Internet Of Things Paradigm Research Articles

Deploying an IoT-based remote physics lab platform to enhance experimental physics education in remote regions

Abstract For several years, the educational gap in certain Latin American countries and around the world has become increasingly evident. The limited access to cutting-edge knowledge and technologies to strengthen student training processes has emerged as a significant problem, leading to increased inequality in job opportunities in these regions and progressive setbacks in social and economic development. One way to mitigate this problem has been to provide access to information through telecommunications. However, integration with educational processes has been deficient, especially in the areas of science, technology, engineering, and mathematics. This work proposes the development of a remote physics laboratory platform for experimental validation of models explaining certain physical phenomena. This platform is based on the Internet of Things (IoT) paradigm, allowing students and teachers to connect with these experiences coveringtopics in mechanics, electricity and magnetism, optics, and thermodynamics from remote regions. These laboratories utilize physical setups in educational institutions that have been modified to provide connectivity and remote control capabilities based on IoT. Finally, a framework for implementing new remote laboratories based on the IoT paradigm through an open-source platform is presented. The system has been tested and validated with high school and early university students to verify its usefulness, showing the potential to enhance educational processes by providing laboratory access to students and teachers from primary education to secondary education and university-level professional programs.

The Intersection of the Internet of Things and Smart Cities: A Tertiary Study

Since the transition from an agricultural to an industrial economy, cities have attracted large masses of people in search of their facilities. Cities are symbols of progress and opportunities. However, urbanization also brings with it several problems and challenges. Smart Cities (SC) offer a way to address these challenges by using technology to make cities more efficient, sustainable, and livable. There are numerous technologies that enable the concept of smart cities, including the Internet of Things (IoT). The IoT provides the fundamental sensing infrastructure that allows connecting and virtualizing the physical world, extracting environmental variables that serve as initial inputs for decision-making processes. Such processes are provided by software systems whose construction and execution need to deal with the dynamism, heterogeneity and often serendipitous nature that permeate both the domains of smart cities and IoT. As the integration of IoT and Smart Cities paradigms is still at an early stage, and there are not yet holistic solutions to explore the full potential of such a synergy, we carried out a literature review on the topic. In particular, the objective of this article is to describe the results of a structured literature review to identify general concepts about quality attributes, applications, technologies, and challenges of IoT solutions applied to the SC domain. Our main goal is to assist in understanding the basic concepts of the research area through the search for secondary studies. This review is a tertiary study covering 17 reviews and aims to promote a high-level discussion on the identified characteristics and provide an overview of the area to promote a better perception of current development needs and opportunities.

A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems

Electric vehicles (EVs) are becoming of significant interest owing to their environmental benefits; however, energy efficiency concerns remain unsolved and require more investigation. A major issue is a lack of EV charging infrastructure, which can lead to operational difficulties. Effective infrastructure development, including well-placed charging stations (CS), is critical to enhancing connectivity. To overcome this, consumers want real-time data on charging station availability, neighboring station locations, and access times. This work leverages the Distance Vector Multicast Routing Protocol (DVMRP) to enhance the information collection process for charging stations through the Internet of Things (IoT). The evolving IoT paradigm enables the use of sensors and data transfer to give real-time information. Strategic sensor placement helps forecast server access to neighboring stations, optimize vehicle scheduling, and estimate wait times. A recommender system is designed to identify stations with more rapidly charging rates, along with uniform pricing. In addition, the routing protocol has a privacy protection strategy to prevent unauthorized access and safeguard EV data during exchanges between charging stations and user locations. The system is simulated with MATLAB 2020a, and the data are controlled and secured in the cloud. The predicted algorithm’s performance is evaluated using several kinds of standards, including power costs, vehicle counts, charging costs, energy consumption, and optimization values.

A comprehensive survey on an IoT-based smart public street lighting system application for smart cities

The swift advancement and updating of urban lighting systems, along with the incorporation of smart and Internet of Things (IoT) infrastructure, have opened up numerous opportunities for technological progress across various facets of life. This paper offers a comprehensive overview on the development of smart public street lighting infrastructure tailored for IoT applications in smart cities. Initially, the focus lies on transitioning from conventional lighting to Light-Emitting Diodes (LEDs) technology in street lighting. Complementing this transition, the incorporation of the wireless networked sensors and controllers ensures dynamic brightness control in operational zones, envisioning substantial energy savings. Furthermore, the notion characterizing smart cities denotes incorporating modern digital infrastructures to develop innovative functionalities and connect various application, following the IoT paradigm. The key findings from the proposed study have enhanced knowledge regarding smart public street lighting application. This system integrates smart poles equipped with LEDs lamps technology, smart sensors, communication network and monitoring unit, leveraging current technological advancements in IoT applications. The implementation of IoT-based smart public street lighting systems presents several challenges, including integrating diverse sensors and actuators ensuring robust device communication, secure data management, and effective system scaling and maintenance. Despite these challenges, this system significantly advances smart city infrastructure by enhancing energy efficiency, safety, and sustainability. However, addressing their high initial costs, data privacy and security concerns, and ongoing maintenance are crucial in future studies to realize their full potential in smart cities.

Revolutionizing cultural heritage preservation: an innovative IoT-based framework for protecting historical buildings

Italy offers a cultural heritage of considerable value that needs to be protected. Indeed, natural deterioration linked to the passage of time affects ancient artifacts and buildings. Sometimes, the deterioration compromises the functionality of cultural assets, pushing them toward decay. In this scenario, effective intervention seems impossible on the various critical points because of the wide variability of factors involved and the wide range of possible treatments. However, the spread of low-cost technologies has led to the possibility of having different devices and sensors able to communicate and interact with each other and humans: the Internet of Things (IoT). In this scenario, the IoT paradigm makes it possible to map reality by defining a coherent virtual representation (Digital Twin), which could help preserve Cultural Heritage. This work introduces an IoT-based system combining monitoring, predictive maintenance, and decision-making regarding the implementable interventions for protecting cultural heritage buildings. For this purpose, deep and machine learning techniques allow for the detection and classification of damages on specific materials. The experimental phase consists of two phases: the first aims to evaluate the accuracy of the proposed architecture, and the second exploits a prototype capable of interacting with expert users. The results of the experimental campaign are promising.

Internet of things (IoT)-based structural health monitoring of laboratory-scale civil engineering structures

Rapid advances in the Internet of Things (IoT) domain have made it a crucial technology for the real-time structural health monitoring (SHM) of civil engineering infrastructures. The availability of quick and accurate vibration data is essential for SHM, and such data can be obtained through IoT devices mounted on the structures. This study proposes a real-time damage prediction and localization approach using a low-cost "do-it-yourself" wireless sensor node with IoT capabilities for SHM. The proposed sensor node comprised a microcontroller (NODE MCU ESP8266) and a 6-axis accelerometer (MPU6050). The IoT devices track the real-time frequency of the laboratory-scale structure indirectly via measurement of acceleration-time history, and their results are compared with conventional industry-standard accelerometers. Promising results, with a <6% average difference from the conventional accelerometer (difference ranging from 1.3 to 14.3%), provided an innovative SHM for vibration-based real-time SHM using the IoT paradigm. The performance of the proposed methodology was validated numerically and experimentally on two laboratory-scale structures, and the potential of IoT technology for enhancing the efficiency of SHM was demonstrated. The proposed method thus can enable the early detection of damages in infrastructures such as buildings and bridges and thus can reduce the likelihood of accidents via continuous SHM.

Generic IoT for Smart Buildings and Field-Level Automation—Challenges, Threats, Approaches, and Solutions

Smart home and building systems are popular solutions that support maintaining comfort and safety and improve energy efficiency in buildings. However, dynamically developing distributed network technologies, in particular the Internet of Things (IoT), are increasingly entering the above-mentioned application areas of building automation, offering new functional possibilities. The result of these processes is the emergence of many different solutions that combine field-level and information and communications technology (ICT) networks in various configurations and architectures. New paradigms are also emerging, such as edge and fog computing, providing support for local monitoring and control networks in the implementation of advanced functions and algorithms, including machine learning and artificial intelligence mechanisms. This paper collects state-of-the-art information in these areas, providing a systematic review of the literature and case studies with an analysis of selected development trends. The author systematized this information in the context of the potential development of building automation systems. Based on the conclusions of this analysis and discussion, a framework for the development of the Generic IoT paradigm in smart home and building applications has been proposed, along with a strengths, weaknesses, opportunities, and threats (SWOT) analysis of its usability. Future works are proposed as well.

Exploring Authentication Paradigms in the Internet of Things: A Comprehensive Scoping Review

In the rapidly expanding domain of the Internet of Things (IoT), ensuring the implementation of robust security measures such as authentication has become paramount to safeguarding sensitive data and maintaining the integrity of connected devices. Symmetry in the IoT commonly denotes the uniformity or equilibrium in data distribution and processing across devices or nodes in a network. Leveraging symmetric patterns can enhance the robustness and scalability of IoT authentication. This scoping review aims to provide a comprehensive overview of recent developments in authentication techniques within the IoT paradigm. It subsequently presents recent research on various IoT authentication schemes, organized around several key research questions. The objective is to decipher the intricacies associated with authentication in the IoT by employing a multi-criteria classification approach. This involves a comprehensive analysis of existing authentication protocols, delineating their respective advantages and disadvantages, and gaining insights into the associated security concerns. The research questions highlighted in the review aim to probe the present scenario of authentication systems utilized in IoT, with a focus on identifying trends and discerning shifts. This review synthesizes insights from scholarly articles to provide a roadmap for future research in IoT authentication. It functions as a valuable resource for establishing theoretical foundations and provides practical implications applicable to practitioners, policymakers, and researchers alike. By elucidating the intricacies of IoT authentication, this review cultivates a profound understanding of the transformative potential and the multifaceted challenges. It establishes the foundation for resilient security measures essential for the sustainable growth of the Internet of Things.

Blockchain for Cybersecurity in Edge Networks

The Blockchain is one of the most promising and artistic cybersecurity solutions. It has been practiced in a variety of reinforcements, including healthcare, transportation, and Internet of Things (IoT) applications. However, Blockchain has a colossal scalability challenge, limiting its ability to control services with high transaction volumes. Edge computing, on the other hand, was designed to allow cloud services and resources to be deployed at the network's edge, although it now faces issues in terms of decentralized security and management. The unification of edge computing and Blockchain within one solution jar provides a vast scale of storage systems, database servers, and authenticity computation towards the end in a safe fashion. Despite the potential of interconnected edge computing and Blockchain environments, difficulties such as scalability, resource management, function integration, self-organization, and new security concerns must be addressed before broad adoption. This article provides an overview of the secure IoT framework, paradigms, enablers, and security problems of combining Blockchain and intelligent edge computing. Finally, broader viewpoints for future research directions are investigated.

An IoT Assimilated Distributed Control Method for Green Electrical Transmission Grids

Green electrical grids utilize renewable energy to ensure sustainable transmission from natural resources. Internet of Things (IoT) like pervasive platforms is integrated with the grids for improving the automation in such power grids. This article considers the IoT control over the green grids for uninterrupted power transmission. The proposed method named Assimilated Distributed Control (ADC) balances the generated and distribution of electrical power based on demand. The IoT paradigm monitors the rising demand for recommending multi-renewable power source assimilation for meeting the distribution demands. In this process, linear decision-making for distribution management and assimilation is performed. The decision-making process relies on power generation and distribution ratio from low to peak demand intervals. Therefore, the number of resource assimilations relies on the distributed control for handling peak demands. The proposed method is analyzed using distribution ratio, peak demand, and recommendation assimilation.

Survey on the Internet of Things Definitions and Applications

The Internet of Things (IoT) refers to devices equipped with sensors, computing power, software, and other technologies that communicate and share data with other devices and systems via the Internet. The Internet of Things seamlessly connects people and things to form a symbiotic relationship. In the widespread presence of the Internet of Things, services are offered as a product. Developments in the Internet and things have added value to the Internet of Things and caused the evolution we see today. Therefore, any significant advancement in the Internet of Things must inevitably come from collaborative efforts across various disciplines, including the social sciences, informatics, electronics, and telecommunications. This evolution has necessitated periodic revisions to the definition of IoT. Our study is intended for people who wish to get involved in this complicated field and help it grow in such a challenging setting. The enabling technologies are reviewed and several perspectives on this IoT paradigm are provided.

Survey on the Internet of Things Definitions and Applications

The Internet of Things (IoT) refers to devices equipped with sensors, computing power, software, and other technologies that communicate and share data with other devices and systems via the Internet. The Internet of Things seamlessly connects people and things to form a symbiotic relationship. In the widespread presence of the Internet of Things, services are offered as a product. Developments in the Internet and things have added value to the Internet of Things and caused the evolution we see today. Therefore, any significant advancement in the Internet of Things must inevitably come from collaborative efforts across various disciplines, including the social sciences, informatics, electronics, and telecommunications. This evolution has necessitated periodic revisions to the definition of IoT. Our study is intended for people who wish to get involved in this complicated field and help it grow in such a challenging setting. The enabling technologies are reviewed and several perspectives on this IoT paradigm are provided.

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

BENIGREEN: Blockchain-Based Energy-Efficient Privacy-Preserving Scheme for Green IoT

The energy conservative extension of Internet-of-Thing (IoT), green IoT, is a revolutionary approach in connecting people, processes, and things in an energy-efficient way. The existing research works in the domain of green IoTs forbid the use of decentralized management (e.g., blockchain) of data due to its intrinsic disadvantages of block mining, transaction incentives, and less throughput. However, the advantages of blockchains urge the development of new decentralized strategies utilizing the architecture of green IoTs. The infancy stage of the conjunction between green IoTs and blockchains, the need of decentralization in energy management in green IoTs motivate us for the present research. In this paper, we address the problems of decentralization, energy-conservation, and privacy simultaneously. We introduce the first blockchain-based privacy-preserving framework for green IoTs. We name this framework as "Blockchain-based ENergy-efficient and prIvacy-preserving data management scheme for GREEN-iot (BENIGREEN)" for smart cities. BENIGREEN uses weight metrics for energy-efficient cluster heads selection. The use of weight metrics is a novel contribution in the field of green IoTs. Further, we integrate a decentralized blockchain framework with an authentication scheme for secure transmission among Base Station (BS) and sensor nodes by employing registration, certification, and revocation phases. Consequently, BS allocates the collected information from cluster heads to decentralized blockchain and cloud storage. The BS eliminates all malicious nodes from the network by employing a certificate revocation process. We execute thorough experiments in terms of the operation time, throughput, average energy consumption, and computational latency. The comparative analysis with the state-of-the-art schemes show that BENIGREEN is efficient for IoT paradigm.

Challenges and Opportunities in the Internet of Intelligence of Things in Higher Education—Towards Bridging Theory and Practice

The application of the Internet of Things is increasing in momentum as advances in artificial intelligence exponentially increase its integration. This has caused continuous shifts in the Internet of Things paradigm with increasing levels of complexity. Consequently, researchers, practitioners, and governments continue facing evolving challenges, making it more difficult to adapt. This is especially true in the education sector, which is the focus of this article. The overall purpose of this study is to explore the application of IoT and artificial intelligence in education and, more specifically, learning. Our methodology follows four research questions. We first report the results of a systematic literature review on the Internet of Intelligence of Things (IoIT) in education. Secondly, we develop a corresponding conceptual model, followed thirdly by an exploratory pilot survey conducted on a group of educators from around the world to get insights on their knowledge and use of the Internet of Things in their classroom, thereby providing a better understanding of issues, such as knowledge, use, and their readiness to integrate IoIT. We finally present the application of the IoITE conceptual model in teaching and learning through four use cases. Our review of publications shows that research in the IoITE is scarce. This is even more so if we consider its application to learning. Analysis of the survey results finds that educators, in general, are lacking in their readiness to innovate with the Internet of Things in learning. Use cases highlight IoITE possibilities and its potential to explore and exploit. Challenges are identified and discussed.

PLS-IoT Enhancement Against Eavesdropping via Spatially Distributed Constellation Obfuscation

Wireless communication has become a ubiquitous facet of modern-day life. With the advent of Internet of Things (IoT) and massive machine-type connectivity, the number of devices is expected to increase even more. However, increased dependence on wireless connectivity is marred with risks related to user privacy and data confidentiality, especially for low-power IoT devices. Therefore, it is imperative to provide an extremely reliable secure algorithm with minimal complexity and computational expense in IoT paradigm. With that in mind, this letter presents asymmetric multi-level physical layer security (PLS) scheme, in which each transmitted symbol undergoes two types of distortion: channel-based phase distortion and multi-reception amplitude randomization. The proposed scheme offers a substantial security advantage for legitimate links, while also streamlining receiver design. Numerical results indicate that the scheme effectively confounds eavesdroppers without compromising the bit error rate of the intended receiver. Additionally, the proposed scheme provides another level of security by concealing the modulation information, requiring the receiver to detect the modulation scheme before detecting transmitted symbols.

Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one’s knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel.

An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence

The Internet of Things (IoT) is a network of smart gadgets that are connected through the Internet, including computers, cameras, smart sensors, and mobile phones. Recent developments in the industrial IoT (IIoT) have enabled a wide range of applications, from small businesses to smart cities, which have become indispensable to many facets of human existence. In a system with a few devices, the short lifespan of conventional batteries, which raises maintenance costs, necessitates more replacements and has a negative environmental impact, does not present a problem. However, in networks with millions or even billions of devices, it poses a serious problem. The rapid expansion of the IoT paradigm is threatened by these battery restrictions, thus academics and businesses are now interested in prolonging the lifespan of IoT devices while retaining optimal performance. Resource management is an important aspect of IIoT because it's scarce and limited. Therefore, this paper proposed an efficient algorithm based on federated learning. Firstly, the optimization problem is decomposed into various sub-problems. Then, the particle swarm optimization algorithm is deployed to solve the energy budget. Finally, a communication resource is optimized by an iterative matching algorithm. Simulation results show that the proposed algorithm has better performance as compared with existing algorithms.

In Situ IoT Development and Application for Continuous Water Monitoring in a Lentic Ecosystem in South Brazil

The monitoring of water resources through conventional methods, related to a manual process when performing the sample collection, followed by laboratory analysis, presents some difficulties concerning the logistics of the process, such as access to the interior of a lake, in addition to often being based on a small number of samples. The concept of the internet of things (IoT) is used here to collect data through five parametric probes contained in the floating station located inside a lake and inform them in real time continuously. The main objective of this research is to demonstrate the applicability of the IoT concept in the continuous monitoring of water in a lentic environment. Therefore, it is necessary to develop a tool for this. Upon reaching this objective, the advantages observed in this research confirmed that the IoT paradigm is an essential resource, justifying a natural tendency to establish itself when there is a need to collect data efficiently and continuously. Furthermore, the experimental result proves the IoT concept’s efficiency, agility, and reliability to environmental issues, especially regarding the most significant natural and indispensable resource for the planet, water.

IOT-Based Generic Health Monitoring with Cardiac Classification Using Edge Computing

Background: The current environment of modern computation can offer a smart healthcare monitoring for the early prediction of disease detection. For the domain of healthcare services, the Internet of Things (IoT) has a vital role, and also aids in the enhancement of the data’s processing as well as predictions. The transfer of data or reports from one location to another will consume a lot of energy as well as time, and also does result in issues of high energy as well as latency. With edge computing, the disadvantages can be easily resolved. Objectives: This work presents a Convolutional Neural Network (CNN)-based model of prediction which employs edge computing as well as IoT paradigms. The term edge computing will refer to a distributed environment framework that facilitates swift resource accessibility and response times by means of the local edge servers for processing at the end of the IoT devices. With this model, there can be an analysis of the health data which has been gathered by the IoT devices. Additionally, the edge devices will employ the edge servers for offering the patients as well as the doctors health-prediction reports in a timely manner. Methods: This work has proposals of an optimized CNN with Tabu Search (TS), Artificial Bee Colony (ABC) as well as the hybrid TS-ABC algorithms. Results: Analysis of these proposed algorithms is done with the parameters of performance such as the rate of error and the accuracy. Also, these algorithms’ simulated outcomes have been able to demonstrate their superior performance in comparison to the other technologically advanced approaches.