Internet Of Things Applications Research Articles

Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification

Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications.

Implementation of Sensors in IOT Application

In the past few decades, the importance of the Internet of things (IOT) has emerged in various fields. It is a technology that connects the Internet to all devices that are incapable to connect to the Internet. Therefore, this paper aims to provide convenient realization of sensors in the IOT system. The idea based on the start of the IOT, as it is a modern idea that combines ease of work and development, in the company of itself and benefitting of limited communications such as short message service (SMS) and broad communications such as wireless fidelity (WIFI), toward ensure robust and applicable sensors that can be serve the community in several aspects, predominantly people with special needs. The system uses Arduino, event stream processing (ESP), sensors and global system for mobile (GSM) to control devices. The system is simulated by using an electronic circuit via Google assistant and messages application.

Matheamatical Modeling of Multi-Objective Adaptive Neuro Fuzzy Inference Based Optimization for IOT Based Wireless Sensor Network

The proliferation of the Internet of Things (IoT) and its integration with wireless sensor networks (WSNs) necessitates advanced optimization techniques to enhance performance and resource allocation while ensuring reliability and energy efficiency. This study introduces a mathematical modeling approach utilizing a Multi-Objective Adaptive Neuro-Fuzzy Inference System (MO-ANFIS) designed for optimizing IoT-based WSNs. The proposed model synergistically combines the adaptive learning capabilities of neural networks with the reasoning prowess of fuzzy inference systems, encapsulated within a multi-objective framework to concurrently address key operational objectives such as minimizing energy consumption, maximizing network lifetime, and enhancing data accuracy and throughput. The mathematical model is formulated to dynamically adapt to changing network conditions and sensor inputs, enabling real-time tuning of fuzzy rules and membership functions through backpropagation neural training. This adaptability ensures optimal performance despite the variable nature of IoT environments. Simulation results demonstrate that the MO-ANFIS model significantly outperforms traditional optimization methods, offering a robust, scalable solution for complex, dynamic WSNs in the IoT landscape. The findings suggest promising applications in various domains, including smart cities, environmental monitoring, and healthcare, where IoT integration is pivotal. This research not only bridges the gap between theoretical fuzzy-neural frameworks and practical IoT applications but also sets a foundation for future explorations into intelligent, adaptive network management systems.

Blockchain Based Proxy Re-Encryption for Secure Data Sharing

This paper proposes a solution to secure data sharing within the Internet of Things (IoT) by combining Proxy Re-Encryption (PRE) and Blockchain. In IoT networks, data must often be shared across multiple devices and users, raising concerns over data privacy and security. Proxy Re-Encryption enables controlled data sharing without disclosing original information, while Blockchain technology offers an immutable, transparent ledger for tracking data transactions. Together, these technologies ensure data integrity, privacy, and efficient access control, making them suitable for modern IoT applications

Design of a Multiband Antenna Using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT Applications

ABSTRACTThe rapid expansion of Internet of Things (IoT) applications presents unprecedented challenges for antenna design, demanding solutions that are versatile, efficient, and capable of operating across multiple frequency bands. This paper addresses these challenges through the development of a design of a multiband antenna using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT applications (DMA‐ACWGAN‐IoT). Here, the Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) is employed to generate synthetic data representing electromagnetic field distributions and antenna characteristics across various frequencies. The proposed metamaterial‐based Multiple‐Input Multiple‐Output (MIMO) antenna contains four discrete elements, each provided by a micro strip feed. The structures overall width and length are 60 and 52 mm. The metamaterial is printed on a patch and dispersed from the fields with the most effective coupling. The proposed structures strong impedance bandwidth works at 8.3 GHz, 10.8 GHz, 12.3 GHz, 13.7 GHz, 16.1 GHz, and 18.1 GHz, fabricating the proposed antenna prototype using a substrate made of FR‐4 material. The proposed DMA‐ACWGAN‐IoT design provides 6.5 dB maximum gain and 25.40%, 21.60%, and 20.05% higher efficiency compared to existing dual band antenna design with resonance frequency prediction under machine learning models (DBA‐PRF‐ML), machine learning verification depending on a distinctive SWB multiple slotted four‐port high isolated MIMO antenna loaded with metasurface for the applications of IoT (SWB‐MIMO‐IOT‐ML), and dual‐band miniaturized composite right–left‐handed transmission line ZOR antenna along machine learning method for microwave communication (DB‐ZORA‐MC‐ML).

Smart Hydroponics Innovation: Blyink IoT Application for Sustainable Food Security

The rapid development of technology has a significant impact on various fields, one of which is technology in agriculture. The use of technology in agriculture, such as the Internet of Things (IoT), aims to make it easier for farmers to monitor, manage and carry out agricultural activities. The solution for farmers to utilize technology in monitoring agricultural land to get more effective and efficient harvests so that they can increase productivity and yields is by using IoT technology. The application of IoT technology in hydroponic farming systems can be done to determine the parts per million (ppm) content in water and water levels in real-time. The method used in this community service is socialization and training for farmers in Medalkrisna Village, as well as testing using the prototyping method connected to the Blynk application. This tool allows farmers to monitor parts per million of water and water levels online and makes it easier for farmers to monitor. The managerial implications of implementing Blyink’s Internet of Things-based hydroponic system to improve food security include increasing the efficiency of resource management and optimizing agricultural production.

Artificial intelligence in the Internet of Things: Integrating and optimizing AI algorithms for real-time data processing and decision-making

Abstract. The introduction of Artificial Intelligence (AI) will provide great opportunities and pose significant challenges in Internet of Things (IoT) systems. IoT devices can create huge amount of data in real-time. Meanwhile, it is crucial to handle immediate processing and decision-making in IoT applications. However, IoT devices are usually constrained by limited computation power, memory, and energy compared to traditional devices, making the deployment of efficient AI algorithms a challenging task. In this paper, we present different algorithmic strategies to overcome the above problems, including model compression, quantization, and hardware accelerators. On the other hand, we also focus on the role of decentralization and edge computing architectures to increase the scalability and performance of AI-IoT systems. Finally, this paper also reviews energy-efficient AI algorithms and latency reduction methods to make the decision process real-time for various IoT applications.

A Proposed Perspective for the Successful Deployment of Internet of Things in a Smart Home Environment

The Internet of Things (IoT) technology is used in smart homes to enhance comfort, security, and energy efficiency. This study outlines a theoretical framework for the deployment and dissemination of IoT technologies in intelligent residential environments. The framework of this study highlights the significance of many components that cater to people’s individual needs. The study offers a systematic explanation of IoT principles, tangible devices, services, data creation, software, applications, and logistical needs. Furthermore, it contains an extensive table that assists users in choosing the most appropriate resources and components. The study points to smart home systems based on IoT, which utilize sensors, controllers, and cloud solutions to manage data and provide user control while ensuring privacy and confidentiality. The study addresses various needs of users by utilizing a semantic vision framework for smart house design that guarantees economic advantages, enhanced security, and user contentment, ultimately leading to enhanced community interactions and the overall welfare of residents.

Research on physical health assessment and intervention strategies for college students based on biomarkers

Effective physical health assessments and intervention strategies are crucial for college students, who often face unique stressors that can impact their physiological health. In this analysis, we explore how biomarkers can be utilized to assess physical health and the efficacy of targeted interventions for this demographic. Hypotheses were developed based on the three intervention groups: (1) a face-to-face intervention group receiving a tailored wellness program, (2) an Internet of Things (IoT) gadgets intervention group using a health management assessment, and (3) a control group with only baseline and follow-up measurements. College students (n = 204) were randomized into these groups. Biomarkers related to stress and inflammation (e.g., cortisol levels, high-sensitivity C-reactive protein (hs-CRP)) were measured at baseline, post-intervention (10 weeks), and follow-up (36 weeks). Psychological and physical health outcomes were also assessed using standardized questionnaires. The Mixed-Effects Models, ANOVA, and Structural Equation Modeling (SEM) were used to analyze the differences between groups, changes in biomarkers over time, and the relationships between psychological, physiological, and lifestyle variables. Results indicated that the face-to-face wellness group demonstrated significantly better physical health outcomes compared to both the IoT gadgets group and control group, with diet modification showing the highest effectiveness, followed by physical activity and stress management interventions.

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Enabling blockchain-IoT convergence for industrial revolution 4.0 innovation: A critical review

The Fourth Industrial Revolution (4IR) has revolutionised global interactions through the Internet of Things (IoT), connecting over 40 billion devices by 2030, raising significant security, protection, and privacy concerns. Blockchain technology offers a decentralised, secure, and transparent solution for data sharing and storage, leveraging its core characteristics—immutability, data integrity, transparency, and consensus—to enable tamper-proof records, real-time tracking, and secure data sharing in supply chain management and healthcare. Despite high implementation costs, blockchain-IoT convergence promises transformative benefits, including enhanced efficiency, reduced costs, and improved security. This study provides an in-depth analysis of blockchain's applications in IoT, highlighting its potential to enhance data security and privacy, improve supply chain management, facilitate secure healthcare data exchange, and enable real-time tracking and monitoring. Future research directions include scalability solutions, interoperability protocols, regulatory frameworks, blockchain-based supply chain management, and quantum-resistant blockchain networks.

Robotics in Disaster Management: AI-Driven Rescue, Aerial Mapping, and Emerging IoT Applications for Effective Response

This review paper explores the growing role of robotics in disaster management, focusing on the integration of artificial intelligence (AI), Internet of Things (IoT) applications, and advanced simulation environments. Highlighting lessons from events like the Fukushima nuclear disaster and Wenchuan earthquake, it examines the deployment of aerial, ground, and underwater robots in tasks such as data collection, search and rescue, and hazard monitoring. AIoT frameworks enable these robotic systems to classify objects and make decisions on-site, supporting timely and efficient responses in hazardous conditions. Virtual platforms like the DARPA Virtual Robotics Challenge, using simulators such as Gazebo and CloudSim, facilitate realistic testing of robotic capabilities in disaster scenarios, advancing operational readiness through virtual training. This review synthesizes recent progress in AI-driven disaster robotics, identifies challenges, and discusses potential improvements for future applications.

Transfer-Learning-Based Gesture and Pose Recognition System for Human–Robot Interaction: An Internet of Things Application

Transfer-Learning-Based Gesture and Pose Recognition System for Human–Robot Interaction: An Internet of Things Application

Enhancing IoT data acquisition efficiency via FPGA-based implementation with OpenCL framework

The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.

Edge Computing and Analytics for IoT Devices: Enhancing Real-Time Decision Making in Smart Environments

This article presents a comprehensive analysis of edge computing and analytics for Internet of Things (IoT) devices, addressing the growing need for real-time processing and reduced latency in IoT applications. We explore the evolution of IoT architectures, from cloud-centric models to edge-enabled systems, and examine the key components and data flows in edge computing environments. Through a detailed comparison of cloud and edge processing, we demonstrate significant latency reductions across various IoT scenarios, with improvements from hundreds of milliseconds to mere milliseconds. The article delves into crucial aspects of data management in edge computing, including local versus cloud processing trade-offs, data synchronization strategies, and privacy considerations. A case study of an edge analytics pipeline in a smart factory setting showcases practical implementations, revealing substantial improvements in anomaly detection speed, bandwidth utilization, and overall system efficiency. The case study achieved a 92.5% reduction in anomaly detection latency and an 85% decrease in bandwidth usage. Finally, we discuss ongoing challenges and future directions in edge computing, including scalability issues, standardization efforts, and the integration of emerging technologies such as 5G and AI accelerators. This article contributes to the growing body of knowledge on edge computing in IoT, offering insights into its transformative potential for creating more responsive and intelligent systems across diverse applications.

The role of telecommunications in enabling Internet of Things (IoT) connectivity and applications

This review paper explores the pivotal role of telecommunications in enabling Internet of Things (IoT) connectivity and applications, providing a comprehensive overview of the current landscape and future prospects. The primary objective is to synthesize existing research and industry insights to highlight how advancements in telecommunications infrastructure and technology have catalyzed the adoption and expansion of IoT. The review systematically examines the evolution of telecommunications technologies, including 4G, 5G, and emerging network protocols such as Narrowband IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). It assesses how these technologies enhance IoT connectivity by providing the necessary bandwidth, low latency, and reliability for real-time data exchange and device interoperability. Key findings from the literature reveal that the deployment of 5G networks has been a game-changer for IoT, offering unprecedented speeds and connectivity capabilities that support a wide range of applications in smart cities, healthcare, agriculture, and industrial automation. Additionally, the paper discusses the role of telecommunications in addressing challenges related to security, scalability, and energy efficiency in IoT deployments. The review concludes that telecommunications are integral to the growth and sustainability of IoT ecosystems. As telecommunications technology continues to advance, its ability to support increasingly complex and ubiquitous IoT applications will drive innovation and enhance efficiency across various sectors. The paper emphasizes the necessity for ongoing investment in telecommunications infrastructure and research to fully exploit the transformative potential of IoT connectivity and applications.

[formula omitted]: A blockchain-based secure access control management for the Internet of Things

The Internet of Things (IoT) paradigm has widespread applications across many fields in which private and sensitive user or environmental data are sensed and shared. Most present-day IoT applications depend on centralized cloud servers for authentication and access control. Validating the identity of a user and determining the legitimacy of his/her access requests require multiple rounds of data communications over the untrusted Internet, exposing sensitive data to potential attacks. Thus, protecting these data from security and privacy attacks and ensuring legitimate access is imperative. To address this challenge, we adopt an emerging technology called blockchain to propose a decentralized security framework called BloAC. It ensures secure access control in IoT networks without the intervention of the back-end cloud. We have used the Hyperledger Fabric, an open-source, permissioned blockchain platform, for implementing a prototype system using customized attribute-based access control (ABAC) policies. We have performed simulated and real test bed-based experiments to illustrate that BloAC outperforms the cloud–server-based access control in latency and scalability, significantly reducing latency by up to 42.45% compared to cloud-based solutions. Finally, we conduct a security analysis to formally verify the ABAC policies used in BloAC and establish its robustness against attacks theoretically and using the AVISPA tool.

Application of Internet of Things (IoT) in Energy Infrastructure: Lessons for the Future of Operations and Maintenance

The application of the Internet of Things (IoT) in energy infrastructure is revolutionizing operations and maintenance practices, driving efficiencies, and enhancing sustainability across the sector. This paper explores the transformative impact of IoT technologies on energy infrastructure, focusing on how they facilitate real-time monitoring, predictive maintenance, and data-driven decision-making. By leveraging IoT devices and systems, energy operators can optimize performance, reduce operational costs, and minimize downtime, ultimately contributing to more resilient and sustainable energy systems. The discussion highlights key IoT applications within energy infrastructure, including smart grids, renewable energy systems, and conventional power plants. Smart sensors and devices enable continuous data collection, providing insights into equipment performance, energy consumption, and environmental conditions. Predictive analytics, powered by IoT data, allows operators to anticipate equipment failures and conduct maintenance proactively, thereby extending asset life and improving reliability. Additionally, the abstract addresses the challenges associated with IoT integration in energy infrastructure, such as cybersecurity risks, data management issues, and the need for interoperability among diverse systems. It emphasizes the importance of establishing robust security protocols to protect sensitive data and ensure the integrity of IoT networks. Furthermore, the role of regulatory frameworks and industry standards in promoting the safe and effective deployment of IoT technologies is discussed. The paper also examines case studies of successful IoT implementations in energy infrastructure, showcasing the lessons learned from these initiatives. These examples illustrate the potential for IoT to enhance operational efficiency, reduce greenhouse gas emissions, and support the transition to cleaner energy sources. In conclusion, the abstract underscores that the adoption of IoT technologies in energy infrastructure is not just a trend but a necessity for the future of operations and maintenance. As the energy sector continues to evolve, embracing IoT will be crucial for achieving sustainability goals and meeting the growing demands for reliable energy supply.

AI-Augmented Chemical Engineering Models for Predictive Battery Maintenance in IoT Applications

This research presents a cutting-edge approach that combines chemical engineering principles with artificial intelligence (AI) to enhance battery maintenance in Internet of Things (IoT) applications. The study develops predictive maintenance models capable of accurately forecasting battery degradation, thereby optimizing usage patterns and extending battery life. By integrating electrochemical data with AI-driven predictive analytics, the project enables real-time monitoring of battery health, achieving over 90% accuracy in anticipating maintenance needs, which reduces unplanned downtime and lowers operational costs. Employing machine learning frameworks, data analysis tools, and chemical engineering simulation software, the focus is on lithium-ion and solid-state batteries, commonly used in IoT networks across sectors such as smart cities, agriculture, and healthcare. Key results include a 20−30% increase in battery lifespan and a 15% improvement in energy efficiency, significantly reducing electronic waste and environmental footprint. This interdisciplinary approach provides a foundation for sustainable battery technology in IoT technology, paving the way for future advancements in AI-augmented predictive maintenance and chemical engineering models for next-generation applications. The integration of AI with chemical engineering principles allows for the development of models that adapt to the unique electrochemical behavior of different battery types, such as lithium-ion and solid-state batteries. By analyzing lifecycle data and device usage patterns, predictive models can account for variables that influence battery performance over time, including temperature fluctuations and charging cycles. This project utilizes neural networks and reinforcement learning algorithms to train models capable of learning from historical data, enhancing their predictive capabilities. A significant aspect of this research involves the preprocessing of battery lifecycle data to ensure high data quality, which is critical for accurate model training and validation.

IoT—A Promising Solution to Energy Management in Smart Buildings: A Systematic Review, Applications, Barriers, and Future Scope

The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector.