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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Internet of Things (IoT)".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 64097

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Dr. Spyros Panagiotakis

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Guest Editor

Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 70013 Heraklion, Greece
Interests: communications and networking; Internet of Things; pervasive and physical computing; sensor networks; industrial informatics; location and context awareness; informatics in education
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Dr. Evangelos K. Markakis

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Guest Editor

Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 70013 Heraklion, Greece
Interests: edge networking; cyber security; public safety; digital video broadcasting; edge computing; SDN; NFV; Internet of Things; network management; network virtualization
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Special Issue Information

Dear Colleagues,

In the Internet of Things era we have entered, the closed-loop “Monitoring–Decision–Execution” cycle of typical automation systems is extended, so it includes distributed developments that might scale from a smart home or greenhouse to a smart city and from autonomous driving to emergency management. In such highly distributed and scalable architectures, each of the three aforementioned processes can take place isolated from the others, situated at any physical or virtual computing system and located, ideally, at any place. Hence, communication and interoperation between the subsystems that comprise the total system, namely extreme edge, edge, fog, and cloud deployments, is of critical importance. To this end, new machine-to-machine protocols, as well as emerging serverless and decentralized architectures, enable the formation of ad-hoc user groups for personalized communication and interaction.

Context awareness is of equal importance, since it enables situation awareness, event recognition, and pervasiveness across the system. The latter can dynamically provide customized service provision to end-users via content adaptation to the user situation and needs. Toward this direction, modern sensor technology extends typical ambient sensing to social and cyber sensing triggering various interactions among connected devices or human beings. The same happens with modern human–computer interfaces that bring input and output capabilities to a plethora of everyday items, transforming them to enchanting and intelligent ones. In parallel, the crowdsensing paradigm vividly emerges on top of various networking topologies as a means for rapidly enabling social sensing. Very recently, researchers have promised the implementation of a full Internet of Senses (IoS) by 2030, where not only typical data will be transferred over the network but also triggers concerning senses like taste, smell, touch, etc.

Despite the richness of available data, however, the key problem for application designers remains the same: How to fuse and mine reliable information from data collected from largely unknown and possibly unreliable sources or how to dynamically extract user preferences, behaviors, and needs from the received events beyond the maintenance of static user profiles. Furthermore, recent advances in IoT management platforms, microcontrollers, and data science bring machine learning and computational intelligence closer to the source of data generation (end users, fog layers, edge, and extreme edge), enabling broader context awareness. However, despite the progress made to date, we are still far from providing low-power autonomous IoT devices which are able to deal with a large amount of data processing or a frequent need for communication or both.

The goal of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in pervasive computing across the different parts of the IoT ecosystem. We solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include but are not limited to the following:

Advances in sensors networking for the IoT;
Advances in sensors technologies for the IoT;
Advances in wireless communications for the IoT;
Advances in networking for the IoT;
Internet of Multimedia Things;
Data compression for the IoT;
Low-power and energy-efficient IoT computing;
Cloud/fog/edge/extreme edge computing for the IoT;
Network management for the IoT;
Network function virtualization and software-defined networking for the IoT;
Privacy and security issues for the IoT;
Context and situation awareness in IoT;
Dynamic user profilng for the IoT;
Adaptive service provision for the IoT;
Sensor and data fusion for the IoT;
Pervasive and ubiquitous computing for the IoT;
Advances in human–computer interfaces for the IoT;
Machine learning and computational intelligence for the IoT;
Decision support systems for the IoT;
Data analytics for the IoT;
Advances in physical computing;
Critical infrastructure IoT;
Advances in STEM education for the IoT;
Serverless and decentralized applications for the IoT;
Crowdsensing and social sensing for IoT applications;
Internet of Senses.

Dr. Spyros Panagiotakis
Dr. Evangelos K. Markakis
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. Information is an international peer-reviewed open access monthly 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 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Internet of Things
Pervasive computing
Ubiquitous computing
Sensor networks
Wireless communications
LPWAN communications
Internet of Multimedia Things
Cloud/fog/edge/extreme edge computing
Network function virtualization and software-defined networking
Machine learning and computational intelligence
Privacy and security
Context and situation awareness
User profiling
Adaptive service provision
Sensors and data fusion
Human-computer Interaction
Data analytics
Physical computing
Distributed computing
Crowdsensing
Social sensing
Internet of Senses

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Pervasive Computing in IoT, 2nd Edition in Information

Published Papers (13 papers)

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Editorial

Jump to: Research, Review

3 pages, 151 KiB

Open AccessEditorial

An Editorial for the Special Issue “Pervasive Computing in IoT”

by Spyros Panagiotakis and Evangelos K. Markakis

Information 2024, 15(6), 320; https://doi.org/10.3390/info15060320 - 30 May 2024

Viewed by 405

Abstract

In the era of Internet of Things (IoT) we have entered, the “Monitoring–Decision–Execution” cycle of typical autonomic and automation systems is extended, so it includes distributed developments that might scale from a smart home or greenhouse to a smart city and from autonomous [...] Read more.

(This article belongs to the Special Issue Pervasive Computing in IoT)

Research

Jump to: Editorial, Review

23 pages, 3200 KiB

Open AccessArticle

Towards a Supervised Remote Laboratory Platform for Teaching Microcontroller Programming

by Manos Garefalakis, Zacharias Kamarianakis and Spyros Panagiotakis

Information 2024, 15(4), 209; https://doi.org/10.3390/info15040209 - 8 Apr 2024

Viewed by 1456

Abstract

As it concerns remote laboratories (RLs) for teaching microcontroller programming, the related literature reveals several common characteristics and a common architecture. Our search of the literature was constrained to papers published in the period of 2020–2023 specifically on remote laboratories related to the subject of teaching microcontroller programming of the Arduino family. The objective of this search is to present, on the one hand, the extent to which the RL platform from the Hellenic Mediterranean University (HMU-RLP) for Arduino microcontroller programming conforms to this common architecture and, on the other hand, how it extends this architecture with new features for monitoring and assessing users’ activities over remote labs in the context of pervasive and supervised learning. The HMU-RLP hosts a great number of experiments that can be practiced by RL users in the form of different scenarios provided by teachers as activities that users can perform in their self-learning process or assigned as exercises complementary to the theoretical part of a course. More importantly, it provides three types of assessments of the code users program during their experimentation with RLs. The first type monitors each action users perform over the web page offered by the RL. The second type monitors the activities of users at the hardware level. To this end, a shadow microcontroller is used that monitors the pins of the microcontroller programmed by the users. The third type automatically assesses the code uploaded by the users, checking its similarity with the prototype code uploaded by the instructors. A trained AI model is used to this end. For the assessments provided by the HMU-RLP, the experience API (xAPI) standard is exploited to store users’ learning analytics (LAs). The LAs can be processed by the instructors for the students’ evaluation and personalized learning. The xAPI reporting and visualization tools used in our prototype RLP implementation are also presented in the paper. We also discuss the planned development of such functionalities in the future for the use of the HMU-RLP as an adaptive tool for supervised distant learning. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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11 pages, 604 KiB

Open AccessArticle

IoT Device Identification Using Unsupervised Machine Learning

by Carson Koball, Bhaskar P. Rimal, Yong Wang, Tyler Salmen and Connor Ford

Information 2023, 14(6), 320; https://doi.org/10.3390/info14060320 - 31 May 2023

Cited by 6 | Viewed by 4696

Abstract

Device identification is a fundamental issue in the Internet of Things (IoT). Many critical services, including access control and intrusion prevention, are built on correctly identifying each unique device in a network. However, device identification faces many challenges in the IoT. For example, a common technique to identify a device in a network is using the device’s MAC address. However, MAC addresses can be easily spoofed. On the other hand, IoT devices also include dynamic characteristics such as traffic patterns which could be used for device identification. Machine-learning-assisted approaches are promising for device identification since they can capture dynamic device behaviors and have automation capabilities. Supervised machine-learning-assisted techniques demonstrate high accuracies for device identification. However, they require a large number of labeled datasets, which can be a challenge. On the other hand, unsupervised machine learning can also reach good accuracies without requiring labeled datasets. This paper presents an unsupervised machine-learning approach for IoT device identification. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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12 pages, 362 KiB

Open AccessArticle

Federated Blockchain Learning at the Edge

by James Calo and Benny Lo

Information 2023, 14(6), 318; https://doi.org/10.3390/info14060318 - 30 May 2023

Cited by 1 | Viewed by 1906

Abstract

Machine learning, particularly using neural networks, is now widely adopted in practice even with the IoT paradigm; however, training neural networks at the edge, on IoT devices, remains elusive, mainly due to computational requirements. Furthermore, effective training requires large quantities of data and privacy concerns restrict accessible data. Therefore, in this paper, we propose a method leveraging a blockchain and federated learning to train neural networks at the edge effectively bypassing these issues and providing additional benefits such as distributing training across multiple devices. Federated learning trains networks without storing any data and aggregates multiple networks, trained on unique data, forming a global network via a centralized server. By leveraging the decentralized nature of a blockchain, this centralized server is replaced by a P2P network, removing the need for a trusted centralized server and enabling the learning process to be distributed across participating devices. Our results show that networks trained in such a manner have negligible differences in accuracy compared to traditionally trained networks on IoT devices and are less prone to overfitting. We conclude that not only is this a viable alternative to traditional paradigms but is an improvement that contains a wealth of benefits in an ecosystem such as a hospital. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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25 pages, 7200 KiB

Open AccessArticle

An Edge Device Framework in SEMAR IoT Application Server Platform

by Yohanes Yohanie Fridelin Panduman, Nobuo Funabiki, Sho Ito, Radhiatul Husna, Minoru Kuribayashi, Mitsuhiro Okayasu, Junya Shimazu and Sritrusta Sukaridhoto

Information 2023, 14(6), 312; https://doi.org/10.3390/info14060312 - 29 May 2023

Cited by 3 | Viewed by 1886

Abstract

Nowadays, the Internet of Things (IoT) has become widely used at various places and for various applications. To facilitate this trend, we have developed the IoT application server platform called SEMAR (Smart Environmental Monitoring and Analytical in Real-Time), which offers standard features for collecting, displaying, and analyzing sensor data. An edge device is usually installed to connect sensors with the server, where the interface configuration, the data processing, the communication protocol, and the transmission interval need to be defined by the user. In this paper, we proposed an edge device framework for SEMAR to remotely optimize the edge device utilization with three phases. In the initialization phase, it automatically downloads the configuration file to the device through HTTP communications. In the service phase, it converts data from various sensors into the standard data format and sends it to the server periodically. In the update phase, it remotely updates the configuration through MQTT communications. For evaluations, we applied the proposal to the fingerprint-based indoor localization system (FILS15.4) and the data logging system. The results confirm the effectiveness in utilizing SEMAR to develop IoT application systems. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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26 pages, 2762 KiB

Open AccessArticle

A Blockchain-Inspired Attribute-Based Zero-Trust Access Control Model for IoT

by Samia Masood Awan, Muhammad Ajmal Azad, Junaid Arshad, Urooj Waheed and Tahir Sharif

Information 2023, 14(2), 129; https://doi.org/10.3390/info14020129 - 16 Feb 2023

Cited by 23 | Viewed by 6126

Abstract

The connected or smart environment is the integration of smart devices (sensors, IoT devices, or actuator) into the Internet of Things (IoT) paradigm, in which a large number of devices are connected, monitoring the physical environment and processes and transmitting into the centralized database for advanced analytics and analysis. This integrated and connected setup allows greater levels of automation of smart systems than is possible with just the Internet. While delivering services to the different processes and application within connected smart systems, these IoT devices perform an impeccably large number of device-to-device communications that allow them to access the selected subsets of device information and data. The sensitive and private nature of these data renders the smart infrastructure vulnerable to copious attacks which threat agents exploit for cyberattacks which not only affect critical services but probably bring threat to people’s lives. Hence, advanced measures need to be taken for securing smart environments, such as dynamic access control, advanced network screening, and monitoring behavioural anomalies. In this paper, we have discussed the essential cyberthreats and vulnerabilities in smart environments and proposed ZAIB (Zero-Trust and ABAC for IoT using Blockchain), a novel secure framework that monitors and facilitates device-to-device communications with different levels of access-controlled mechanisms based on environmental parameters and device behaviour. It is protected by zero-trust architecture and provides dynamic behavioural analysis of IoT devices by calculating device trust levels for each request. ZAIB enforces variable policies specifically generated for each scenario by using attribute-based access control (ABAC). We have used blockchain to ensure anonymous device and user registrations and immutable activity logs. All the attributes, trust level histories, and data generated by IoT devices are protected using IPFS. Finally, a security evaluation shows that ZAIB satisfies the needs of active defence and end-to-end security enforcement of data, users, and services involved in a smart grid network. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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24 pages, 16232 KiB

Open AccessArticle

Construction of a Low-Cost Layered Interactive Dashboard with Capacitive Sensing

by Agapi Tsironi Lamari, Spyros Panagiotakis, Zacharias Kamarianakis, George Loukas, Athanasios Malamos and Evangelos Markakis

Information 2022, 13(6), 304; https://doi.org/10.3390/info13060304 - 17 Jun 2022

Viewed by 2489

Abstract

In the present work, a methodology for the low-cost crafting of an interactive layered dashboard is proposed. Our aim is that the tangible surface be constructed using domestic materials that are easily available in every household. Several tests were performed on different capacitive materials before the selection of the most suitable one for use as a capacitive touch sensor. Various calibration methods were evaluated so that the behavior of the constructed capacitive touch sensors is smooth and reliable. The layered approach is achieved by a menu of few touch buttons on the left side of the dashboard. Thus, various different layers of content can be projected over the same construction, offering extendibility and ease of use to the users. For demonstration purposes, we developed an entertaining plus an educational application of projection mapping for the pervasive and interactive projection of multimedia content to the users of the presented tangible interface. The whole design and implementation approach are thoroughly analyzed in the paper and are presented through the illustration and application of various multimedia layers over the dashboard. An evaluation of the final construction proves the feasibility of the proposed work. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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14 pages, 758 KiB

Open AccessArticle

A New Data-Preprocessing-Related Taxonomy of Sensors for IoT Applications

by Paul D. Rosero-Montalvo, Vivian F. López-Batista and Diego H. Peluffo-Ordóñez

Information 2022, 13(5), 241; https://doi.org/10.3390/info13050241 - 9 May 2022

Cited by 9 | Viewed by 3608

Abstract

IoT devices play a fundamental role in the machine learning (ML) application pipeline, as they collect rich data for model training using sensors. However, this process can be affected by uncontrollable variables that introduce errors into the data, resulting in a higher computational cost to eliminate them. Thus, selecting the most suitable algorithm for this pre-processing step on-device can reduce ML model complexity and unnecessary bandwidth usage for cloud processing. Therefore, this work presents a new sensor taxonomy with which to deploy data pre-processing on an IoT device by using a specific filter for each data type that the system handles. We define statistical and functional performance metrics to perform filter selection. Experimental results show that the Butterworth filter is a suitable solution for invariant sampling rates, while the Savi–Golay and medium filters are appropriate choices for variable sampling rates. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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17 pages, 5627 KiB

Open AccessArticle

Betraying Blockchain: Accountability, Transparency and Document Standards for Non-Fungible Tokens (NFTs)

by Kristin Cornelius

Information 2021, 12(9), 358; https://doi.org/10.3390/info12090358 - 31 Aug 2021

Cited by 33 | Viewed by 16808

Abstract

Transparency and accountability are important aspects to any technological endeavor and are popular topics of research as many everyday items have become ‘smart’ and interact with user data on a regular basis. Recent technologies such as blockchain tout these traits through the design of their infrastructure and their ability as recordkeeping mechanisms. This project analyzes and compares records produced by non-fungible tokens (NFTs), an increasingly popular blockchain application for recording and trading digital assets, and compares them to ‘document standards,’ an interdisciplinary method of contract law, diplomatics, document/interface theory, and evidentiary proof, to see if they live up to the bar that has been set by a body of literature concerned with authentic documents. Through a close reading of the current policies on transparency (i.e., CCPA, GDPR), compliance and recordkeeping (i.e., FCPA, SOX, UETA), and the consideration of blockchain records as user-facing interfaces, this study draws the conclusion that without an effort to design these records with these various concerns in mind and from the perspectives of all three stakeholders (Users, Firms, and Regulators), any transparency will only be illusory and could serve the opposite purpose for bad actors if not resolved. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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56 pages, 12679 KiB

Open AccessEditor’s ChoiceArticle

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

by Nirmalya Thakur and Chia Y. Han

Information 2021, 12(3), 114; https://doi.org/10.3390/info12030114 - 7 Mar 2021

Cited by 49 | Viewed by 5759

Abstract

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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26 pages, 5939 KiB

Open AccessEditor’s ChoiceArticle

An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

by Nirmalya Thakur and Chia Y. Han

Information 2021, 12(2), 81; https://doi.org/10.3390/info12020081 - 14 Feb 2021

Cited by 73 | Viewed by 7912

Abstract

This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human–computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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17 pages, 4785 KiB

Open AccessArticle

A Method of Human Activity Recognition in Transitional Period

by Lei Chen, Shurui Fan, Vikram Kumar and Yating Jia

Information 2020, 11(9), 416; https://doi.org/10.3390/info11090416 - 28 Aug 2020

Cited by 10 | Viewed by 3069

Abstract

Human activity recognition (HAR) has been increasingly used in medical care, behavior analysis, and entertainment industry to improve the experience of users. Most of the existing works use fixed models to identify various activities. However, they do not adapt well to the dynamic nature of human activities. We investigated the activity recognition with postural transition awareness. The inertial sensor data was processed by filters and we used both time domain and frequency domain of the signals to extract the feature set. For the corresponding posture classification, three feature selection algorithms were considered to select 585 features to obtain the optimal feature subset for the posture classification. And We adopted three classifiers (support vector machine, decision tree, and random forest) for comparative analysis. After experiments, the support vector machine gave better classification results than other two methods. By using the support vector machine, we could achieve up to 98% accuracy in the Multi-class classification. Finally, the results were verified by probability estimation. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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Review

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17 pages, 2140 KiB

Open AccessReview

Pervasive Healthcare Internet of Things: A Survey

by Kim Anh Phung, Cemil Kirbas, Leyla Dereci and Tam V. Nguyen

Information 2022, 13(8), 360; https://doi.org/10.3390/info13080360 - 28 Jul 2022

Cited by 7 | Viewed by 4288

Abstract

Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. We classify the vast amount of publications into different categories such as sensors, communication, artificial intelligence, infrastructure, and security. Intensively covering 118 research works, we survey (1) applications, (2) key components, their roles and connections, and (3) the challenges. Our survey also discusses the potential solutions to overcome the challenges in this research field. Full article

(This article belongs to the Special Issue Pervasive Computing in IoT)

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