IoT Cloud Research Articles

Remote Fault Diagnosis for the Powertrain System of Fuel Cell Vehicles Based on Random Forest Optimized with a Genetic Algorithm.

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.

Portable ECG monitoring system with IOT analysis

This system is designed to calculate the heart rate of patients and transmit the data in beats per minute (bpm) to a cloud database. By using this system, doctors can analyze critical parameters remotely. The project involves interfacing the AD8232 ECG Sensor with the NodeMCU ESP8266 Board to monitor ECG waveforms on a Serial Plotter Screen and transmit the data to an IoT cloud platform for real-time monitoring. This innovation allows for remote monitoring of heart activity, reducing the need for hospital stays. Utilizing the Blynk IoT platform, the system ensures online access via PC or Smartphone, representing a significant advancement in patient health monitoring. This approach aims to improve early detection and prevention of heart diseases, enhancing patient care and reducing fatalities.

Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model

Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model

Retracted: IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

Retracted: IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

Modeling a Smart IoT Device for Monitoring Indoor and Outdoor Atmospheric Pollution

Air pollution is caused by chemical, physical, or biological components alters the fundamental properties of the atmosphere, and contaminates the interior or outdoor settings. With the rapid industrialization activity in recent years, there is an urgent need to monitor air quality. The proposed research provides a mechanism for monitoring air pollution in indoor and outdoor environments. The system consists of an IoT-based Arduino device. The Arduino IDE connects the Temperature and Humidity sensor, Grove light sensor, and air quality sensor to measure the air pollutants such as Carbon dioxide CO2, Nitrogen oxide (NOx), and Particulate Matter PM2.5. The sensors work efficiently and provide qualitative findings from the environment when they are exposed to CO2, gasoline, solvents, thinner, formaldehyde, and other harmful chemicals. The Wi-Fi module of the Blues Wireless Notehub is used for secure data routing to the IoT cloud. The Air Quality Index (AQI) measures provide information on whether there is something unsafe in indoor and outdoor environments. For collecting and analyzing the device data, the Notecard is intended to be used with a cloud storage service. Also, the indoor fire detector identifies the incident of fire intimates the users through the alarm, and measures the indoor pollutant at that time. The proposed smart IoT product model would be an excellent device for air quality monitoring because of its long-term consistency and low electricity consumption.

Design and Development of a University Outdoor Air Quality Monitoring System

An increase in air quality monitoring research using low-cost air quality sensors has been observed. Existing air quality monitoring systems provide real-time data on at most five primary pollutants, cover a short area, and could be made expandable. An air quality monitoring system utilizing a scalable, low power wide area network, centralized system, and low cost sensors is proposed. The nodes gather data on air pollutants and environmental factors. Data conversion, storage, and calculation of air quality indices were processed on the central subsystem and visualization through IoT cloud. The results suggest improvement in the design and integration of prediction models, battery, and power management techniques.

Цифровизация АПК: оценка и перспективы внедрения в аграрном секторе экономики страны

Digital technologies in agriculture are relevant for managing production processes, data processing, monitoring and analyzing soil and plant conditions, and ensuring the quality and safety of products. To assess the level of digitalization and readiness for implementing digital technologies in agriculture, the authors conducted an analysis of agricultural producers in 24 regions from 8 Federal Districts. Primary data for assessing the level of digitalization were obtained from federal and regional agencies. Six indicators were considered: compatibility of machinery, equipment, and devices with digital infrastructure; number of digitized agricultural lands; number of employees who have undergone digital profile training; number of integration points with IoT devices, equipment, and cloud services; coverage area of agricultural enterprise areas by high-speed networks; number of agricultural machinery with IT equipment. As a result of the research, recommendations were given for the regions of the Russian Federation with different levels of digitalization. For effective implementation of digital technologies, regions with low levels of digitalization should actively attract and effectively implement state support to acquire modern equipment and improve digital competences. Regions with high levels of digitalization should focus on workforce training and stimulate investment provision. Regions with average levels of digitalization should concentrate on improving technical infrastructure and staff training. Further development of digitalization in agriculture requires proper financing, qualified personnel competent in IT technologies, domestic solutions for digital services and platforms, and the elimination of uneven distribution of IT resources among the regions of the Russian Federation.

Real-time Soldier Health Monitoring System Using IoT and Blynk Cloud

Real-time Soldier Health Monitoring System Using IoT and Blynk Cloud

Evolution of Geophysical Techniques over the last twenty years: Commercialization, societal impact, and research and development perspectives

Geophysics plays a crucial role in critical sectors such as energy, mineral resources, water, the environment, and waste and natural risk management. Geophysicists navigate a constantly evolving technological landscape, leveraging innovations ranging from cloud computing, robotics, IoT, and numerical simulation to artificial intelligence. As key players at the intersection of these advancements, geophysicists must meet the needs of both industries and an increasingly engaged public, providing a fertile ground for research, development, and entrepreneurship. The analysis of geophysicists' testimonies, also involved in entrepreneurship, highlights the major trends of the last two decades and offers insights into the future developments of this discipline.

Optimization of Network Management Resource in Internet Exchange Point (IXP) Implementation: Case Study of PT. Cloud Hosting Indonesia

Internet Exchange Point (IXP) is vital for global network development, supporting cloud computing and IoT trends. However, managing IXP networks is increasingly complex, impeding internet network growth. This research optimizes IXP network management by utilizing IXP Manager as the central infrastructure. Qualitative methods reveal limitations in BIRD configuration, confined to the Chief Technology Officer without a management platform. IXP Manager enables automation, empowering all technicians with performance graphs for evaluation, centralized management, improved employee performance, and enhanced device functionality. This approach fosters cost savings and supports the expansion of the internet network.

Design of Cloud Scheduling Simulation System for IoT Resources Based on Digital Intelligent Environment

With the popularization of intelligent devices and the development of internet technology, the Internet of Things technology is receiving increasing attention. However, how to effectively schedule and manage resources in the Internet of Things environment has become an increasingly important issue. In order to solve this problem, a cloud scheduling simulation system for IoT resources in a digital intelligent environment has emerged. This system utilizes cloud computing and IoT technology to simulate the behavior of intelligent devices and deep learning technology, achieving efficient resource scheduling. In addition, experimental data shows that the accuracy of the IoT resource cloud scheduling simulation system based on deep learning algorithm combined with digital intelligent environment is over 70%; the average task processing time is within 0.3 seconds; the average task response time is within 0.6 seconds. From this, it can be seen that combining deep learning algorithms with the system can quickly respond and solve problems, improving the reliability and stability of the system. The application range of this system is very wide, which can be used to optimize resource scheduling in many fields such as production, and can also be applied in fields such as smart home, smart medical, and intelligent transportation. With the continuous development of technology, this system would also be further improved and expanded to better serve people's lives and work.

Design of an Integrated Method for Blockchain-Based Secure Healthcare Cloud IoT Using Federated Learning and Homomorphic Operations

Design of an Integrated Method for Blockchain-Based Secure Healthcare Cloud IoT Using Federated Learning and Homomorphic Operations

Blockchain-Aided Anonymous Traceable and Revocable Access Control Scheme With Dynamic Policy Updating for the Cloud IoT

The Internet of Things has been applied in various fields of industry, which has promoted the intelligent development of the industry and improved efficiency in industrial production. The devices involved in the IoT have generated useful and sensitive data over time and upload the data to the cloud to realize real-time data sharing. To ensure the confidentiality of data, many systems use attribute-based encryption primitive to encrypt data. However, there are still some security and privacy problems in this mode, such as the lack of identification of malicious users who leaked private keys, performance bottleneck caused by excessive reliance on a single central authority, and vulnerability because a single central authority holds the private keys of all users in the system. In this paper, white-box tracking is used to identify malicious users. The alliance chain is introduced to support multi-authority environments, where the consensus nodes are managed by different authorities and assist central authority in generating partial private keys. To protect users’ privacy, users remain anonymous at all times during their interactions with blockchain consensus. The security analysis and simulation results show that the proposed scheme outperformed other comparable schemes, indicating that it is a preferable scheme.

Development of an IoT Cloud Control System for Managing Pressurized Gas Containers

Automatic remote management of gas cylinders is an important task for gas companies, especially if it saves time and money on replacement and maintenance. The system presented here can be used in various industries that require monitoring of cylinders containing gas, such as industrial, medical, and food. The main advantage offered is the ability to arrange for cylinder replacement in advance to speed up, minimize downtime, and facilitate logistics. Monitoring uses a microcontroller from the Arduino family, which can continuously detect the state inside the cylinder via a pressure transducer. The customer can monitor the system by viewing the cylinder's status locally or through an online dashboard. In addition, if the level inside the cylinder becomes critical, the system turns on a warning LED light and sends an Alert message to the Cloud containing location information. The alert message was generated in the AWS IoT Cloud environment using the MQTT protocol and will be received on a mobile device owned by the cylinder replacement operator. Finally, an energy analysis has been carried out to evaluate the autonomy characteristics of the device.

Analysis of Smart Technologies Used in Smart Hotels

Purpose –The development and widespread use of smart technologies have led to the emergence of smart hotels. Smart hotels are smart systems that provide benefits to all stakeholders (staff, managers, customers, hotel owners) in the hospitality sector with smart technologies and interact with each other. Hotels that utilize smart technologies differentiate the hotel service, provide superiority in competition, and reduce hotel costs. This study aims to analyze the hotels in the world where smart technologies are used and the type, purpose of use, and customer touchpoints of the smart technologies used in these hotels and to provide a perspective and foresight for hotels.Design/methodology/approach –The data used in the research were obtained from secondary sources. The document analysis method, one of the qualitative research methods, was used to analyze the sources. The content analysis technique was used to analyze the data. A seven-step systematic review process was applied in the research. The seven-step review process consists of the following stages: (1) determining the review objectives and formulating the research questions; (2) determining the search terms and selection criteria; (3) searching for smart hotel applications before clarifying the exclusion and inclusion criteria; (4) evaluating the quality and suitability of smart hotel applications; (5) determining the content analysis review variables (6) conducting content analysis, and (7) synthesizing and reporting the findings. The population of the research consists of all hotels where smart technologies are used. In the research, 1084 smart hotels were determined as a sample. The data was collected via the internet between 1 December 2022 and 20 June 2023.Findings –The most used technologies in hotels are cloud computing, artificial intelligence, big data, IoT, NFC, and recognition technologies. Blockchain, mixed reality, metaverse, and 3D printers are the least used technologies. The most common use of smart technologies is at the first, second, third, and fourth customer touchpoints. While the majority of hotels use 6 or more types of smart technologies, the second place is occupied by hotels using between 2 and 5 types of smart technologies. Smart technologies are most often used in customer-facing services and transactions.Discussion –Providing a personalized experience and responding quickly to their guests' needs is a common goal for all smart hotels. Some hotels provide their guests with IoT-enabled tablets to control all the functions offered, while others have mobile applications downloaded from Play stores. Some hotels are investing more in AI and robotics technology for butler, housekeeper, and doorman services, while others are using AI, big data, and IoT to provide personalized services to customers. In the future,smart hotel rooms are expected to be different from today, and even unmanned hotels are expected to appear more

An Automatic Internet of Things-Based System for Rabbit Cage

Rabbits, low-maintenance mammals in terms of cost and space requirements, require meticulous care, encompassing disease control, feeding, and cage maintenance. To address these concerns, an automated system for feeding, drinking, temperature control, and monitoring rabbit manure gas levels within the cage was developed, all remotely accessible. The system comprises ultrasonic sensors, DHT11 sensors, MQ-135 gas sensors, a real-time clock (RTC), an Arduino Mega 2560 with built-in Wi-Fi, relays, servo motors, mini water pumps, mini fans, and a heat lamp. The feeding and drinking functions are automated, triggered by RTC sensor data or can be manually controlled through the Arduino IoT Cloud dashboard. Temperature regulation is managed based on data from the DHT11 sensor, and gas levels in the rabbit manure are monitored using the MQ-135 gas sensor. Conducting 30 tests for each primary function, including automatic and manual feeding/drinking, temperature control, and disinfectant spraying, these functions performed as designed. An exception occurred three times when the DHT11 microcontroller sensors lost connection, rendering the input from these sensors unusable. To address this issue, the addition of an extra voltage supply to the Arduino Mega 2560 microcontroller is proposed, mitigating this vulnerability.

Nonlinear Time Series Analysis for Anomaly Detection in Cybersecurity using Cloud IoT

The CloudIoT is an extensive network that hosts several IoT gadgets and apps. This comprises the servers and the storage space required for real-time processing and operations. For commercial purposes, data gathered by IoT devices may be safely saved and handled on the cloud. CloudIoT allows for the scalable distribution of software. IoT devices capture information using built-in sensors and monitors. After collection, it is sent to a central location where data from other Internet of Things devices is kept. Cloud storage offers a scalable, cost-effective alternative for maintaining and administering IoT data—the capacity to grow or change quickly. More and more Internet of Things devices means a greater need for data storage and processing capacity. The cloud computing solution is scalable and adaptable enough to meet these shifting requirements. Hence, this paper proposes a nonlinear time series analysis for enhancing anomaly detection in cybersecurity based on Cloud-Internet of Things (CloudIoT-CS). Statistics like precision and positive and negative results are considered part of assessing the suggested methodology by comparing it against conventional anomaly detection techniques. The study also looks at the system's resistance to hostile assaults, guaranteeing its efficacy in practical cybersecurity settings. This study's findings provide a holistic and flexible approach that accounts for the unique challenges posed by CloudIoT settings. As well as improving the precision of anomaly identification, integrating nonlinear time series evaluation with CloudIoT creates a scalable and robust framework for protecting vital systems from ever-evolving cyber threats.

Securing Iot Data Transmission: A Comprehensive Approach Integrating Two-Fish Encryption With Wireless Smart Energy Systems And Iot Cloud Services

This work addresses the challenges posed by the interconnected nature of the Internet of Things (IoT). This paradigm, facilitating the seamless connectivity of diverse devices, gives rise to issues such as addressability, confidentiality, and data management. The proposed solution advocates for the integration of the robust Twofish encryption algorithm into wireless smart energy systems, creating a holistic approach that capitalizes on the capabilities of IoT cloud services. Twofish, recognized for its strong encryption capabilities, becomes an integral part of the IoT framework to ensure the security of communication and data confidentiality. Through the amalgamation of Twofish encryption with wireless smart energy systems and IoT cloud services, the approach provides a comprehensive solution for the secure transmission and management of data. This integration enhances system scalability, flexibility, and accessibility, thereby safeguarding the confidentiality of information exchanged over the internet. The paper establishes the superiority of Twofish over lightweight cryptography for low-power devices within IoT systems through a thorough analysis of critical factors. These factors encompass the robust security strength of Twofish, its effective encryption capabilities guaranteeing data confidentiality, the algorithm's maturity validated by extensive scrutiny from the cryptographic community, compatibility and interoperability with diverse platforms, resource efficiency in maintaining a balance between security and device capabilities, and the crucial long-term security assurance required for the extended operational lifetimes of IoT devices. The research findings affirm that the incorporation of Twofish into wireless smart energy systems and IoT cloud services offers a secure and efficient solution for safeguarding sensitive information within IoT environments. This consolidated approach contributes significantly to advancing secure communication and data management in the continually evolving landscape of the Internet of Things.

COVID-19 prediction using Caviar Squirrel Jellyfish Search Optimization technique in fog-cloud based architecture.

In the pandemic of COVID-19 patients approach to the hospital for prescription, yet due to extreme line up the patient gets treatment after waiting for more than one hour. Generally, wearable devices directly measure the preliminary data of the patient stored in capturing mode. In order to store the data, the hospitals require large storage devices that make the progression of data more complex. To bridge this gap, a potent scheme is established for COVID-19 prediction based fog-cloud named Caviar Squirrel Jellyfish Search Optimization (CSJSO). Here, CSJSO is the amalgamation of CAViar Squirrel Search Algorithm (CSSA) and Jellyfish Search Optimization (JSO), where CSSA is blended by the Conditional Autoregressive Value-at-Risk (CAViar) and Squirrel Search Algorithm (SSA). This architecture comprises the healthcare IoT sensor layer, fog layer and cloud layer. In the healthcare IoT sensor layer, the routing process with the collection of patient health condition data is carried out. On the other hand, in the fog layer COVID-19 detection is performed by employing a Deep Neuro Fuzzy Network (DNFN) trained by the proposed Remora Namib Beetle JSO (RNBJSO). Here, RNBJSO is the combination of Namib Beetle Optimization (NBO), Remora Optimization Algorithm (ROA) and Jellyfish Search optimization (JSO). Finally, in the cloud layer, the detection of COVID-19 employing Deep Long Short Term Memory (Deep LSTM) trained utilizing proposed CSJSO is performed. The evaluation measures utilized for CSJSO_Deep LSTM in database-1, such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) observed 0.062 and 0.252 in confirmed cases. The measures employed in database-2 are accuracy, sensitivity and specificity achieved 0.925, 0.928 and 0.925 in K-set.

Automatic Crop Protection from Rain Fall Using IOT and IBM Cloud

With the ongoing fourth revolution, technology has been growing rapidly day by day. One of the most significant and efficient uses of this has been evident in the farming sector. From a basic water sprinkler system to the requirement of Fertilizer for specific crops, the likes of Artificial intelligence and the Internet of things have brought in great changes. One of the issues which need to be addressed is the protection of crops from heavy rainfall which causes significant damage to the crop production and soil yield, also causing huge mental distress to a farmer. We in our proposed model have given a solution to this problem by giving automated as well as manual control. The motivation behind our paper is to keep the crops protected from these heavy precipitations and preserve the same rainwater for future purposes when water is scarce. We use a sensor with a NodeMCU module to ensure the covering of the estimated field and LCD display to show status of Field. This project addresses the critical issue of protecting crops from heavy rainfall through the integration of Internet of Things (IoT) technology and the IBM Cloud platform. By deploying a network of sensors capable of measuring rainfall intensity, soil moisture, temperature, and humidity, real-time data is collected and transmitted to the IBM Cloud for analysis. These measures may include adjusting protective structures, or activating drainage systems. The project aims to improve crop yield and reduce losses caused by adverse weather conditions, making agriculture more sustainable and efficient. The combination of IoT and IBM Cloud empowers farmers with real-time monitoring and control, enhancing their ability to protect valuable crops. This project not only demonstrates the practical application of advanced technologies in agriculture but also provides a foundation for future research and development in this field.