligently orchestrate them in the dedicated IoT network. Thus we identify and describe a representative industry-relevant use case for such a smart and adaptive environment through interviews with experts from a leading telecommunication vendor. We further propose and evaluate candidate architectures to achieve dynamic and intelligent orchestration in such a smart environment using a systematic approach for architecture design and by engaging six senior domain and IoT experts. The candidate architecture with an adaptive and intelligent element (“Smart AAA agent”) was found superior for modifiability, scalability, and performance in the assessments. This architecture also explores the enhanced role of authentication, authorization, and accounting (AAA) and makes the base for complete orchestration. The results indicate that the proposed architecture can meet the requirements for a dedicated IoT network, which may be used in further research or as a reference for industry solutions. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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20 pages, 1524 KiB

Open AccessArticle

An IoT-Aware Solution to Support Governments in Air Pollution Monitoring Based on the Combination of Real-Time Data and Citizen Feedback

by Teodoro Montanaro, Ilaria Sergi, Matteo Basile, Luca Mainetti and Luigi Patrono

Sensors 2022, 22(3), 1000; https://doi.org/10.3390/s22031000 - 27 Jan 2022

Cited by 11 | Viewed by 3732

Abstract

One of the main concerns of the last century is regarding the air pollution and its effects caused on human health. Its impact is particularly evident in cities and urban areas where governments are trying to mitigate its effects. Although different solutions have been already proposed, citizens continue to report bad conditions in the areas in which they live. This paper proposes a solution to support governments in monitoring the city pollution through the combination of user feedbacks/reports and real-time data acquired through dedicated mobile IoT sensors dynamically re-located by government officials to verify the reported conditions of specific areas. The mobile devices leverage on dedicated sensors to monitor the air quality and capture main roads traffic conditions through machine learning techniques. The system exposes a mobile application and a website to support the collection of citizens’ reports and show gathered data to both institutions and end-users. A proof-of-concept of the proposed solution has been prototyped in a medium-sized university campus. Both the performance and functional validation have demonstrated the feasibility and the effectiveness of the system and allowed the definition of some lessons learned, as well as future works. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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14 pages, 360 KiB

Open AccessFeature PaperArticle

A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments

by Aitor Almeida, Unai Bermejo, Aritz Bilbao, Gorka Azkune, Unai Aguilera, Mikel Emaldi, Fadi Dornaika and Ignacio Arganda-Carreras

Sensors 2022, 22(3), 701; https://doi.org/10.3390/s22030701 - 18 Jan 2022

Cited by 6 | Viewed by 2219

Abstract

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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26 pages, 1273 KiB

Open AccessArticle

Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment

by Petar Krivic, Mario Kusek, Igor Cavrak and Pavle Skocir

Sensors 2022, 22(2), 465; https://doi.org/10.3390/s22020465 - 08 Jan 2022

Cited by 6 | Viewed by 2316

Abstract

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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22 pages, 4328 KiB

Open AccessArticle

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

by Juan S. Angarita-Zapata, Gina Maestre-Gongora and Jenny Fajardo Calderín

Sensors 2021, 21(24), 8401; https://doi.org/10.3390/s21248401 - 16 Dec 2021

Cited by 10 | Viewed by 2714

Abstract

Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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23 pages, 2821 KiB

Open AccessArticle

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

by Sebastian Wilhelm and Jakob Kasbauer

Sensors 2021, 21(23), 8036; https://doi.org/10.3390/s21238036 - 01 Dec 2021

Cited by 5 | Viewed by 2905

Abstract

Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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29 pages, 915 KiB

Open AccessArticle

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation

by Andres Munoz-Arcentales, Sonsoles López-Pernas, Javier Conde, Álvaro Alonso, Joaquín Salvachúa and Juan José Hierro

Sensors 2021, 21(21), 7095; https://doi.org/10.3390/s21217095 - 26 Oct 2021

Cited by 10 | Viewed by 2970

Abstract

In recent years, many proposals of context-aware systems applied to IoT-based smart environments have been presented in the literature. Most previous works provide a generic high-level structure of how a context-aware system can be operationalized, but do not offer clues on how to implement it. On the other hand, there are many implementations of context-aware systems applied to specific IoT-based smart environments that are context-specific: it is not clear how they can be extended to other use cases. In this article, we aim to provide an open-source reference implementation for providing context-aware data analytics capabilities to IoT-based smart environments. We rely on the building blocks of the FIWARE ecosystem and the NGSI data standard, providing an agnostic end-to-end solution that considers the complete data lifecycle, covering from data acquisition and modeling, to data reasoning and dissemination. In other words, our reference implementation can be readily operationalized in any IoT-based smart environment regardless of its field of application, providing a context-aware solution that is not context-specific. Furthermore, we provide two example use cases that showcase how our reference implementation can be used in a variety of fields. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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18 pages, 1639 KiB

Open AccessArticle

Toward an Adaptive Threshold on Cooperative Bandwidth Management Based on Hierarchical Reinforcement Learning

by Motahareh Mobasheri, Yangwoo Kim and Woongsup Kim

Sensors 2021, 21(21), 7053; https://doi.org/10.3390/s21217053 - 25 Oct 2021

Cited by 1 | Viewed by 1652

Abstract

With the increase in Internet of Things (IoT) devices and network communications, but with less bandwidth growth, the resulting constraints must be overcome. Due to the network complexity and uncertainty of emergency distribution parameters in smart environments, using predetermined rules seems illogical. Reinforcement learning (RL), as a powerful machine learning approach, can handle such smart environments without a trainer or supervisor. Recently, we worked on bandwidth management in a smart environment with several fog fragments using limited shared bandwidth, where IoT devices may experience uncertain emergencies in terms of the time and sequence needed for more bandwidth for further higher-level communication. We introduced fog fragment cooperation using an RL approach under a predefined fixed threshold constraint. In this study, we promote this approach by removing the fixed level of restriction of the threshold through hierarchical reinforcement learning (HRL) and completing the cooperation qualification. At the first learning hierarchy level of the proposed approach, the best threshold level is learned over time, and the final results are used by the second learning hierarchy level, where the fog node learns the best device for helping an emergency device by temporarily lending the bandwidth. Although equipping the method to the adaptive threshold and restricting fog fragment cooperation make the learning procedure more difficult, the HRL approach increases the method’s efficiency in terms of time and performance. Full article

(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments Ⅱ)

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