Internet Of Things Applications Research Articles

TFSemantic: A Time–Frequency Semantic GAN Framework for Imbalanced Classification Using Radio Signals

Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this article, we propose a time–frequency semantic generative adversarial network framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution and attention semantic feature embedding methods for the semantic extraction module. A discrete wavelet transform is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.

Enterprise asset management and accounting using the Internet of Things and big data

With the rapid development of information technology, the Internet of Things (IoT) and big data have gradually become the core tools of modern enterprise management and decision-making. This study explores the application and potential value of IoT and big data in the field of enterprise assets and accounting. Through strategic questionnaire design and multi-channel data collection, a corresponding data model is constructed, and the effectiveness of the model is further verified. The results show that these technologies can not only improve the management efficiency of enterprises, but also provide more accurate and timely data support for corporate decision-making. However, in practical applications, enterprises also need to face data integrity, technology updates, and a host of other challenges. This study not only provides enterprises with an opportunity to gain an in-depth understanding of these technologies, but also provides valuable implications for future research and applications.

The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead

Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry.

IoTvulCode: AI-enabled vulnerability detection in software products designed for IoT applications

AbstractThe proliferation of the Internet of Things (IoT) paradigm has ushered in a new era of connectivity and convenience. Consequently, rapid IoT expansion has introduced unprecedented security challenges , among which source code vulnerabilities present a significant risk. Recently, machine learning (ML) has been increasingly used to detect source code vulnerabilities. However, there has been a lack of attention to IoT-specific frameworks regarding both tools and datasets. This paper addresses potential source code vulnerabilities in some of the most commonly used IoT frameworks. Hence, we introduce IoTvulCode - a novel framework consisting of a dataset-generating tool and ML-enabled methods for detecting source code vulnerabilities and weaknesses as well as the initial release of an IoT vulnerability dataset. Our framework contributes to improving the existing coding practices, leading to a more secure IoT infrastructure. Additionally, IoTvulCode provides a solid basis for the IoT research community to further explore the topic.

Predicting the Output Performance of Triboelectric Nanogenerators Using Highly Representative Data‐Based Neural Networks

Triboelectric nanogenerators (TENGs) are promising potential sustainable power sources for wireless sensing networks within the Internet of Things (IoT) realm. Developing an efficient TENG evaluation model, characterized by high speed, accuracy, and representativeness, facilitates its integration into practical applications, which is urgent and lack of investigation currently. Herein, an artificial intelligence (AI) based evaluation model is developed to predict the performance of freestanding rotational TENGs (FR‐TENGs) for demonstration. An accurate and representative train dataset is essential for development of AI‐based evaluation model, which has been generated using finite element analysis and equivalent circuit simulation alongside the non‐dominated sorting genetic algorithm II. Through comprehensive experiments and simulations, the accuracy of the model has been verified in predicting the power output performance of FR‐TENGs, which has 99.6% (three design parameters) and 99.2% (seven design parameters) maximum train set accuracy. More importantly, the predicted results from the AI‐based evaluation model have notably expanded the coverage of data and significantly expedited the generation time from days to seconds. Herein, the use of AI in assessing the performance of TENGs is enhanced. The TENG design process can be significantly simplified, while maintaining a high evaluation model accuracy, thus promising advancements of IoT applications in future.

Smart Home Powered by Solar: IoT-based SEPIC Converter Control

Background: In this article, an IOT solution for managing and controlling a PV system with applications for the home is presented. A DC-DC SEPIC converter, a bidirectional converter, a PWM generator, and a single-phase voltage source inverter with active clamping are used for power conditioning. An output voltage control loop is implemented, and real-time online communication with an internet server is accomplished using the PIC microcontroller. Methods: The ESP8266MOD WIFI IOT module works with the Blynk mobile app. Experimental findings showing the proper operation of IOT features demonstrate the effectiveness of voltage regulation. In this article, frequency is taken into consideration as solar power is integrated with the grid. A user may easily control the DC-DC converter, load batteries, and monitor battery voltage through our userfriendly GUI interface. Results: In this paper, an IOT (Internet of Things) application was used to control and track the output of a modest PV power plant. The study shows the efficiency of the digitally controlled system developed to regulate the output voltage of the SEPIC DC-DC converter. Practical Significance: They further argue that the ESP8266 and microcontroller's versatility make them an appropriate instrument for remote and real-time monitoring and control of the proposed model from around the world. The energy that is produced by the sun is added to the grid so that it may be put to effective use and contribute to satisfying the rising demand for energy. Solar energy is the input that SEPIC uses to charge the batteries and for all of its other processes. In the event that solar photovoltaic (PV) panels are not available, the SEPIC DC-DC converter will derive its energy from AC power. Conclusion: In this article, an IOT (Internet of Things) application was used to control and track the output of a modest PV power plant. The research demonstrates the effectiveness of the digitally controlled system created to manage the output voltage of the SEPIC DC-DC converter. They further argue that the ESP8266 and microcontroller's versatility make them an appropriate instrument for allowing remote and real-time monitoring and control of the proposed model from any part of the world. The energy that is produced by the sun is added to the grid so that it may be put to effective use and contribute to satisfying the rising demand for energy. Solar energy is the input that SEPIC uses to charge the batteries and for all of its other processes. In the event that solar photovoltaic (PV) panels are not available, the SEPIC DC-DC converter will derive its energy from AC power.

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive IoT networks

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive <scp>IoT</scp> networks

Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset

The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks—blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. •The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks.•This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.

Advancing Healthcare Networks: Optimizing Multi-Objective RPL for Diverse Traffic in Low-Power, Lossy Environments

RPL: Powering modern healthcare IoT Networks. The Routing Protocol for Low Power and Lossy Networks(RPL) is a protocol specifically designed for routing in networks characterized by low power and lossy connections built on IPv6. It is specifically designed for the growing use of Instantaneous IoT (Internet of Things) applications. These networks cater to the specialized requirements of Low Power (resource-constrained) and Lossy Networks(unstable networks), also known as LLNs, where routing data smoothly and prioritizing traffic types are the significant challenges. Studies have shown that standard RPL, which makes use of a set of routing rules, struggles to provide the level of performance that many IoT applications in modern-day healthcare demand. This limits RPL’s potential in scenarios where we have a mix of data traffic, which is very common in healthcare settings. To handle the diverse data traffic found in healthcare settings, our method involves distributing several instances of RPL settings throughout the network. The main objective of this strategy is to improve the effectiveness of important and critical healthcare applications. We have developed and thoroughly tested our system using Contiki, which is an open-source IoT operating system. We have three instances in our MultiInstance RPL setup, each of which is intended to handle different kinds of network traffic and provide varying Quality of Service (QoS) measures. Different sets of Objective Function (OF) rules are used by each instance to decide which route is optimal for the data it processes. We have made use of OF0 (Objective Function 0) and MRHOF (Minimum Rank with Hysteresis Objective Function). We have rigorously tested our solution. Our focus has been on metrics like delay, average energy consumption and Packet Delivery Ratio (PDR). Compared to standard RPL, our approach improved packet delivery rates and significantly reduced delays for high priority data packets. This research shows how to better address the complex data traffic needs of real-time healthcare IoT networks.

Efficient Cyber Security Framework for IoT Using Machine Learning Algorithms

Through the network's infrastructure, the IoT can impart perception, recognition, and remote control to inanimate objects. Due to IoT's characteristics, it's possible to integrate the real world into a digital system, which improves precision, productivity, and bottom line. While traditional internet infrastructure comprises of highly capable computers and servers, IoT gadgets have limited processing power and storage space. The authentication and key agreement system SecureAuthKey is very lightweight. The suggested technique is meant to solve the security and privacy problems that plague modern constraint-based CPS programmes. A lightweight approach for authenticating cyber-physical objects is one of the expected outcomes. Adaptation and autonomy in cyber-physical systems need not be compromised by a lack of trust or security in users' data or the devices themselves, according to a new type of security algorithm. To provide flexibility and scalability, a new middleware module has been developed on the Raspberry platform to facilitate communication between CPS-based devices and services. Secure Internet of Things (IoT) evaluation methodology that may be used in a variety of contexts and is user-focused. With the suggested system, the two CPS units will be able to authenticate one another. When a network grows larger, the possibility of an attack rises. Therefore, the IoT network is much more susceptible to attacks than conventional networks. As the number of connected devices grows, so do the number of potential threats to their security. To protect the IoT ecosystem from current threats, cutting-edge technology is essential. The proposed approach must be very efficient and have low computational overheads. It creates random session keys to protect wireless transmissions. The system is protected from a variety of cyber threats. The current constraint-based CPS system requires a new lightweight security solution.

Improved DAG in blockchain tangle for IOTA

The internet of things (IoT) enables machine-to-machine communication without human intervention. Consequently, every object connected to the internet can exchange information with each other. Internet of things application (IOTA) has undertaken a project to address the high transaction fees inherent in traditional blockchain systems and enhance the efficiency of microtransactions between machines by combining blockchain and IoT. IOTA employs its unique Tangle technology, which introduces a novel transaction consensus method, addressing the fee issues, limited scalability, and the inability to conduct offline transactions associated with traditional blockchains. This paper provides a detailed overview of the characteristics of the Tangle structure and the concepts applied in IOTA. Additionally, it explores potential approaches for integrating blockchain into IoT.

A Hybrid Wind and Raindrop Energy Harvesting Operating on Savonius Turbine

In offshore environments, abundant wind and rainfall resources await development, representing significant untapped energy potential for powering Internet of Things (IoT) devices. Triboelectric Nanogenerators (TENGs) are particularly suited for capturing continuous motion in offshore environments, presenting a reliable solution for sustainable energy generation in IoT applications. Herein, we propose a wind-droplet hybrid generator (WDHG) tailored for adaptive environmental energy harvesting, explicitly targeting the demanding conditions prevalent in offshore environments. The WDHG combines the efficiency of a helical Savonius TENG with the versatility of an interdigitated electrode TENG, enabling simultaneous wind and rain energy harvesting. The helical Savonius TENG exhibits superior aerodynamic performance, producing an output power ranging from 0.39 to 2.10mW with wind speeds increasing from 0.75 to 6.84m/s. Additionally, the droplet TENG generates a power output of 35.90mW/m2 under rainfall at 70mm/min. Optimizations include not only the comparative analysis of helical Savonius turbines and the variation in pole-pair numbers but also adjustments in fluorinated ethylene propylene (FEP) film thickness and electrode gap length for interdigitated electrode, alongside the development of a tailored power management circuit (PMC) featuring dual bridge rectifiers, a buck circuit module, and a smart load switch. The PMC enables continuous operation of a low-powered digital thermohygrometer under conditions of 4.5m/s wind speed, and drives a 20-second wake BLE server under 7.2m/s wind speeds. Integration of the WDHG with offshore wind and raindrop extends energy harvesting capabilities, exhibiting a great potential of providing reliable power source for IoT devices.

From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability

In contemporary computing paradigms, the evolution from cloud computing to fog computing and the recent emergence of federated-fog computing have introduced new challenges pertaining to semantic interoperability, particularly in the context of real-time applications. Fog computing, by shifting computational processes closer to the network edge at the local area network level, aims to mitigate latency and enhance efficiency by minimising data transfers to the cloud. Building upon this, federated-fog computing extends the paradigm by distributing computing resources across diverse organisations and locations, while maintaining centralised management and control. This research article addresses the inherent problematics in achieving semantic interoperability within the evolving architectures of cloud computing, fog computing, and federated-fog computing. Experimental investigations are conducted on a diverse node-based testbed, simulating various end-user devices, to emphasise the critical role of semantic interoperability in facilitating seamless data exchange and integration. Furthermore, the efficacy of federated-fog computing is rigorously evaluated in comparison to traditional fog and cloud computing frameworks. Specifically, the assessment focuses on critical factors such as latency time and computational resource utilisation while processing real-time data streams generated by Internet of Things (IoT) devices. The findings of this study underscore the advantages of federated-fog computing over conventional cloud and fog computing paradigms, particularly in the realm of real-time IoT applications demanding high performance (lowering CPU usage to 20%) and low latency (with picks up to 300ms). The research contributes valuable insights into the optimisation of processing architectures for contemporary computing paradigms, offering implications for the advancement of semantic interoperability in the context of emerging federated-fog computing for IoT applications.

An Efficient Design of a Three-Layer Magnitude Comparator for Nano-Scale IoT Applications Based on QCA Technology

The difficulty of further downscaling CMOS technology arises from the restriction of feature size reduction. Quantum-dot cellular automata (QCA) emerges as a paradigm-shifting successor to CMOS, heralding a new era of effective digital design at the nanoscale. It stands as an enticing frontier in nanoscale computing, with limited exploration into the realms of smaller QCA cells, elevated processing speeds, and more compact area requirements across diverse circuits. Within the intricate landscape of decoding circuits and process controllers, the binary comparator assumes a role of paramount significance. On the other hand, the quickly developing Internet of Things (IoT) market aims to produce high-speed, low-power gadgets. A comparator is a crucial component in the analog-to-digital conversion process used by IoT devices. In order to meet the power and latency requirements of IoT devices, a high-speed, low-power comparator is greatly required. Consequently, the strategic design of comparators within the QCA framework has ascended to a position of heightened importance in cutting-edge research. This study undertakes the formidable task of conceiving QCA-centric designs for MV32, the majority gate, and the inverter gate, thereby contributing to the development of a sophisticated multi-layered comparator architecture for IoT devices. In the realization of a three-layer comparator implemented in QCA, we attain an impressive feat—a minimal clock zone demanding only a singular clock pulse coupled with exceptional compaction (measuring at a mere 0.03 μm2). Experimental revelations corroborate the substantial advancement of the proposed design over traditional methodologies, particularly in terms of circuit area, cell count, and clock efficiency.

Exploring open source and proprietary LoRa mesh technologies

This paper explores low power wide area network (LPWAN) LoRa and its diverse variants, encompassing open-source and proprietary wireless mesh networks, operating over the physical LoRa or LoRaWAN layer. The primary challenge lies in defining an optimal LoRa mesh solution that balances cost-effectiveness, energy efficiency, low latency, long-range capability, and security. This study also comprehensively examines key LoRa mesh solutions from 2017 to 2024, as proposed by various authors. Furthermore, a detailed analysis is conducted to contrast open-source and commercial solutions, considering their applications, limitations, issues, characteristics, and pros and cons of mesh routing protocols. In the current landscape, the proliferation of open-source and proprietary LoRa mesh solutions has been instrumental in facilitating the connectivity of internet of things (IoT) devices. However, these solutions pose challenges related to energy consumption, latency, and suboptimal transmission throughput. These challenges are influenced by various LoRa characteristics such as spectrum factor, bandwidth, and transmission power, which directly impact the transmission range. Our research aims to perform a comparative analysis of existing LoRa mesh solutions by, systematically studying their advantages and disadvantages. This analysis offers valuable insights for making informed choices among these solutions in diverse domains for IoT applications.

Business Model Canvas for Internet of Things Application on Hydroponic in Tangerang

Internet of Thing (IoT) in hydroponics was developed to monitor and control the hydroponic environment for efficiency and effectiveness with maximum results purpose. Many papers expose about IoT application on hydroponic, this article completed it with business model so it will sustain as valuable business especially in Tangerang Banten Indonesia. It is interesting and necessary to analyze the business model, so that this valuable system of the solution can sustain and growth, also proper for the big picture purpose. Of seventeen (17) goals SDGs, hydroponic using IoT application is related to solving point number two (2) about zero hunger and point number thirteen (13) about climate action. This paper uses qualitative descriptive methodology which analyzes and provides points of BMC for IoT application on hydroponic. The result of this paper found that BMC is proper tool to analyze and describe business model for IoT of hydroponic. Then this paper also finds there are several points of revenue streams with some different customer segments, support from hydroponic community, and some collaboration with key partners is needed, which means the business cannot stand alone. This paper has limitations which could be further interesting topics for the next paper.

IoT applications in asset management: A review of accounting and tracking techniques

In the digital age, the confluence of Internet of Things (IoT) technologies with asset management practices heralds a transformative era, promising unprecedented efficiency and strategic advantage. This paper delves into the intricacies of this integration, exploring the evolution, challenges, and potential of IoT in reshaping asset management paradigms. With a keen aim to elucidate the transformative impact of IoT and navigate the complexities of its integration into asset management systems, the study adopts a qualitative research methodology, underpinned by a systematic literature review. This approach enables a comprehensive exploration of the subject matter, ensuring a nuanced understanding of the IoT's potential and pitfalls within the asset management domain. The findings illuminate a landscape where IoT applications significantly enhance asset utilization rates, underscored by empirical success stories and quantifiable improvements in operational efficiency. However, the journey reveals barriers—technical, organizational, and cultural—that impede seamless IoT adoption. Addressing these challenges, the paper offers strategic recommendations, emphasizing the need for strategic alignment, inter-organisational collaboration, and robust data management strategies to overcome integration hurdles. Conclusively, the study not only achieves its aim of highlighting the potential of IoT in asset management but also charts a course for future endeavors in this field. It posits that the strategic integration of IoT technologies stands as a cornerstone for the next generation of asset management practices, promising a future marked by enhanced efficiency, economic viability, and strategic agility. The recommendations provided serve as a compass for organizations embarking on the IoT integration journey, guiding them towards a horizon of transformative potential.

Design and Comparative Analysis of E-Shape and H-Shape Microstrip Patch Antenna for IoT Application

In this study, H-type and E-type microstrip antenna arrays with high efficiency and management are designed using Surrogate Model Assisted Differential Evolution Algorithm (SADEA) for antenna synthesis. In order to meet the increasing wireless communication requirements of the internet of things (IoT) technology it has become very important to design new antenna designs with artificial intelligence techniques to transmit electromagnetic waves between antenna elements in the most efficient way. With a view to operate in the 2.5 GHz band, which is widely used in wireless communication technologies. H-type and E-type microstrip antennas with a 2x2 structure are analyzed and finally compared. By using the SADEA method, the amplitude and the distance between the array elements are optimized. The total amplitude of the 4-element E-type and H-type microstrip antenna array is minimized to 2.45885 V and 2.14929 V, respectively, after optimization, while it is 4V for both before optimization.

Review of Hardware Platforms for Designing of Internet of Things (IoT) system

Review of Hardware Platforms for Designing of Internet of Things (IoT) system

Research on sustainable green building space design model integrating IoT technology.

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT's impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.