Trends In Internet Of Things Research Articles

Enhancing Agricultural Productivity through Smart Farming with IoT

Technology has greatly advanced the agriculture industry, making it more data- driven and intelligent. The Internet of Things has grown at an exponential rate, causing significant changes in many industries, radical change creates opportunities as well as challenges because it destroys current farming methods. The Internet of Things (IoT) and wireless sensors' possible uses in agriculture are covered in this article. It also discusses some of the challenges that could occur from integrating this technology with traditional farming methods. The IoT tools and techniques used in agricultural applications are explained in detail. Based on this article, we can determine current IoT trends in agriculture.

Big data analytics and internet of things for personalised healthcare: opportunities and challenges

<p>With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future.</p>

Machine Learning and IoT Trends for Intelligent Prediction of Aircraft Wing Anti-Icing System Temperature

Airplane manufacturers are frequently faced with formidable challenges to improving both aircraft performance and customer safety. Ice accumulation on the wings of aircraft is one of the challenges, which could result in major accidents and a reduction in aerodynamic performance. Anti-icing systems, which use the hot bleed airflow from the engine compressor, are considered one of the most significant solutions utilized in aircraft applications to prevent ice accumulation. In the current study, a novel approach based on machine learning (ML) and the Internet of Things (IoT) is proposed to predict the thermal performance characteristics of a partial span wing anti-icing system constructed using the NACA 23014 airfoil section. To verify the proposed strategy, the obtained results are compared with those obtained using computational ANSYS 2019 software. An artificial neural network (ANN) is used to build a forecasting model of wing temperature based on experimental data and computational fluid dynamics (CFD) data. In addition, the ThingSpeak platform is applied in this article to realize the concept of the IoT, collect the measured data, and publish the data in a private channel. Different performance metrics, namely, mean square error (MSE), maximum relative error (MAE), and absolute variance (R2), are used to evaluate the prediction model. Based on the performance indices, the results prove the efficiency of the proposed approach based on ANN and the IoT in designing a forecasting model to predict the wing temperature compared to the numerical CFD method, which consumes a lot of time and requires high-speed simulation devices. Therefore, it is suggested that the ANN-IoT approach be applied in aviation.

ANALYSIS OF GLOBAL RESEARCH TRENDS IN THE INTERNET OF THINGS (IoT) AREA WITH NETWORK MAP TECHNIQUE

The Internet of Things (IoT) has emerged as a transformative technology paradigm that connects billions of devices, enabling seamless communication and data exchange. The study used the Web of Science Core Collection database to uncover global research trends in the IoT. The data in the study were visualized using the VOSviewer software, and bibliometric analysis was performed using the network map technique. The study aims to examine global research trends in IoT, examine IoT-related studies in social sciences, examine the status of IoT studies, especially in management and public administration, and guide researchers who aim to work on this subject. In the study, 87,397 studies on IoT between 2002 and 2022 were examined, and 1162 studies were included by limiting the studies to management and public administration. Studies were categorized and analyzed concerning fields of study, departments, years of publication, keywords, authors, and the most cited authors.

A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles

The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. We can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to a massive quantity of information being transferred from the devices connected in IoV. One such overhead is the network connection between the units of an IoV. To make an efficient ITS using IoV, optimization of network connectivity is required. A survey on network optimization in IoT and IoV is presented in this study. It also highlights the backdrop of IoT and IoV. This includes the applications, such as ITS with comparison to different advancements, optimization of the network, IoT discussions, along with categorization of algorithms. Some of the simulation tools are also explained which will help the research community to use those tools for pursuing research in IoV.

A Smart Design Based on User Experience for Lighting System in Smart Home

Smart home and the Internet of Things (IoT) have become integral parts of our daily lives. This paper provides an overview of the concept, development history, application scenarios, and future trends of smart home and IoT. The paper introduces the fundamental concepts of smart home and IoT, including the use of sensors, communication technology, and cloud computing. It presents the design of a highly automated smart home lighting system that incorporates traditional smart home technology and IoT technology, allowing the system to use big data to determine when to turn on the lights. The proposed method is tested through a mathematical model and theoretical simulations, followed by an actual simulation using a 51 microcomputer. The paper discusses potential future developments in the field of smart home and IoT, such as the integration of artificial intelligence and machine learning.

Analysis on Intrusion Detection Systems to Secure IoT Networks

The amalgamation of physical objects and the internet constitutes the Internet of things(IoT). In the subsequent times, more such physical objects will be connected to the internet and this will lead to the escalation in the attacks carried out on IoT devices and network. This is mainly due to the less memory and lack in built-in security controls. This has made IoT Security, the most researched topic in the past decade. Thus to protect IoT nodes and networks, a security mechanism called Intrusion Detection System(IDS) is brought in use.The advent of Internet of things has played a crucial role in making the lives of the people more easier and comfortable. It has improved the lives of the people in terms of several aspects like comfort and efficiency. This has overall consequently lead to what is called smart environments. When the case of real world smart environment is discussed, both security and privacy comes into play in being the key issues to be considered. But the need demands for the Intrusion Detection Systems (IDSs) to be designed for the IoT networks or environments to mitigate attack related issues. These are the attacks that exploits the vulnerabilities present in the network. The work here demonstrates the need of the fact that despite having previous technologies, we still lack a secure system that could safeguard IoT networks. In this paper, a detailed analysis is provided on IoT, security attacks in IoT, IDS, types of IDS, Machine learning and Deep learning techniques and algorithms used with IDS in IoT and current trends and research on use of IDS in securing Internet of Things is discussed. Lastly future research based on the current analysis is also examined. Keywords: IoT, IDS, Network, Security, Machine Learning, Deep Learning

The State of IoT for Agribusiness in Brazil

The Internet of Things (IoT) plays a significant role in advancing Agro 4.0 in Brazil. This article explores current IoT trends, benefits, and challenges of the IoT in the agriculture industry.

A Step toward Next-Generation Advancements in the Internet of Things Technologies.

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.

Improving Source location privacy in social Internet of Things using a hybrid phantom routing technique

The amalgamation of Smart IoT and Machine learning is an emerging research area. In this context, a new trend in IoT called Social IoT has been considered for this study. The Social IoT has benefits of connectivity exhibited within the network of connected objects through the Internet of Things (IoT). It covers the entire world and provides innovative services to improve life standards, establishes novel businesses, and makes buildings and cities. Certain smart things allow the collection of ubiquitous data or traffic, which pose a threat to source location privacy. Therefore, it limits the source of the Internet of Things vision if implemented wrongly. These threats come along with some challenges, adversary profiles, and the location privacy of personal data. When they are used to monitor important assets, the attacker can easily hunt the location of these assets. However, the source location constitutes a way to prevent the adversary from finding the location of the source. This research has used a hybrid phantom method by combining the phantom node and multi-path route that improves privacy and reduces the consumption of energy. The Analytic Hierarchal Process (AHP) is used for phantom node selection, based on parameters such as energy, distance, heterogeneity, and neighbor list. The result shows the average consistency value of the parameters is 4.2 and the consistency index value is 0.066. The overall priority of the alternative node is 2.089 as compared to other nodes. The sum of the vector weight value is obtained as 4.845. The total average energy consumption is 1.211 J and the average safety period capture ratio is 59.41%. The proposed techniques overwhelmed the deficiencies in existing techniques, reduces energy consumption improves the safety period and increases the network lifetime.

Survey of Secure Communications of Internet of Things with Artificial Intelligence

The Internet of Things(IoT) with artificial intelligence aims to build a network that enables smart devices to communicate autonomously and efficiently. It is one of the fastest-growing fields in the information era. It encompasses billions of smart devices worldwide. With the explosive growth of the number of Internet of Things access devices, the types of access network devices are also complex and diverse, especially the heterogeneity of the network is becoming more and more prominent. Faced with such a huge and complex network architecture and communication environment, the existing network security architecture is facing huge challenges. The paper first analyzes the existing three-tier architecture of the IoT and possible security issues, including DDoS Attack, Replay Attack, SQL Injection, Man-in-The-Middle Attacks, and illegal Access. Internet security solutions, including Security Framework, Security Routing Protocol, Authentication Protocol, Data Encryption Transfer & Storage, Key Exchange Protocol and Intrusion Detection, and then explore the possible applications of quantum computing and quantum communication in IoT security, including Quantum Security Hardware Used in IoT, Quantum Key Distribution (QKD) in IoT, Intrusion Detection During QKD with Deep Learning (DL) and Data Encryption Protocol in IoT Based On Quantum Walks (QW), and finally discussed the opportunities and trends of existing security solutions for the IoT Challenges, these challenges and trends can be used as potential research directions for future IoT security.

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable.

A Review of Using IoT for Energy Efficient Buildings and Cities: A Built Environment Perspective

Applications of the Internet of Things (IoT) are rapidly utilized in smart buildings and smart cities to reduce energy consumption. This advancement has caused a knowledge gap in applying IoT effectively by experts in the built environment to achieve energy efficiency. The study aims to provide an extensive review of IoT applications for energy savings in buildings and cities. This study contributes to the field of IoT by guiding and supporting built environment experts to utilize IoT technologies. This paper performed a thorough study using a systematic review that covered an overview of IoT concepts, models, applications, trends and challenges that can be encountered in the built environment. The findings indicated limitations in developing IoT strategies in buildings and cities by professionals in this field due to insufficient comprehension of technologies and their applied methods. Additionally, the study found an indefinite implementation and constraints on using IoT when integrated into the built environment. Finally, the study provides critical arguments and the next steps to effectively utilize IoT in terms of energy efficiency.

A contextual framework development toward triboelectric nanogenerator commercialization

The world is experiencing the third wave of electronic commerce, in which battery-free electronics dominate the market share. Additionally, the wide distribution of the sensors and high maintenance costs make batteries an insufficient solution, especially for remote or inaccessible areas. Powering the internet of things (IoT) would be impossible without making the sensors self-powered by harvesting energy from the working environment to ensure long-term operation. Besides, self-powering is a significant advancement for wireless sensing and the emerging IoT trend, where nanogenerators emerge as a compelling platform technology. However, nanogenerator-enabled self-powered systems still introduce a slew of new challenges, including the grand challenge of relatively low power generation in most energy-harvesting modalities. Therefore, it is crucial to alleviate various technical, operational, and social barriers of nanogenerator-based devices and present their potential mitigation solutions. Here, ten significant barriers are scrutinized in terms of the market adoption of triboelectric nanogenerator (TENG)-based technologies, and interrelationships among them are examined using a total interpretive structural modeling (TISM) approach and cross-impact matrix multiplication applied to classification (MICMAC) analysis. The findings of this study can help key players in this field understand the most significant impediments to TENG market adoption, which can revolutionize wireless and distributed electronics without batteries.

A systematic review of meta-heuristic algorithms in IoT based application

Internet-of-Things (IoT) has gained quick popularity with the evolution of technologies such as big data analytics, block-chain, artificial intelligence, machine learning, and deep learning. IoT based systems provides smart and automatic framework for the efficient decision making and automation of various task to make human life easy. Meta-heuristic algorithms are self-organized and decentralized algorithms used for solving complex problems using team intelligence. Recently, meta-heuristic algorithms has been widely used for solving a number of IoT based challenges. This paper presents a systematic review of meta-heuristic algorithms used for unfolding the IoT based applications. The broad classification of existing meta-heuristic based algorithms has been documented. Further, the prominent applications of IoT based system using the meta-heuristic algorithms are presented. Moreover, the current research questions are included to illustrate the new opportunities for the researchers. Finally, the current trends in IoT and possible future directions are documented. This paper will provide new directions to the researchers working in the field of meta-heuristic algorithms and IoT based system.

Internet of things and consumer engagement on retail: state-of-the-art and future directions

PurposeThe growing complexity of consumer engagement (CE) due to the impact of Internet of things (IoT) has been attracting significant attention from both academics and industry practitioners especially in recent times. Hence, understanding this phenomenon remains very crucial to the body of knowledge. This study conducted a systematic review on IoT and CE with the aim of proposing future research opportunities using the TCCM model.Design/methodology/approachExtant literature studies were systematically examined by sourcing high ranking ABS journals from EBSCO, ScienceDirect and Emerald. A total of 58 articles were included in the final analysis of this research.FindingsThe analysis established the need to conduct more research on CE due to the impact of new technological implementation in retail. The results further suggest the need for extensive research across African countries and emerging markets to enable broader empirical generalizations of research outcomes. Using the TCCM framework, the authors indicated directions for future empirical research.Originality/valueThis study exposes the current trends in CE and IoT. The results and analysis are both compelling and verifiable, hence, establishing a firm base of reference for future research in related fields.

Special issue editorial on emerging trends in internet of things for e‐health and medical supply chain systems

Special issue editorial on emerging trends in internet of things for e‐health and medical supply chain systems

Future Industry Internet of Things with Zero-trust Security

AbstractThe emerging techniques, such as the fifth-generation communications (5G), Internet of Things (IoT), blockchain, artificial intelligence, etc., are operating in unison will drive the transformation of global business forward. The 5G technology is expected to unleash a massive IoT ecosystems by providing massive connectivity for huge number of IoT devices with faster data rate, ultra-low latency, and low-cost access. The 5G networks will be designed to bring the level of performance needed for massive IoT and will enable a perceived fully ubiquitous connected world. Meanwhile, the blockchain being promoted as the fundamental for new business model in Future IoT (FIoT). This paper attempts to provide a set of new directions and ideas for research in 5G/6G enabled IoT and new technique trends in IoT. The current IoT are facing a number of challenges, such as massive IoT devices access, network performances, security, standardization, and critical applications. This paper investigates new technologies, such as 5G, zero-trust, and blockchain will catalyse the innovation in IoT. Specifically, a zero-trust security architecture for FIoT is proposed and a blockchain-based device authentication in IoT environment (BasIoT) is proposed that can provide massive secure device access in FIoT.

WoTemu: An emulation framework for edge computing architectures based on the Web of Things

The edge computing model is an approach to Internet of Things (IoT) architectures based on the redistribution of services and infrastructure from centralized clouds to locations closer to IoT devices. The Web of Things (WoT) is another important IoT trend, currently led by the W3C, which aims at solving the IoT interoperability problem by adopting proven technologies and patterns from the Web. The design and validation of IoT deployments based on these paradigms is a complex task that involves multiple services, heterogeneous hardware and diverse communication technologies. Testing such projects in real world conditions usually requires a significant investment of resources. There are simulation tools that can assist in this process with much lower barriers of entry, however, they involve the designer making modelling assumptions that are not always representative of the real systems. This work presents an emulation tool for IoT projects based on the edge computing model that is able to seamlessly scale horizontally by leveraging container orchestration (Docker swarm mode). Furthermore, the W3C WoT model is included as a first-class citizen, enabling the designer to model all actors in the system as Things. The tool can run the real production code with minimal modifications and provides meaningful insights into the behaviour of the proposed architecture. This knowledge serves to rapidly iterate the optimization process, simplifying design issues and the detection of bottlenecks before committing to a real deployment in the field. A real-world scenario is also emulated in order to demonstrate its capabilities and validate its contribution.

Recent Trends in IoT : A review

The first wave of the internet revolution came in with the growth of personal computers and in the second wave the internet came right onto our palms that is with the advent of mobile devices and here we are, living the third wave where all electronics devices are almost already connected to internet or will be in the near future. We are quickly going into another universe of computing and network. Machines with sensors and sensors catching information sending this data to distributed storage for additional and future use and this cycle in the area of science and innovation many are calling as "The Internet of Things". Machine to machine, machine to system, machine to condition, the Internet of everything, the web of clever things, sharp structure — call it as the need might arise, but it's going on, and its future open doors is enormous and huge. In this paper, we seek to highlight the concept of Internet of Things (IoT) in general, as well as reviewing the main challenges of the IoT environment by focusing on the recent research directions in this topic. Recently, IoT has emerged as a new technology that is used to express a modern wireless telecommunication network, and it can be defined as an intelligent and interoperability node interconnected in a dynamic global infrastructure network, also it seeks to implement the connectivity concept of anything from anywhere at any time. They range widely in use, size, energy capacity, and computation power. However, the integration of these smart things into the standard Internet introduces several security challenges because the majority of Internet technologies and communication protocols were not designed to support IoT.