IoT Research Research Articles

A Bibliometric Review of Intrusion Detection Research in IoT: Evolution, Collaboration, and Emerging Trends

A Bibliometric Review of Intrusion Detection Research in IoT: Evolution, Collaboration, and Emerging Trends

Research Trends in the Use of the Internet of Things in Sustainability Practices: A Systematic Review

This article discusses the role of technological advancements, particularly in the IoT domain, in promoting economic, productive, and social development in the context of environmental sustainability. The research focuses on identifying specific trends in the application of these systems for sustainable practices through a bibliometric analysis using PRISMA. The text presents an evaluation of global scientific productivity, highlighting the significant contributions of countries such as China and the United States. It also emphasizes India’s prominent role in the efficiency of the agri-food supply chain. The study further examines thematic evolution, keyword networks, and their co-occurrence, as well as the relationship between validity and frequency. The article proposes a research agenda that concludes the exponential growth of IoT research in sustainable cities since 2016. The research agenda focuses on energy efficiency, cloud computing, and big data. Additionally, the article identifies smart cities and sensor usage, along with the creation of new decentralized networks, as priority research areas that will remain relevant in the future. The article emphasizes the importance of security and privacy in these areas.

Optimizing decision-making in IoT through ensemble voting and dynamic group formation with DBSCAN

In the era of widespread connectivity, leveraging artificial intelligence models and analyzing the vast datasets generated by smart devices are central points in IoT research. While existing studies mainly focus on improving the decision-making prowess of central systems, the potential for local optimization remains largely unexplored. This paper presents an Ensemble Voting Scheme with Multilayer Dynamic Groups (EVMDS), which assigns decision weights to IoT devices based on their attribute data. By employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, dynamic clusters among IoT devices can be identified, the application of ensemble voting rules at each stage of group formation, enabling layered computations to ease backend burden and achieve hierarchical decision-making capability, facilitating regional-level decision-making that strikes a balance between local and global optimization. Through simulated decision-making scenarios in a small-scale IoT environment, our experiments demonstrate the superior accuracy and reliability of the proposed approach compared to existing models.

The Past and Future Trends in IoT Research

The Past and Future Trends in IoT Research

Protecting the Internet of Things (IOT) with Machine Learning and Deep Learning Techniques

Abstract- Deep learning (DL) and Machine learning (ML) as an IoT paradigm have improved problem-solving, and as a result, their application has expanded to many different fields. This has led to the idea that there are two powerful ways to use data—deep learning (DL) and machine learning (ML)—to solve specific problems. Thus, this article's objective is to provide a thorough analysis of "Scanning Machines and Deep Learning Techniques for Internet of Things (IOT) Security and Privacy," which addresses the current state of IoT research as well as its joint endeavor with DL. This technique stops the adversary from discovering the training data for the target model by utilizing differential privacy. The research's authors concluded that machine learning and deep learning algorithms were developed relatively recently and were never intended for use in cryptography applications. However, researchers with the necessary skills can use deep learning and machine learning to develop cryptography. Index Terms- Internet of Things, Deep Learning, Machine Learning, Security

An enhanced distributed framework for real-time performance testing of large scale IoT dataset using big data analytic tools

<p>The demand for analyzing enormous IoT datasets is rising in parallel with the popularity of the IoT. There are considerable obstacles to effective processing and analysis due to the amount, velocity, and variety of IoT data. In this research, we present a distributed system that makes use of big data analytic tools like Apache Hive, Spark, and Hadoop to efficiently test the performance of massive IoT datasets. The framework addresses the lack of a comprehensive solution by providing a scalable and fault-tolerant architecture. We discuss the motivation behind real-time performance testing in the context of big data analytics for IoT datasets and highlight the need for a distributed framework. A literature review is conducted to explore existing performance testing frameworks, big data analytic tools, and approaches for performance testing big data analytics. The proposed framework’s key components, including dataset generation, test scenario specification, cluster configuration, performance metrics collection, analysis and visualization modules, and implementation details, including tool choices, are discussed. An experimental evaluation is conducted to validate the framework’s performance, and it is suggested to incorporate blockchain technology. Overall, the proposed framework offers a comprehensive solution for real-time performance testing of large-scale IoT datasets, providing organizations and researchers with a valuable tool to ensure efficient and reliable IoT data processing and analysis.</p>

Internet of Things Performance Evaluation from Three Decades in Education

Objective: This research tends to find the complete opportunity, novelties, and state-of-the-art for further researchers to make an easier way to conduct research in IoT, especially in educational fields worldwide.
 
 Method: This research is bibliometric and employs qualitative analysis. Based on the review and analysis of the IoT in the educational field, it is concluded that in the range year of three-decade IoT publications.
 
 Results: The exciting results are that the publication trends tend to increase yearly. Future research can use IoT throughout every educational institution and analyze the impact. In the fewer keywords, the research opportunities are the development of learning techniques and the emergence of the IoT as a learning system. Other keywords are used to find novelties and recommend the implementation, development, upgrade, installation, or further research. Researchers may identify the critical study paths of researchers in each set era, the issues most important to Scopus' IoT, and the researchers responsible for the most significant influence. As a result, it also aids in narrowing down the future trends that may be established in these domains of study.
 
 Conclusion: The research opportunities are developing learning techniques and the emergence of the IoT as a learning system. Other keywords are used to find novelties and recommend the implementation, development, upgrade, installation, or further research.

Synergistic Integration of Blockchain and Machine Learning: A Path to a Decentralized Intelligent Future

The integration of blockchain and machine learning has emerged as a promising paradigm that can revolutionize various industries and applications. Blockchain’s decentralized and immutable nature, coupled with the analytical capabilities of machine learning, presents new opportunities for secure and transparent data sharing, collaborative model training, and intelligent decision-making. This research paper explores the concept of synergistic integration of blockchain and machine learning, providing an overview of the underlying technologies, related work, and existing frameworks. It proposes a novel Decentralized Intelli- gent Learning Network (DILN) framework that combines the strengths of both technologies to create a decentralized and efficient ecosystem for collaborative machine learning applications. The paper presents case studies in healthcare, finance, supply chain management, IoT, and academic research to showcase the potential impact of this integration. Furthermore, it discusses technical approaches, challenges, and ethical considerations to address in the deployment of decentralized intelligent systems. The research paper concludes by encourag- ing further research and development in the field to unlock the full potential of this transformative technology.

A comprehensive evaluation approach for efficient countermeasure techniques against timing side-channel attack on MPSoC-based IoT using multi-criteria decision-making methods

ContextTiming side-channel attack countermeasure techniques (TSCA-CTs) evaluation is a multi-criteria decision-making (MCDM) problem based on different MPSoCs of IoT platform architectures, performance, and design overhead. Therefore, the Fermatean by fuzzy decision opinion score method (F-FDOSM) for prioritizing the powerful countermeasure technique against timing side-channel attack is the best approach because it employs the most efficient MCDM ranking technique. Nonetheless, the FDOSM method needs to weigh the criteria before being submitted for the ranking process. In order to address this theoretical challenge, the Criteria-importance through inter-criteria correlation (CRITIC) technique can be applied as an effective MCDM weighting technique to offer an explicit weight for a set of criteria with no inconsistency based on the standard deviation, which uses correlation analysis to determine the relevance of each criterion. ObjectivesThis research proposes a Fermatean-FDOSM framework for evaluating TSCA-CTs in the context of MPSoCs-based IoT and CRITIC techniques to weight the criteria. MethodsThe methodology is presented in three phases. Firstly, a proposed countermeasure techniques dataset was collected that included seven defense approaches (e.g., Gossip and SER router) based on ten criteria (e.g., number of malicious IP cores, number of routers, power, latency… etc.). Then, the decision matrix was built based on an intersection of the countermeasure techniques as an alternative and MPSoC design and performance criteria. Then, the multi-criteria decision-making methods were integrated. The CRITIC method for criteria weighting was followed by the development of the Fermatean-FDOSM method for ranking. Results(1) CRITIC weighting shows that MPSoC NoC throughput (packet/clock) is the highest weight criterion, whereas latency (clock/cycle) is the less weight criterion. (2) The Fermatean-FDOSM-based group ranking shows that the Adaptive routing countermeasure technique is the highest score alternative compared to the Combined with separate interface hybrid approach. (3) The TSCA-CTs priority ranks were subjected to a systematic ranking that was confirmed by strong correlation results throughout ten criterion weight values. A comparison with recent studies confirmed the feasibility of the proposed framework. ConclusionThe outcomes of this study are anticipated to give particular knowledge and direction to those who want to do decision theory-based MPSoCs-based IoT and NoC communication security research.

An Application of Blockchain: A Review

Population growth and urbanization have accelerated waste generation, making waste management a global challenge. Technological advancements such as IoT, big data analytics, cloud computing, machine learning, and blockchain have supported various approaches to waste management. Blockchain provides information security, integrity and data record keeping, making it attractive for conventional garbage. However, many people have not yet accepted it. This research paper provides an overview of blockchain’s role in waste management and explores its potential to integrate IoT, AI, and social research. The adaptability of blockchain technology is becoming more and more apparent as more businesses investigate its possible uses. Although the financial services industry has received the majority of attention to yet, blockchain is starting to be incorporated into the operations of other service-related industries, such as healthcare. This paper explores the various ways that blockchain technology can be applied to the healthcare sector. The paper illustrates possible effects, goals, and possibilities related to this disruptive technology using instances from public healthcare administration, patient-centered medical research, and pharmaceutical anti-counterfeiting initiatives.

Internet of Things - IoT research trends from a bibliometric analysis

This bibliometric study aims to analyze the scientific literature on information systems and IoT using the Scopus database, which has the largest number of indexed journals, through quantity, quality, and structure bibliometric indicators, to identify the most relevant research actors. Papers were selected that had information systems and IoT in their title. The analyses were performed in Rstusio, VosViewer and Excel. The results are classified by authors, journals, countries of publication, and keywords. With the aim of identifying research trends in the subject. Among the main findings, it is a topic of interest due to the growth in the last few years. The collaborative works are reflected among countries such as the United States, China, United Kingdom, and authors from the same countries dispersing the publications in important journals of Information technologies, such as the IEEE. Additionally, the main topics are related to reducing risks in information systems, logistics systems, health, smart cities, real-time information, and decision making.

A bibliometric review: Exploring the intersection of the Internet of things and open innovation

The Internet of Things (IoT) has dominated the technological environment recently. It is shaping how organizations interact, collaborate, and share knowledge. Its ability to connect everything creates endless opportunities for open innovation (OI) and vice versa. This paper aims to provide a bibliometric review to assess the research status quo and emerging trends on the role of IoT in open innovation. Our findings highlight the evolution of research on IoT and OI over time, the most influential authors and countries in this field, and the topmost journals and conferences in which IOT and OI researchers published their work. We also used Vosviewer to map keywords co-occurrence, which led to identifying six distinct related clusters. Finally, we identified limitations and future research avenues that may enable academics and practitioners to embrace the potential of IoT in driving open innovation initiatives.

Evaluation Approach for Efficient Countermeasure Techniques Against Denial-of-Service Attack on MPSoC-Based IoT Using Multi-Criteria Decision-Making

Context: Denial-of-Service Attack countermeasure techniques (DoS A-CTs) evaluation is a Multi-criteria decision-making (MCDM) problem based on different MPSoCs of IoT platform design, performance, and design overhead. Therefore, the Fermatean by fuzzy decision opinion score method (F-FDOSM) for prioritizing the powerful countermeasure technique against Denial-of-Service (DoS) attack is the best approach because it employs the most efficient MCDM ranking technique. Nonetheless, the FDOSM method needs to weight the criteria before being submitted for the ranking process. In order to address this theoretical challenge, the Criteria-importance through inter-criteria correlation (CRITIC) technique can be applied as an effective MCDM weighting technique to offer an explicit weight for a set of criteria with no inconsistency based on the standard deviation, which uses correlation analysis to determine the relevance of each criterion. Objectives: This research proposes a Fermatean-FDOSM framework for evaluating DoS A-CTs in the context of MPSoCs-based IoT and CRITIC techniques to weight the criteria. Methods: The methodology is presented in three phases. Firstly, a proposed countermeasure techniques dataset was collected that included eighteen defense approaches (e.g., Sniffer, SeRA, and RLAN) based on thirteen criteria (e.g., size, power, latency, and effectiveness…etc.). Then, the Decision matrix (DM) was built based on an intersection of the countermeasure techniques as an alternative and MPSoC design and performance criteria. Then, the multi-criteria decision-making methods were integrated. The CRITIC method for criteria weighting was followed by the development of the Fermatean-FDOSM method for ranking. Results: (1) CRITIC weighting shows that MPSoC NoC Routing Algorithm (XY and YX) is the highest weight criterion, whereas latency (clock/cycle) is the less weight criterion. (2) The Fermatean-FDOSM-based group ranking shows that the Collision Point Router Detection (CPRD) countermeasure technique is the first-ranked alternative compared to the Secure Model Checkers (SMCs) approach. (3) The DoS A-CTs priority ranks were subjected to a systematic ranking that was confirmed by solid correlation results throughout thirteen criterion weight values. A comparison with recent studies confirmed the feasibility of the proposed framework. Conclusion: The results of this research are expected to provide a specific understanding and guide for those who want to engage in MPSoCs-based IoT and NoC communication security research with decision theory.

Effects of Body Shadowing in LoRa Localization Systems

Applications where a person carries environmental sensors benefit from knowing the position of the individual. Researchers can locate the device by analyzing the radio signals used for sending information. However, it is necessary to know the impact of body obstruction on communication links and, therefore, on the localization system. The paper studies this impact by using field tests employing one LoRa node with an omnidirectional antenna in an outdoor parking lot with vegetation. The tests use four LoRa gateways located in the corners of the area to measure the signals with and without body obstruction. The node was located on the subject’s chest to maximize signal obstruction. Results show that signal strength decreases by an average of 3 dB on links with body shadowing. Additionally, the study uses these signals with machine learning and Kalman filter localization algorithms. Finally, the results show that body shadowing affects location accuracy in most methods. These results are useful for IoT researchers that need to locate people carrying LoRaWAN devices but cannot use GPS/GNSS due to energy constraints.

Internet of things in low-power wide area network and short range network: A review

In the digital age, people may not be able to live without smart devices because they easily adopt them. IoT has made smart devices possible, which consist of sensors, connectivity, the cloud, and user interfaces. IoT research places sensors at the forefront of smart devices. IoT sensors are efficiently used in different IoT applications to develop smart environments in all fields. These sensors act like human sensing organs that collect real-time information from physical objects or the environment and convert it into electronic signals for processing in the IoT. Normally, two types of networks are applied in IoT: long-range low-power wide area networks and short-range networks. This review aims to assist researchers in understanding IoT applications and protocol classifications using short-range and long-range wireless technology that can communicate wirelessly within regions that facilitate IoT communication from a minimum distance of one millimeter to a few kilometers. This paper presents the types of sensors and various applications of IoT, as well as the energy requirements of different protocols and applications. Each network type has its own unique characteristics regarding energy consumption in the IoT network.

Virtual IoT Device in C# WPF Using Sinric Pro

Purpose: IoT is becoming the leading player in the industrial automation environment. In most scenarios, we experiment with IoT using a physical device. We can also research using a virtual device that can perform as real hardware. Without buying any physical hardware, we can visualize the status of the operating load or device which is being triggered through the IoT server or client endpoint. The researcher who is not from an electronics background or does not have sufficient knowledge to continue IoT research can do experiments using it. This paper will show how we can create such kinds of virtual instruments or devices. We use the C# client application adopted from Sinric Pro. We simplified the client module for new researchers in the IoT field for easy understanding. We added some Graphical user elements to display the status of the devices in real-life we see. The code is available for customization. Design/Methodology/Approach: We create and configure the device inside the Sinric Pro IoT server. Download the C# client and customize it for better understanding. We added some graphical elements to display the status of the load. Then we trigger the load from any endpoint like Alexa, the server dashboard, or a mobile phone application. Findings/Result: We get better load status visibility using the GUI element and a minimalistic code structure to send or receive the data to and from the IoT server. Through this concept, we made IoT development or demonstration easy. With slight modifications, we can use this procedure to communicate with any IoT server. Originality/Value: Various clients of fake load for IoT are available. Here we are experimenting more realistic way. Fetching the status and triggers the load using visual indication as the real world does. So it will be more understandable to us, and also, we can trigger the load from our GUI, which has the feature to send the value to the respective load. Paper Type: Experimental-based Research.

Mapping Top Strategic E-commerce Technologies in the Digital Marketing Literature

The increasing use of e-commerce technologies has been studied in several fields and from different perspectives: technological, economic, organizational and social: hence, the need for a literature review that provides a map of top strategic e-commerce technologies from a managerial perspective and, more specifically, for digital marketing research. This paper aims to provide researchers with a comprehensive overview of the range of e-commerce technologies that have had a significant role in shaping digital marketing strategies. Based on a thematic analysis, e-commerce technologies were classified through eleven categories. The objective is to reveal how each set of technologies affected the different digital marketing strategies. Both descriptive and clustering analyses show that the most evoked technologies in the digital marketing literature are Information and Communication Technologies and platforms. Results outline the growing interest in artificial intelligence technologies. Moreover, this literature review reveals how digital marketing research has focused on technology-enabled segmentation and targeting strategies, along with the use of social platforms and the development of new products and services. The scarcity of marketing papers studying the impact of cloud technologies, IoT, blockchain and data analytics orient researchers towards exploring further the potential of these technologies for digital strategies.

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

PurposeInternet of Things (IoT) is a key enabler of big data and it is critical toward a new age of firm's architecture and proper decision-making for the industry. IoT adoption by the hospitality and tourism (H&T) industries is still rare and with contrasting evidence. The purpose of this study is to highlight the concept of IoT, its benefits, its challenges and its applications in the hospitality industry.Design/methodology/approachThis study presents an in-depth and critical review of the 78 articles published on IoT in hospitality, spanning more than seven years (from 2015 to March 2022). The eligible 78 articles were collected from the “Web of Science database” and H&T Social Sciences Citation Index-listed journals.FindingsThis systematic review exhibits that IoT in hospitality has advanced in the past seven years. IoT has brought many advantages and challenges in hospitality. However, hospitality IoT research is scarce, necessitating a profound and more thorough investigation of many significant matters.Practical implicationsThis systematic review provides significant implications and recommendations for hospitality sectors, IoT developers and manufacturers, governments and policymakers. This review ends with an in-depth research agenda with several research questions and a framework to achieve theoretical and empirical advances in future IoT research.Originality/valueTo the best of the authors’ knowledge, the current research is one of the first to systematically and critically review IoT with its concept, benefits, challenges and applications in a hospitality context. This major review provides a comprehensive idea of the focus of the past studies and what must be concentrated in future research.

Development of automatic medical internet of things system (MIoT) for robotic surgery with multi-protocol approach using COAP and MQTT protocols

Modern medical devices serving as blessings to patients worldwide as IoT systems are becoming pillar for healthcare professionals. IoT research is growing in the direction of automation of patient care systems to automation of robotic surgeries. Industrial IoT is serving numerous sectors like medical, agriculture, manufacturing and many more sectors. Hence, this paper presents the architecture for medical device sensors and devices which can be automated for fast response to incoming IoT protocol. We developed the multiprotocol communication approach where CoAP and MQTT protocols can respond to medical system control panel request by extracting the common message parameters. This can be helpful for several medical systems which need automation and quick response systems can be developed using proposed approach.

The big picture on the internet of things and the smart city: a review of what we know and what we need to know

This study examines how the application of the IoT in smart cities is discussed in the current academic literature. Based on bibliometric techniques, 1,802 articles were retrieved from the Scopus database and analyzed to identify the temporal nature of IoT research, the most relevant journals, authors, countries, keywords, and studies. The software tool VOSviewer was used to build the keyword co-occurrence network and to cluster the pertinent literature. Results show the significant growth of IoT research in recent years. The most productive authors, journals, and countries were also identified. The main findings from the keyword co-occurrence clustering and an in-depth qualitative analysis indicate that the IoT is used alongside other technologies including cloud computing, big data analytics, blockchain, artificial intelligence, and wireless telecommunication networks. The major applications of the IoT for smart cities include smart buildings, transportation, healthcare, smart parking, and smart grids. This review is one of the first attempts to map global IoT research in a smart city context and uses a comprehensive set of articles and bibliometric techniques to provide scholars and practitioners with an overview of what has been studied so far and to identify research gaps at the intersection of the IoT and the smart city.