Smart IoT Research Articles

Retraction Note: Quantum healthcare analysis based on smart IoT and mobile edge computing: way into network study

Retraction Note: Quantum healthcare analysis based on smart IoT and mobile edge computing: way into network study

Development of the Early Childhood Health Education Program Model Using Picture Books Storytelling and IoT Smart Healthcare Devices

Development of the Early Childhood Health Education Program Model Using Picture Books Storytelling and IoT Smart Healthcare Devices

Securing fog-assisted IoT smart homes: a federated learning-based intrusion detection approach

Securing fog-assisted IoT smart homes: a federated learning-based intrusion detection approach

A Smart IoT Oil Condition Sensor for Real-Time Monitoring of Machinery Health

A Smart IoT Oil Condition Sensor for Real-Time Monitoring of Machinery Health

Development and Accuracy Evaluation of a YOLOv4-Based Food Detection Model for Smart IoT Refrigerators

Development and Accuracy Evaluation of a YOLOv4-Based Food Detection Model for Smart IoT Refrigerators

Energy-Efficient Industrial Internet of Things in Green 6G Networks

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

Efficient Lightweight Blockchain with Hybridized Consensus Algorithm for IoT Networks

Blockchain (BC) technology has attracted a lot of interest due to its excellent security and privacy features as well as its immutability. While BC can address security and privacy challenges in IoT, it is often computationally expensive, suffers from limited scalability, and introduces bandwidth overheads and delays, making it unsuitable for IoT applications. This paper proposes a novel lightweight and scalable blockchain (LIGHT-SB) designed specifically for IoT environments. In a smart IoT ecosystem, low-resource devices benefit from a centralized manager that handles communication keys and manages requests. LIGHT-SB enhances decentralization while maintaining robust privacy and security through a hybrid consensus algorithm combining permissionless proof-of-capacity and permissioned Byzantine fault tolerance (PoC-PBFT). Secure message delivery is achieved using SHA-256 and RSA cryptographic techniques. IoT device authentication, communication, and storage are safeguarded within the network. Distributed throughput management is implemented to monitor and adjust BC usage, ensuring it remains efficient. Qualitative evaluations demonstrate that LIGHT-SB effectively mitigates several security threats.

Integration and Application of Artificial Intelligence in Smart IoT and Electrical Automation

This paper explores the integrated application of artificial intelligence (AI) in smart IoT and electrical automation. It discusses the background and significance of the integration, provides a detailed analysis of the application of AI algorithms in intelligent control and optimization, and examines their role in fault diagnosis and predictive maintenance. The paper also dissects the technological innovation, industrial development, and social development opportunities brought by the integration, while highlighting the challenges of data quality and security, algorithm complexity, talent shortage, and social ethics. Finally, it concludes that we should actively address these challenges to fully leverage the advantages of integration to promote development in various fields.

Research on Security Issues of Artificial Intelligence in Smart IoT and Electrical Automation

This paper explores the application of artificial intelligence (AI) in smart IoT and electrical automation, along with the security challenges it brings. It outlines the important roles of smart IoT and electrical automation, and the opportunities and challenges that AI presents within them. The paper analyzes potential data security, algorithmic security, and system security issues that may arise from AI and proposes corresponding countermeasures. Through case studies, it demonstrates practical effectiveness and concludes by emphasizing the practical significance of researching security issues in this field and future prospects.

Komodo Dragon Mlipir Algorithm-based CNN Model for Detection of Illegal Tree Cutting in Smart IoT Forest Area

Introduction: Trees and woods are vital to preventing climate change and protecting our planet. Sadly, they are constantly being destroyed due to human activities like deforestation, fires, etc. Method: This research presents and examines an outline for using audio event categorisation to automatically detect unlawful tree-cutting activity in forests. To monitor large swaths of forest, the research team proposes using ultra-low-power, minor devices incorporating edgecomputing microcontrollers and long-range wireless communication. An efficient and accurate audio classification solution based on multi-layer perceptron (MLP) and modified convolutional neural networks (M-CNN) is projected and tailored for cutting. The Komodo Dragon Mlipir Algorithm (KDMA) is used to pick the best weight for the CNN. Result: Compared to earlier efforts, the suggested system uses a computing technique to recognise deforestation-related hazards. Various preprocessing methods have been evaluated, with special attention paid to the trade-off between classification precision and computer resources, memory, and power use. Conclusion: Additionally, there have been long-range communication trials performed in natural settings. The experimental consequences demonstrate that the suggested method can notice and apprise tree-cutting occurrences through smart IoT for efficient and lucrative forest nursing.

Hi‐Z fault location identification on low voltage distribution systems using IoT‐based digital twin model computation

AbstractDistribution feeders carry and supply power to industrial, commercial and residential loads from the point where sub‐transmission (33 kV level) ends after stepping down to suitable voltages, such as 11 kV and further down to 400/230 V. In recent times, high impedance (Hi‐Z) faults on distribution systems are creating unique challenges to utilities both from operational and safety perspectives. Most of these Hi‐Z faults occur at distribution voltages of 15 kV or below, with the problem being worse at lower voltages. Hi‐Z fault detection technologies emerged and were developed and incorporated on embedded platforms, such as relays, reclosers and sensors, which protect and monitor distribution systems. Although these technologies can detect Hi‐Z fault on feeders, most of them cannot identify the exact location of the fault. Specifically, there is no solution available in literature for detecting Hi‐Z fault location on low voltage (LV) circuits like 3‐phase 4‐wire 400 V distribution network. In this paper, we introduce a novel and a unique algorithm to identify the location of Hi‐Z faults using proposed smart metres IoT data‐based distribution system load flow and digital twin model representation of the network. Furthermore, a case study on the standard 33 bus LV system clearly depicts the functionality of the proposed algorithm.

Agricultural Field Monitoring Robot: Using IoT

Abstract: The agricultural field monitoring robot is a big step forward in precision farming. It uses technology like path memorization and smart IoT to help farmers manage their fields better. The robot can move around the farm on its own, remembering where it's been to do tasks more efficiently later. It's built on a platform-Arduino car system, which is controlled via Bluetooth. The smart IoT part comes from a platform called BlynkIOT, this system gathers data in real-time, helping farmers keep track of things like crop health and environmental conditions. With these technologies combined, the robot can do things like planting seeds, watering crops, and checking their health without needing much help from humans. Overall, it's meant to help farmers work smarter, save money on labor, and farm in a more sustainable way.

IoTDeploy: Deployment of IoT Smart Applications over the Computing Continuum

IoT Smart Applications have created a demand for architectures, infrastructure, platforms, orchestration, and service deployment strategies. They are deployed from sensors to the cloud over a geographical and computing continuum, which is challenging for service orchestration and DevOps strategies. The distributed infrastructure to implement the end-to-end data path may vary, even for similar applications, concerning the services deployed over the mist, fog, edge, and cloud stages. This paper proposes and evaluates IoTDeploy, a solution for streamlining and scaling static and dynamic IoT service deployment over the continuum. IoTDeploy implements a CI/CD tool plugin for deploying applications running on the continuum and supports dynamic service migration. We evaluated the service migration from cloud to fog and fog to cloud with a case study on smart irrigation in agriculture. The experiments reveal that deployment in IoT-distributed environments is reliable and resilient, enabling migration without interrupting the application and losing data.

A novel methodology for anomaly detection in smart home networks via Fractional Stochastic Gradient Descent

With the rising integration of IoT devices within smart home environments, securing these interconnected systems against unauthorized access and cyber threats has become increasingly critical. This study introduces a novel methodology utilizing an advanced Artificial Neural Network (ANN) frame-work to enhance attack and anomaly detection capabilities within smart home networks. The objective is to develop a robust model that outperforms traditional detection methods and provides high accuracy and low false positive rates in identifying potential security threats. The research employs a pioneering approach to train the ANN by incorporating a Fractional Stochastic Gradient Descent optimizer grounded in Grunwald–Letnikov fractional calculus. This method was chosen for its potential to refine learning processes and improve detection accuracy over standard optimizers. The evaluation of the model was performed using the DS2OS traffic traces dataset, applying precision, sensitivity (recall), and specificity metrics to assess performance. The proposed model demonstrated exceptional performance with an accuracy of 0.9951. It significantly surpassed traditional methods like Logistic Regression and Support Vector Machines. The precision achieved was high, indicating a low rate of false positives, while the sensitivity and specificity values underscore the model’s ability to identify both typical and unconventional behaviours within the network accurately. This study introduces a robust and efficient ANN-based methodology for enhancing security in smart home IoT networks. Using a fractional stochastic gradient descent optimizer has proven effective in improving the model’s accuracy and reliability in detecting anomalies and attacks. The findings suggest significant implications for the future of IoT security, highlighting the potential for broader applications of fractional calculus in machine learning to enhance cybersecurity measures in various domains.

A secure addressing mutual authentication scheme for smart IoT home network

A secure addressing mutual authentication scheme for smart IoT home network

Development of Long-Range, Low-Powered and Smart IoT Device for Detecting Illegal Logging in Forests

Development of Long-Range, Low-Powered and Smart IoT Device for Detecting Illegal Logging in Forests

Ideas development of IoT smart tracking products for XYZ company using a design thinking approach

Ideas development of IoT smart tracking products for XYZ company using a design thinking approach

Simulation of low energy building thermal energy cycle in IoT smart city planning based on environmental sensors and deep learning

With the intensification of global climate change, the reduction of building energy consumption has become an important goal of smart city development. By optimizing the thermal energy circulation system, low energy buildings can not only reduce energy consumption, but also improve living comfort. In recent years, with the help of environmental sensors and deep learning technology, the intelligent management of building heat energy cycle has become a research hotspot. The research constructs an intelligent thermal energy circulation system that integrates multiple environmental sensors for real-time monitoring of indoor and outdoor temperature, humidity and other key environmental parameters. A deep learning algorithm is used to analyze the collected data to optimize the control strategy of the thermal energy cycle. Through simulation, the energy efficiency performance of the scheme under different climatic conditions and building types was evaluated. The experimental results show that the control strategy based on environmental sensors and deep learning can significantly improve the thermal energy utilization efficiency of low-energy buildings, and the average energy consumption is greatly reduced compared with the traditional management mode. The system shows good adaptability and stability under different climate conditions. Therefore, the application of environmental sensors and deep learning technology in the thermal energy cycle of low-energy buildings can effectively promote the energy efficiency management of smart cities.

Innovative Application of Intelligent Internet of Things in Smart Cities

The Internet of Things, as an important component of the new generation of information technology system, regards the Internet as the main carrier of information, enabling all ordinary physical objects that can be independently addressed to achieve interconnectivity. This is the key to promoting urban modernization and building smart cities. This paper analyzes and studies the application of Internet of Things technology in smart cities. Based on the understanding of relevant concepts, it aims to understand the relationship between IoT technologies and smart city construction, clarify the role of Internet of Things in smart city construction, and the challenges currently faced by IoT technologies in smart city application. Therefore, taking the smart community as the starting point, the application of the smart Internet of Things in the smart community is completed through eight steps: demand analysis, planning and design, sensor and device deployment, data collection, data storage, data analysis, control and scheduling, and user application development. The experiment results show that after establishing a smart community control platform based on smart IoT technologies, the failure rate of the comprehensive information sending end, receiving end, output system, and hardware system is low, the user experience satisfaction is high, and the platform performance is strong.

Application of digital transformation in real-time bridge monitoring systems: a case study with GNSS, accelerometer and IoT solution

Abstract Digital transformation is a term that has recently become more popular in all industries such as economics, financial fields, and engineering. The development of the Internet of Things, smart sensor systems, and cloud computing is the basis for forming effective solutions in construction monitoring from cyclic monitoring and post-processing to real-time monitoring and processing; from physical storage systems to virtual systems, and connecting every component in the project. This article presents a study and application of advanced devices including GNSS, acceleration sensors and IoT solutions in bridge construction monitoring. The research aims to apply smart devices and IoT to build real-time monitoring modules for bridge projects following the digital transformation trend. The study uses a GNSS N3 Comnav receiver with a sampling interval of 1Hz, an MPU 6050 acceleration sensor with a sampling frequency of 250 Hz, and an ESP Wi-Fi module. The result of the research is a real-time wireless monitoring system that operates effectively and economically and initially processes data with GNSS and high-frequency acceleration sensors.