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Emerging IoT Technologies for Smart Environments

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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 December 2019) | Viewed by 40528

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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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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
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Prof. Dr. Cosimo Distante

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Guest Editor

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
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.

With this Special Issue, we invite 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:

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

Manuscript Submission Information

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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.

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Published Papers (8 papers)

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Research

32 pages, 7932 KiB

Open AccessArticle

MicroServices Suite for Smart City Applications

by Claudio Badii, Pierfrancesco Bellini, Angelo Difino, Paolo Nesi, Gianni Pantaleo and Michela Paolucci

Sensors 2019, 19(21), 4798; https://doi.org/10.3390/s19214798 - 4 Nov 2019

Cited by 31 | Viewed by 9233

Abstract

Smart Cities are approaching the Internet of Things (IoT) World. Most of the first-generation Smart City solutions are based on Extract Transform Load (ETL); processes and languages that mainly support pull protocols for data gathering. IoT solutions are moving forward to event-driven processes using push protocols. Thus, the concept of IoT applications has turned out to be widespread; but it was initially “implemented” with ETL; rule-based solutions; and finally; with true data flows. In this paper, these aspects are reviewed, highlighting the requirements for smart city IoT applications and in particular, the ones that implement a set of specific MicroServices for IoT Applications in Smart City contexts. Moreover; our experience has allowed us to implement a suite of MicroServices for Node-RED; which has allowed for the creation of a wide range of new IoT applications for smart cities that includes dashboards, IoT Devices, data analytics, discovery, etc., as well as a corresponding Life Cycle. The proposed solution has been validated against a large number of IoT applications, as it can be verified by accessing the https://www.Snap4City.org portal; while only three of them have been described in the paper. In addition, the reported solution assessment has been carried out by a number of smart city experts. The work has been developed in the framework of the Select4Cities PCP (PreCommercial Procurement), funded by the European Commission as Snap4City platform. Full article

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

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22 pages, 6671 KiB

Open AccessArticle

A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments

by Daniela Sousa, Diego Hernandez, Francisco Oliveira, Miguel Luís and Susana Sargento

Sensors 2019, 19(21), 4695; https://doi.org/10.3390/s19214695 - 29 Oct 2019

Cited by 16 | Viewed by 5749

Abstract

The Internet of Things (IoT) is a rapidly evolving technology that is changing almost every business, and aquaculture is no exception. In this work we present an integrated IoT platform for the acquisition of environmental data and the monitoring of aquaculture environments, supported by a real-time communication and processing network. The complete monitoring platform consists of environmental sensors equipped in a swarm of mobile Unmanned Surface Vehicles (USVs) and Buoys, capable of collecting aquatic and outside information, and sending it to a central station where it will be stored and processed. The sensing platform, formed by the USVs and Buoys, are equipped with multi-communication technology: IEEE 802.11n (Wi-Fi) and Bluetooth for short range communication, for mission delegation and the transmission of data collection, and LoRa for periodic report. On the back-end side, supported by FIWARE technology, an interactive web-based platform can be used to define sensing missions and for data visualization. Results on the sensing platform lifetime, mission control and delay processing time are presented to assess the performance of the aquatic monitoring system. Full article

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

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24 pages, 21658 KiB

Open AccessArticle

A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT

by Marzieh Jalal Abadi, Luca Luceri, Mahbub Hassan, Chun Tung Chou and Monica Nicoli

Sensors 2019, 19(21), 4609; https://doi.org/10.3390/s19214609 - 23 Oct 2019

Cited by 5 | Viewed by 3550

Abstract

This paper presents a system based on pedestrian dead reckoning (PDR) for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a novel cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple devices. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth’s magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. Compared to other methods in the literature, the method has the advantage of being infrastructure-free, fully distributed and robust to sensor failures thanks to the pre-filtering of perturbed measurements. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance. Full article

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

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19 pages, 1063 KiB

Open AccessArticle

Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs

by Taewoon Kim, Chanjun Chun and Wooyeol Choi

Sensors 2019, 19(20), 4415; https://doi.org/10.3390/s19204415 - 12 Oct 2019

Cited by 4 | Viewed by 4296

Abstract

In networking systems such as cloud radio access networks (C-RAN) where users receive the connection and data service from short-range, light-weight base stations (BSs), users’ mobility has a significant impact on their association with BSs. Although communicating with the closest BS may yield the most desirable channel conditions, such strategy can lead to certain BSs being over-populated while leaving remaining BSs under-utilized. In addition, mobile users may encounter frequent handovers, which imposes a non-negligible burden on BSs and users. To reduce the handover overhead while balancing the traffic loads between BSs, we propose an optimal user association strategy for a large-scale mobile Internet of Things (IoT) network operating on C-RAN. We begin with formulating an optimal user association scheme focusing only on the task of load balancing. Thereafter, we revise the formulation such that the number of handovers is minimized while keeping BSs well-balanced in terms of the traffic load. To evaluate the performance of the proposed scheme, we implement a discrete-time network simulator. The evaluation results show that the proposed optimal user association strategy can significantly reduce the number of handovers, while outperforming conventional association schemes in terms of load balancing. Full article

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

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26 pages, 2341 KiB

Open AccessArticle

Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System

by Faisal Jamil and Do Hyeun Kim

Sensors 2019, 19(18), 3946; https://doi.org/10.3390/s19183946 - 12 Sep 2019

Cited by 34 | Viewed by 4919

Abstract

The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha–beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of RMSE. Full article

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

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22 pages, 9806 KiB

Open AccessArticle

Real-Time User Identification and Behavior Prediction Based on Foot-Pad Recognition

by Kuk Ho Heo, Seol Young Jeong and Soon Ju Kang

Sensors 2019, 19(13), 2899; https://doi.org/10.3390/s19132899 - 30 Jun 2019

Cited by 11 | Viewed by 2895

Abstract

In the IoT (Internet of things)-based smart home, the technology for recognizing individual users among family members is very important. Although research in areas such as image recognition, biometrics, and individual wireless devices is very active, these systems suffer from various problems such as the need to follow an intentional procedure or own a specific device. Furthermore, with a centralized server system for IoT service, it is difficult to guarantee real-time determinism with high accuracy. To overcome these problems, we suggest a method of recognizing users in real time from the foot pressure characteristics measured as a user steps on a footpad. The proposed model in this paper uses a preprocessing algorithm to determine and generalize the angle of foot pressure. Based on this generalized foot pressure angle, we extract nine features that can distinguish individual human beings, and employ these features in user-recognition algorithms. Performance evaluation of the model was conducted by combining two preprocessing algorithms used to generalize the angle with four user-recognition algorithms. Full article

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

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27 pages, 2520 KiB

Open AccessArticle

A Strongly Unforgeable Certificateless Signature Scheme and Its Application in IoT Environments

by Xiaodong Yang, Xizhen Pei, Guilan Chen, Ting Li, Meiding Wang and Caifen Wang

Sensors 2019, 19(12), 2692; https://doi.org/10.3390/s19122692 - 14 Jun 2019

Cited by 14 | Viewed by 3236

Abstract

With the widespread application of the Internet of Things (IoT), ensuring communication security for IoT devices is of considerable importance. Since IoT data are vulnerable to eavesdropping, tampering, forgery, and other attacks during an open network transmission, the integrity and authenticity of data are fundamental security requirements in the IoT. A certificateless signature (CLS) is a viable solution for providing data integrity, data authenticity, and identity identification in resource-constrained IoT devices. Therefore, designing a secure and efficient CLS scheme for IoT environments has become one of the main objectives of IoT security research. However, the existing CLS schemes rarely focus on strong unforgeability and replay attacks. Herein, we design a novel CLS scheme to protect the integrity and authenticity of IoT data. In addition to satisfying the strong unforgeability requirement, the proposed scheme also resists public key replacement attacks, malicious-but-passive key-generation-centre attacks, and replay attacks. Compared with other related CLS schemes without random oracles, our CLS scheme has a shorter private key, stronger security, and lower communication and computational costs. Full article

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

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25 pages, 966 KiB

Open AccessArticle

Secure Three-Factor Authentication Protocol for Multi-Gateway IoT Environments

by JoonYoung Lee, SungJin Yu, KiSung Park, YoHan Park and YoungHo Park

Sensors 2019, 19(10), 2358; https://doi.org/10.3390/s19102358 - 22 May 2019

Cited by 58 | Viewed by 5573

Abstract

Internet of Things (IoT) environments such as smart homes, smart factories, and smart buildings have become a part of our lives. The services of IoT environments are provided through wireless networks to legal users. However, the wireless network is an open channel, which is insecure to attacks from adversaries such as replay attacks, impersonation attacks, and invasions of privacy. To provide secure IoT services to users, mutual authentication protocols have attracted much attention as consequential security issues, and numerous protocols have been studied. In 2017, Bae et al. presented a smartcard-based two-factor authentication protocol for multi-gateway IoT environments. However, we point out that Bae et al.’s protocol is vulnerable to user impersonation attacks, gateway spoofing attacks, and session key disclosure, and cannot provide a mutual authentication. In addition, we propose a three-factor mutual authentication protocol for multi-gateway IoT environments to resolve these security weaknesses. Then, we use Burrows–Abadi–Needham (BAN) logic to prove that the proposed protocol achieves secure mutual authentication, and we use the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to analyze a formal security verification. In conclusion, our proposed protocol is secure and applicable in multi-gateway IoT environments. Full article

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

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