Intelligent IoT Research Articles

Compressive Learning Based Federated Learning for Intelligent IoT with Cloud-Edge Collaboration

Compressive Learning Based Federated Learning for Intelligent IoT with Cloud-Edge Collaboration

An Intelligent IoT Framework for Heart Diseases Prediction Using Harris Hawk Optimized GRNN

Recently, Heart diseases is considered as the one of deadliest diseases which has resulted in the increased death rates across the globe. Predicting heart diseases requires vast experiences along with advanced knowledge. IoT and AI are two emerging technologies that help in heart disease prediction. High diagnostic accuracy with minimal processing overhead, however, continues to be a design problem for researchers. To address this problem, this paper develops the Intelligent IoT structure for the better prediction of cardiac diseases employing Harris Hawk Optimized Gated Modified Recurrent Units (HHO-M-GRU). The paper also proposes the real time data collection using IoT wearable test beds which comprises of electrocardiography sensors (ECG) interfaced with MICOTT Boards ESP8266 transceivers. For later processing, the acquired data are saved on the cloud. The proposed deep learning network is utilized for evaluating the received heart data and used for predicting the heart diseases. Additionally, the suggested HHO-GRU is trained with the versatile datasets which consist of normal and abnormal stages of heart diseases. By calculating the suggested model's performance measures, including accuracy, precision, recall, specificity, and F1-score, a thorough experiment is conducted. The proposed framework was implemented in Keras libraries with Tensorflow 2.1.1 as backend. Furthermore, prediction performance and complexity overhead is compared using the other cutting-edge deep learning algorithms already in use to demonstrate the model's superiority. in predicting the heart diseases. The suggested approach beats previous models for learning with respect to of accurate prediction (99%) and minimal computing overhead, according to the results.

Exploring Intelligent IoT Networks and Enhanced Communications to Revolutionize Connectivity

Exploring Intelligent IoT Networks and Enhanced Communications to Revolutionize Connectivity

Intelligent IoT Anklets for Monitoring the Assessment of Parkinson’s Diseases

Intelligent IoT Anklets for Monitoring the Assessment of Parkinson’s Diseases

Research on the development of AloT sensing module for new energy power information collection

Abstract In order to improve the ability of power communication technology to support large-scale and massive services of smart electricity consumption, this paper designs a sensing model to collect information on new energy electricity consumption. Using the arrival rate calculation method of intelligent IoT, the information on new energy electricity users is collected, and the environmental measurement data is collected with the help of intelligent sensors. Monitoring the concurrency factor of all sensors in the operation state, collecting queuing theory packet loss rate through utilization maximization objective function, and setting the average queuing length of communication nodes. On this basis, the voltage amplitude of the electrical energy sampling point is measured, and the Gaussian perceptual loss vector with non-zero mean and heteroskedasticity is performed to iteratively converge the constraint matrix and error covariance. The results show that the maximum relative errors for service delay and packet loss rate are 16.8% and 7.0%, respectively. As bandwidth increases, the queuing delay and packet loss rate decrease. The broadband power line carrier can reach up to 210 Mbps, and the micropower wireless rate can reach 38.5 kbps. The annual cycle characteristics fluctuate widely, but the cumulative error of electricity load prediction results is lower, and the prediction accuracy is high. It shows that the electricity consumption information collection sensing model provides an important reference for building a new communication network with low cost, high speed and reliability for smart electricity consumption.

Bluetooth Mesh Network-based Intelligent Light Replenishment System for Agricultural Greenhouses

Given the actual situation of greenhouse production nowadays, this paper proposes a kind of intelligent light replenishment system for greenhouses based on a Bluetooth Mesh network. The system uses the ESP32-C3FH4 microcontroller from Espressif as Bluetooth Mesh nodes to form a Bluetooth Mesh monitoring network. This paper designs an intelligent IoT device control system based on Bluetooth Mesh network technology and cloud platform services. Through the flooded network composed of Bluetooth Mesh, the natural light intensity data monitored by each service node is sent to the cloud platform periodically through the gateway, the monitored data is accessed by the cloud platform for management, and the artificial light source module is triggered according to the deposited data to realize automatic control, realizing the modernized digital management function of agricultural greenhouses.

Developed A Hybrid Optimal Feature Vector Selection with Blockchain Technology for Smart Healthcare 4.0

The health economy has been an innovative technology since time-honored. Preserving and maintaining patient data are essential in a routine life. Patient’s medical information is very important for every individual not only for patients but also for doctors who are examining them. Advances in sensing technology, processing of data, and communication protocols have transformed the healthcare industry. Patients, physicians, hospitals, and other stakeholder may keep vital data and medical records with the use of electronic healthcare records (EHR). The goal of research should be to develop a Hybrid Optimal Feature Vector Selection with Blockchain Technology (HOFVS-BT) for smart healthcare 4.0 to improve the secure transmission of data which is supported intelligent IoT and medical detection platform possible. For Feature vector selection, proposed an Orthogonal Wolf Optimization (OWO) algorithm. Furthermore, safeguarding private patient details is taken into account by establishing an upgraded Blockchain-based IoT data security solution that not only secures the data, but also fosters trust between patients/users and healthcare service providers.

Design of intelligent manufacturing IoT sensing system for polymer process monitoring

Design of intelligent manufacturing IoT sensing system for polymer process monitoring

Retracted: Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Retracted: Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT

Intelligent medical IoT health monitoring system based on VR and wearable devices

Abstract In order to improve the shortcomings of the traditional monitoring equipment that is difficult to measure the daily physical parameters of the elderly and improve the accuracy of parameter measurement, this article designs wearable devices through the Internet of Things technology and virtual reality technology. With this device, four daily physical parameters of the elderly, such as exercise heart rate, blood pressure, plantar health, and sleep function, are measured. The feasibility of the measurement method and equipment is verified by experiments. The experimental results showed that the accuracy of the measurement method based on the reflective photoplethysmography signal was high, with the mean and difference values of the subjects’ heart rate basically lying around 0 BPM and in good agreement between the estimated heart rate and the reference value. In the blood pressure measurements, the correlation coefficient between the P r s {P}_{rs} estimate and the reference value was 0.81. The estimation accuracy of the device used in the article was high, with the highest correlation coefficient of 0.96 ± 0.02 for subjects’ heart rate at rest, and its estimation error rate was 0.02 ± 0.01. The P n t h {P}_{{n}th} value for subject B8 exceeded the threshold of 0.5 before subject B21, and subject B8 had more severe symptoms, which was consistent with the actual situation. The wearable device was able to identify the subject’s eye features and provide appropriate videos to help subjects with poor sleep quality to fall asleep. The article provides a method and device that facilitates healthcare professionals to make real-time enquiries and receive user health advice.

Studying and Realizing the Based-Intelligent IoT System for Pipeline Cathodic Protection

Cathodic protection (CP) emerges as the optimal approach for mitigating metal corrosion in pipeline networks. To ensure the continued efficiency of the CP system, monitoring the potential distribution throughout the network is crucial. In distribution networks, the pipeline potential is influenced by time-varying environmental factors, making it challenging to estimate the potential at locations away from the measurement point. This paper presents a proposed CP system for pipelines, utilizing an intelligent IoT system. The sensor nodes of the system designed based on the STM32 microcontroller perform the measurement of voltages from the reference electrodes, process the signal as well as perform wireless communication about the control stations via LoRa (Long Range Radio) communication. Besides, the control stations are designed to implement voltage compensation control through autotransformer and to communicate with the server through the GPRS communication. The objective is to deploy an affordable system capable of effectively monitoring and regulating the potential of the pipeline at a designated measurement location in industrial plants.

Design and Hardware Implementation of an Intelligent Industrial IoT Edge Device for Bearing Monitoring and Fault Diagnosis

Design and Hardware Implementation of an Intelligent Industrial IoT Edge Device for Bearing Monitoring and Fault Diagnosis

Optimal application of intelligent IoT in school sports teaching management based on resource coordination and mobile edge computing

Optimal application of intelligent IoT in school sports teaching management based on resource coordination and mobile edge computing

Industry 4.0: Marvels in Profitability in the Transport Sector

Despite the COVID-19 pandemic, the current era offers the ultimate possibility for prosperous corporate life, especially in the transport sector. Industry 4.0 covers artificial intelligence, big data, or industrial IoT, and thus spatial cognition algorithms, traffic flow prediction, autonomous vehicles, and smart sustainable mobility are not far away. The mentioned tools have already been implemented by enterprises in emerging countries. This exploration focused on transportation within the V4 region from 2016–2021. This article aims to confirm the positive sequel of applying Industry 4.0 to chosen indicators of profitability. The positive, negative, or no shift in the development of 534 businesses was based on Pettitt’s test. The Pearson chi-square test disclosed the significant dependency between Industry 4.0 and shifts in profitability ratios. Then, more than 25% of enterprises involved in Industry 4.0 had positive shifts in ROA, ROC, ROS, and ROR. The research proved not only its balanced effect but also its augmented force through the z-test of proportion. This investigation may provide multiple proofs for connected sectors with transportation to adapt the tools of Industry 4.0 and deliver the call for the governments in the V4 region to make this tool more achievable.

Ecological restoration process of watershed land space with intelligent IoT technology

Abstract Watershed ecological protection and restoration is the key link to restore the ecological service function of damaged watersheds and promote the harmonious coexistence between humans and nature. In practice, it is difficult to balance the interests of different parties in the process of watershed ecological protection and restoration in terms of water resource utilization, water environment protection, and biodiversity conservation, which makes it difficult to achieve the watershed ecological protection goals. Therefore, this study takes the Jianghuai watershed area as the research object and explores the core points of the ecological restoration of land space in the relevant experimental area in the form of algorithmic optimization frequency. The experimental data of the algorithm model show that the land types in the Jianghuai watershed are mainly woodlands and watersheds, with relatively uneven spatial distribution; a total of 85 ecological corridors were identified that showed a pattern of more in the middle and less around; and a total of 267 radiation channels were identified that showed irregular tree distribution. The empirical results show that the model has a fitting accuracy of 89%.

Smart contract based DDoS attack traceability audit mechanism in intelligent IoT

Smart contract based DDoS attack traceability audit mechanism in intelligent IoT

Guest Editorial: Evolutionary trends of intelligent IoT networking for commercial and industrial use cases

Guest Editorial: Evolutionary trends of intelligent IoT networking for commercial and industrial use cases

Intelligent IoT Platform for Multiple PV Plant Monitoring.

Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV plants on a single platform is required to satisfy the current industrial demand for monitoring multiple PV plants on a single platform. This work proposes a method to perform multiple PV plant monitoring using an IoT platform. Next-day power generation prediction and real-time anomaly detection are also proposed to enhance the developed IoT platform. From the results, an IoT platform is realized to monitor multiple PV plants, where the next day's power generation prediction is made using five types of AI models, and an adaptive threshold isolation forest is utilized to perform sensor anomaly detection in each PV plant. Among five developed AI models for power generation prediction, BiLSTM became the best model with the best MSE, MAPE, MAE, and R2 values of 0.0072, 0.1982, 0.0542, and 0.9664, respectively. Meanwhile, the proposed adaptive threshold isolation forest achieves the best performance when detecting anomalies in the sensor of the PV plant, with the highest precision of 0.9517.

Addressing the IoT Schemes for Securing the Modern Healthcare Systems with Block chain Neural Networks

This paper provides a wide-range of literature review of various IOT with AI based enabling wearable technologies and protocols used for medical (IoT) with a taught of examining the present and future smart health care technologies. Despite recent advances in medical systems, biomedical hardware, the growth of IoT in medicine continues to advance in terms of biomedical hardware, monitoring figures like cancer patient data disease indicators, temperature levels, oxygen levels, and glucose levels. In the near future, medical IoT is expected to replace the old traditional healthcare systems to smart Ai-IoT based healthcare systems. In our paper we provided a theoretical approach of the most relevant protocols and wearable technologies used for the IoT health care medical systems. We also provided a proposed smart AI based intelligent IoT frameworks for hospital systems settings.

Computed Tomography and 3-D Face Scan Fusion for IoT-Based Diagnostic Solutions

In clinical diagnosis, multimodal medical image fusion is meaningful and necessary, for the reason that some diseases need to be diagnosed in combination with the situation of different tissues of patients. Spiral computed tomography realizes the high-precision and smooth reconstruction of bone tissue, while it can not represent the color and texture information in soft tissue reconstruction with high accuracy. The face scan precisely records the color and shape of the maxillofacial region. The diagnosis of some diseases (like cavernous hemangioma and jaw deformity caused by idiopathic condylar resorption) needs to combine the information of maxillofacial soft tissue and bone, so it is of great significance to fuse spiral CT and face scan images. In this paper, a novel intelligent IoT scene is proposed: a multimodal medical images acquisition and fusion system, and by combining CT machine and face scan equipment, the CT and face scan of patients can be synchronously collected. Deep point neural networks are used to extract feature points and a threshold iterative closest point algorithm performing registration with deep feature points and contributed region segmentation is applied. Finally, high-precision fused modal data is output at the mobile terminal to facilitate diagnosis and analysis and improve the efficiency of doctor-patient communication. Quantitative experiments show promising results, and clinical experiments prove that our method enables patients and doctors to better understand the state of an illness and improves the efficiency of doctor-patient communication.