Internet Of Things Cloud Platform Research Articles

IoT Based Health Monitoring System

Abstract: This paper presents a comprehensive system designed for continuous monitoring of patients' physiological parameters using Internet of Things (IoT) technology. In recent times, patient monitoring systems have gained significant traction among researchers and caregivers alike. The proposed system is adept at monitoring key physiological indicators such as pulse rate, body temperature, and heart rate from the patient's body. Data collected by the system is transmitted to an IoT cloud platform via a Wi-Fi module, where the patient's health status is securely stored. This setup allows medical professionals or authorized individuals to remotely monitor the patient's condition in real-time through the cloud server. The primary aim of this research is to provide efficient and effective healthcare facilities to patients, thereby enhancing overall patient care.

Navigating the Landscape of IoT, Distributed Cloud Computing: A Comprehensive Review

This comprehensive academic exploration delves into the revolutionary convergence of the Internet of Things (IoT) with distributed cloud computing, redefining the realms of data processing, storage, and communication. The paper critically analyzes scholarly work from reputable journals, providing profound insights into this integration's multifaceted applications and underlying technological frameworks. The relevance of IoT, a network of interconnected devices and sensors, is emphasized through its significant impact on diverse sectors, including healthcare, education, agriculture, and smart cities. This impact is magnified by its extensive data collection, processing, and analysis capabilities, enabled through cloud computing platforms. The objective of the paper is to methodically compare and contrast contemporary scholarly contributions, shedding light on the diverse applications and technological infrastructures of IoT in conjunction with distributed cloud computing. This endeavor encompasses an examination of IoT-based cloud infrastructure, detailed analysis of specific needs, implementations, and applications of IoT-based cloud computing, and a review of various IoT cloud platforms. The paper also highlights the benefits of integrating IoT with cloud computing, elucidating significant advantages and potential future directions of this technology. Through this scholarly inquiry, the paper aims to offer an in-depth perspective on the state-of-the-art developments in IoT and distributed cloud computing. It underscores their significance and potential in shaping the future of digital technology and its applications across various domains.

Incorporating the Internet of Things (IoT) Learning Module into the Smart Building Course

The utilization of Internet of Things (IoT) devices is increasing very rapidly, creating a demand for skilled professionals who can navigate the complexities of this technology. It is a crucial educational challenge to prepare undergraduates to become contributors in this emerging field. The curriculum needs to be updated to offer students both theoretical learning and practical application so they can benefit from it. This paper introduces a comprehensive IoT learning module to the final-year students of civil engineering in an elective course, Smart Building. The proposed learning module includes an introduction to IoT concepts and applications, IoT devices, communication protocols, cloud platforms, user interfaces, and IoT risk management and security, all of which can be easily integrated into existing courses. Results from the module are presented using direct and indirect assessments, including assignments, hands-on practical, examinations, and surveys. These assessments are designed to evaluate the students’ understanding and the challenges they encountered in their IoT learning process. The findings indicate that a significant majority, surpassing 90% of the students, have demonstrated understanding of a minimum of three essential components of the fundamental IoT architecture, despite their limited background knowledge at the beginning of the course. Additionally, this learning module can serve as a valuable resource for other educators who intend to deliver IoT-related courses.

Enhanced slime mould optimization with convolutional BLSTM autoencoder based malware classification in intelligent systems

AbstractAutonomous intelligent systems are artificial intelligence (AI) tools that act autonomously without direct human supervision. Cloud computing (CC) and Internet of Things (IoT) technologies find it challenging to deploy sufficient security defences because of the different structures, storage, and limited computing capabilities that make them more vulnerable to attacks. Security threats against IoT structures, devices, and applications are increasing with the demand for IoT technology. The training data available to AI models may be limited, which could impact their performance and generalizability. Adopting AI solutions in real‐world situations may be impeded by compatibility concerns and the requirement for flawless integration. Malware classification errors can occur due to a lack of contextual knowledge, particularly in cases where benign files behave identically to malicious. Various studies were carried out on detecting IoT malware to evade the menaces posed by malicious code. However, prevailing techniques of IoT malware classification supported particular platforms or demanded complicated methods for attaining higher accuracy. This study introduces an enhanced slime mould optimization with a convolutional BLSTM autoencoder‐based malware classification (ESMO‐CBLSTMAE) system in the IoT cloud platform. The projected ESMO‐CBLSTMAE system focuses on detecting and classifying malware in the IoT cloud platform. To achieve that, the ESMO‐CBLSTMAE algorithm employs a min–max normalization technique for scaling the input dataset. The ESMO‐CBLSTMAE method uses a convolutional bidirectional long short‐term memory autoencoder (CBLSTM‐AE) model for the malware detection process. Lastly, the ESMO method is executed for the optimum hyperparameter tuning of the CBLSTM‐AE technique, which boosts the malware classification results. The experimental analysis of the ESMO‐CBLSTMAE method is tested against a benchmark database, and the outcomes portray the greater efficacy of the ESMO‐CBLSTMAE approach over other existing techniques. The proposed malware classification model achieved an accuracy of 98.57 and F Score of 80.77 and outperformed the existing models.

BUDGET-BASED SMART CITY IMPLEMENTATION FOR TIER III CITIES

In the present day's world the Internet of Things (IoT) is gaining popularity as a field of research in various engineering applications. IoT includes various elements like communication technologies, communication protocols, identification technologies, sensing, computation, and actuation. Today, almost every home is equipped with smart IoT home appliances. As cities grow and expand, smart and innovative solutions are crucial for improving productivity, increasing operational efficiencies, reducing management costs, and enhancing the lifestyle of human beings. A smart city consists of a number of applications. In this paper, budget-based implementation of smart cities with specific applications like smart parking, smart dustbins, smart streetlights, and smart traffic monitoring and management systems are done using various IoT boards, various sensors, actuators, and various IoT cloud platforms. In the smart city application, several heterogeneous devices are connected through the internet for communication, and huge amounts of data are collected from various applications and stored in databases. Data are classified, analyzed, and visualized on both web and mobile dashboards.

IOT-BASED SMART SALINE LEVEL AND HEALTH MONITORING SYSTEM

The many uses of saline solution include as providing temporary fluids when there is under or no hydration among the trauma cases. In addition, they participate in the control of salt and the levels of electrolytes in the body. To maintain the level of a saline bottle, hospital nurses or aides prime responsibility is to check it on a regular basis. The failure to replace them in time may result in a reversal of blood flow, compromising the patient’s life. An Internet of Things (IoT)-based Smart Saline Level was developed to solve this issue and to provide an automated and efficient solution for remote monitoring. The Proposed system employs Internet of Things (IoT) technology, integrating NodeMCU, a load cell amplifier (HX711), Arduino UNO, Pulse oximeter (MAX 30100) sensor, DHT11 Sensor and a voltage regulator(L7805). The load cell, in combination with the HX711 amplifier, accurately measures the weight of the saline bottle. MAX30100 sensor measures Heart Rate & Oxygen saturation (SPo2) levels in blood. DHT11 Sensor detects the Humidity and Temperature levels. All the data is transmitted to the Arduino UNO, which serves as a master, the NodeMCU facilitates wireless communication, relaying the collected data from the Arduino UNO to the designated IoT cloud platform for remote monitoring.

IoT Enabled Boat for Cleaning Water Surface

This project introduces an IoT-enabled boat designed for the purpose of cleaning water surfaces. The system utilizes two gear motors to drive a conveyor belt for efficient collection of floating debris. An Arduino Uno, in conjunction with two IR sensors, provides precise control over the conveyor system, ensuring effective garbage collection. Additionally, the boat's overall movement is managed through a controller employing two gear motors and an Arduino IoT Cloud platform. The conveyor system, powered by the gear motors, facilitates the seamless collection of debris present on the water surface. The Arduino Uno, acting as the central processing unit, interprets signals from two IR sensors strategically placed to optimize the efficiency of the garbage collection process. This intelligent control system allows for the autonomous navigation of the boat and precise maneuvering around obstacles. Key Words: IoT (Internet of Things), Water Surface Cleaning, Gear Motors, Conveyor System, Arduino Uno, IR Sensors, Boat Controller, Arduino IoT Cloud, Garbage Collection, Autonomous Navigation, Remote Monitoring, Environmental Cleanup, Sustainability, Pollution Control, Scalable Solution, Real-time Feedback, Smart Technologies, Automation, Water Pollution, Adaptive Systems.

Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival

Aquaculture involves cultivating various marine and freshwater aquatic creatures within regulated environments. Monitoring the aquatic environmental conditions in real-time is crucial for successful fish farming. The Internet of Things (IoT) offers significant potential for real-time monitoring, and this paper introduces an IoT framework designed for efficient monitoring and effective control of various water-related aquatic environmental parameters. The proposed system is implemented as an embedded system utilizing sensors and an Arduino microcontroller. In cultivating pond water, diverse sensors such as pH, temperature, and turbidity sensors are deployed, with each sensor connected to an Arduino Uno-based microcontroller board. These sensors collect data from the water, which is then stored as a CSV file in an IoT cloud platform called ThingSpeak through the Arduino microcontroller. To gather data for analysis, we conducted measurements across five ponds, varying in size and environmental conditions. After getting the real-time data, we compared our experimental results with the standard reference values. As a result, we could take the decision of whether a pond is suitable for cultivating fish or not. After that, we labeled the data with 11 fish categories: Katla, sing, prawn, shrimp, rui, tilapia, pangas, karpio, magur, silver carp, and koi. The data was analyzed using 10 machine learning (ML) algorithms, including J48, Random Forest, K Nearest Neighbors (K-NN), K*, Logistic Model Tree (LMT), Reduced Error Pruning Tree (REPTree), Jumping Rule Inference with Pruned Search (JRIP), Partial Decision Trees (PART), Decision Table, and Logit boost. After experimental analyses, it was discovered that only three of the five ponds were ideal for fish farming, and those three ponds only met the required standards for pH, Temperature, Turbidity, and Conductivity. Among the state-of-art machine learning algorithms, Random Forest achieved the highest score of performance metrics as accuracy 94.42%, kappa statistics 93.5%, and Avg. TP Rate 94.4%. In addition, we calculated the Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO) for one scenario. This study includes prototype hardware details of the proposed IoT system.

CRSExtractor: Automated configuration option read sites extraction towards IoT cloud infrastructure

There are a large number of solutions for big data processing in the Internet of Things (IoT) environments, among which the IoT cloud infrastructure is one of the most mature solutions. Typically, modern IoT cloud infrastructures have different kinds of configuration options. The diversity of configurations leads to frequent software configuration errors. Generally, troubleshooting configuration errors relies on finding the mapping relationship between configuration options in the documents (e.g., official manuals) and their read sites in the source code. Most current works still manually extract configuration read sites. Automated methods are not always interchangeable and they incur considerable time overheads and low extraction rates.In this paper, we propose CRSExtractor, an automatic technique for extracting configuration read sites based on intra-procedural analysis. Using our technique, configuration option read sites can be automatically identified and built into maps with configuration options. Evaluations on several core software systems of IoT cloud platforms, such as Hadoop and Cassandra, show that our approach performs well, with an accuracy rate of over 90% and efficiency nearly 20 times faster than previous works.

Development of a distributed MR-IoT method for operations and maintenance of underground pipeline network

The underground pipeline network (UPN) is an essential infrastructure and plays an irreplaceable role in national defense and urban activities. The complexity of structural environment and management makes its operation and maintenance difficult. To solve this problem, a distributed mixed reality (MR) and internet of things (IoT) system is developed through game thinking. Firstly, digital models are created based on design drawings and real-world environments, and then an MR system for the UPN is built by the game engine and the OpenXR platform. Secondly, an IoT cloud platform is built to connect with the MR system based on the API sets and cloud services; the data communication between sensors and MR devices is linked with the Socket method, and the data filtering model is constructed by the Kalman algorithm to realize the information exchange between the field workers and the backend managers. Finally, the National Center for Materials Service Safety at the University of Science and Technology Beijing (NCMS_USTB) is used as the experimental site to test this system, and its underground sewage and rainwater pipeline network are used to simulate the key problems in the operation and maintenance. The effect of the application shows that there is potential technical complementarity between the MR and IoT, and the distributed MR-IoT approach can be used as a new technical reference for the operation and maintenance of the UPN.

Internet of Things (IoT) for Soil Moisture Detection Using Time Series Model

Technology in agriculture has been widely and massively applied. One of them is automation technology and the use of big data through the Internet of Things (IoT). The use of IoT allows a process to run automatically without human intervention. Extreme weather changes and narrow land use are one of the main problems in agriculture. The development of IoT devices has been widely developed regarding this subject. One of them is a soil moisture detection system. This study aims to build an IoT soil moisture detection system. The system will use a sensor as input which is then processed in a microcontroller device and the prediction results are sent to the IoT cloud platform. Prediction results are obtained using a time series model and then its performance is evaluated using RMSE. This model was chosen because the structure of the observed soil moisture data is based on time. The results of this study indicate that the soil moisture IoT system can work well. This is supported by the results of the prediction evaluation value of the RMSE = 1.175682x10-5 model which is very small.

Intelligent home control system based on BP neural network speech recognition

Abstract With the rapid progress and wide application of cutting-edge technologies such as automatic control technology and wireless communication technology in recent years, smart home, as an effective combination of these cutting-edge technologies and daily life, has received more and more attention and Research has been greatly developed. In order to solve the shortcomings of the existing home control need to download various cumbersome APP, the WeChat applet is introduced into the smart home system, and the intelligent voice technology is planned to be used in the smart home control to realize the voice control, improve the user experience and make the home Life gets smarter. This time, the deep learning technology was used in the development process of the smart home control system, and according to the historical information of the home, combined with the learning ability of the recurrent neural network for time series data, through the mining, analysis and learning of historical data, the construction based on specific The user’s unique computing model can seamlessly connect the capabilities of the voice cloud platform with the capabilities of the Internet of Things cloud platform through the Web server, making it possible to quickly access the voice recognition capabilities to smart homes. The entire system has stable connectivity, easy deployment and low cost. The main functions are deployed in the cloud and extended. After 10 rounds of iterative training of the Attention-GRU model in this paper, its prediction accuracy can quickly rise to about 97%, and finally stabilize at about 98.2%, and the lighting prediction accuracy can reach 95% or higher.

Intelligent lighting control system based on Internet of things technology

With the rapid development of the economy[1], the number of cars in our country continues to increase, underground parking garages have become commercial centers, supporting facilities in large residential areas, the number of underground garages is increasing, and the energy consumption of underground garage lighting systems is also rising. This question proposes an intelligent lighting control system based on the Internet of Things technology, studies the wifi protocol, wireless mesh networking technology, the Internet of Things cloud platform and other technologies. Based on the ESP32 chip, the terminal lighting control node is designed and developed, which cooperates with the sensor to realize the detection of personnel movement and light intensity information. The terminal lighting node controls the brightness and switch of the lamps according to this information, and realizes the effect of turning on the lights when the car arrives and turning off the lights when the car leaves, so as to achieve energy-saving control purpose.

Effect of Pulmonary Rehabilitation Nursing on Elderly Patients with Stable Chronic Obstructive Pulmonary Disease by Internet of Things Cloud Platforms

Effect of Pulmonary Rehabilitation Nursing on Elderly Patients with Stable Chronic Obstructive Pulmonary Disease by Internet of Things Cloud Platforms

Internet of Things Middleware: Evaluating the Costs of Service-Based Solutions Under Different Application Scenarios

The rapid evolution of technology has paved the way for the many applications of the Internet of Things (IoT). As IoT systems become increasingly complex, new technologies that simplify the development of the IoT are emerging, for example, IoT cloud platforms have gained popularity as tools that reduce development time and cut costs. Recent surveys indicate that only a few IoT projects have had successful outcomes. Among the main reasons for this, there is the lack of knowledge regarding IoT design and architecture. Numerous research studies have tried to address the problem of choosing the right cloud platform based on specific requirements, but none of the reviewed studies has proposed a cost-benefit analysis of the current available cloud solutions for the IoT. Here, the IoT has been studied from a decision-maker’s perspective, giving a brief but concise introduction to modern IoT architecture and IoT middleware and evaluating the costs of four popular IoT cloud middleware solutions under different project requirements and design scenarios.

Over-the-air firmware update for IoT devices on the wild

Internet of Things (IoT) has been gaining a lot of attention in the last few years and despite the large amount of investments, there is still much to be developed and refined. Software and firmware updates over-the-air (OTA) are a crucial part for IoT solutions longevity. This paper presents the design and implementation of an OTA firmware update method based on the standard architecture recently proposed by the Internet Engineering Task Force (IETF). The proposed solution is evaluated through a testbed using 20 constrained IoT devices and an open source IoT cloud platform, as well as on a set of devices deployed in a real world application. Results show that the proposed solution is suitable for constrained devices and has little impact on the network traffic.

IoT Smart Agriculture and Agricultural Product Income Insurance Participant Behavior Based on Fuzzy Neural Network

With the continuous development of modern science, more and more attention is paid to the application of science and technology for agricultural production. First of all, this article analyzes the development trend of modern agriculture, and designs the system through an overall plan of four parts: automatic acquisition, terminal control, network transmission, and cloud platform and terminal application. Taking automatic irrigation system and temperature automatic control system as examples, design an automatic control algorithm based on fuzzy neural network, and obtain the best control strategy through fuzzy inference and neural network training. Then, this paper studies the reliability of “price setting” in agricultural income insurance. This article takes the main crops of a certain province as an example to verify and analyze the risks of farmers' income levels from the viewpoint of corn yield risks and price risks, and advocate the necessity and feasibility of agricultural income insurance. Finally, from the perspective of different participants, this article analyzes the impact of insurance premium subsidies on the enthusiasm of agricultural producers to participate in insurance, the impact of insurance companies on the improvement of insurance operation efficiency in the planting industry, and the stability of the government's promotion of agricultural production. It also influences to design a set of IoT automatic control system for intelligent agriculture. The system realizes the acquisition and remote control of agricultural data by establishing an Internet of Things cloud platform, and uses automatic control algorithms based on fuzzy neural networks to realize automatic water-saving irrigation, automatic temperature control, and other functions. Based on this research, the main actions of joining agricultural income insurance and the participation model based on the perspective of tripartite participants.

River Water Pollution Prevention and Monitoring System Based on Internet of Things Cloud Platform

River Water Pollution Prevention and Monitoring System Based on Internet of Things Cloud Platform

Three-Dimensional Measuring Device and Method of Underground Displacement Based on Double Mutual Inductance Voltage Contour Method.

Landslide is a very common and destructive geo-hazard, and displacement monitoring of it is integral for risk assessment and engineering prevention. Given the shortcomings of current landslide displacement monitor technologies, a new three-dimensional underground displacement monitoring technology is proposed based on the double mutual inductance voltage contour method. The underground displacement measuring device mainly consists of an information processing unit and sensing array, connected by power and RS-485 communication lines. An underground displacement measurement model to convert the double mutual inductance voltages and the inter-axis angle into the relative displacement between adjacent sensing units is established based on the interval-interpolation and contour-modeling. Under the control of the information processing unit, the relative displacement between any two adjacent sensing units can be calculated through the underground displacement measurement model, so as to obtain the total displacement from underground depth to surface, and the measurement data can be further sent to the Internet of things cloud platform through the 4G module; thus the remote real-time monitoring of underground displacement three-dimensional measurement for the rock and soil mass from underground depth to the surface is realized. The measurement model is verified by building an experimental platform to simulate the underground displacement of rock and soil mass. The experimental results show that for each measuring unit, when the horizontal displacement and vertical displacement are within the measurement range of 0–50 mm, the maximum measurement error will not exceed 1 mm, which can meet the accuracy requirements of underground displacement monitoring of landslide.

A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing

Abstract An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants.