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1
da Costa Filho, Paulo Eugênio, Leonardo Augusto de Aquino Marques, Israel da S. Felix de Lima, et al. "Machine-Learning-Based Classification of Electronic Devices Using an IoT Smart Meter." Informatics 12, no. 2 (2025): 48. https://doi.org/10.3390/informatics12020048.
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This study investigates the implementation of artificial intelligence (AI) algorithms on resource-constrained edge devices, such as ESP32 and Raspberry Pi, within the context of smart grid (SG) applications. Specifically, it proposes a smart-meter-based system capable of classifying and detecting the Internet of Things (IoT) electronic devices at the extreme edge. The smart meter developed in this work acquires real-time voltage and current signals from connected devices, which are used to train and deploy lightweight machine learning models—Multi-Layer Perceptron (MLP) and K-Nearest Neighbor (KNN)—directly on edge hardware. The proposed system is integrated into the Artificial Intelligence in the Internet of Things for Smart Grids IAIoSGT architecture, which supports edge–cloud processing and real-time decision-making. A literature review highlights the key gaps in the existing approaches, particularly the lack of embedded intelligence for load identification at the edge. The experimental results emphasize the importance of data preprocessing—especially normalization—in optimizing model performance, revealing distinct behavior between MLP and KNN models depending on the platform. The findings confirm the feasibility of performing accurate low-latency classification directly on smart meters, reinforcing the potential of scalable AI-powered energy monitoring systems in SG.
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Bhuvaneshwari, V. "Development of Real Time Underground Monitoring System Using Unmanned Vechile." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41657.
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The cave environment plays a crucial role in research across geology, biology, ecology, hydrology, and cultural anthropology. This project aims to develop an unmanned vehicle using IoT and Raspberry Pi technologies to monitor cave conditions and transmit data via GPS. The vehicle will detect environmental parameters such as air quality, temperature, humidity, lighting, objects, and soil condition using integrated sensors and AI algorithms. Designed for safe navigation, the unmanned vehicle identifies hazards like air pollution, darkness and autonomously retreats to avoid damage. The unmanned vehicles have become more reliable and practical for cave exploration. This system will assist in identifying dangers like collapses, flooding, landslides, and low oxygen zones, enhancing safety in cave exploration. The vehicle is designed to operate in confined and harsh environments, ensuring reliable performance in challenging cave conditions. Robust sensors ensure precise detection of environmental parameters, even in low-light or obstructed areas. This innovative solution bridges the gap between manual cave exploration and modern technological advancements. Key Words: Sensor Fusion, Internet of things, Remote Sensing , Artificial Intelligence, Hazard Detection
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Li, Yaxin, Yan Chen, Zhen Yang, Chao Tong, Xinxing Yang, and Weiliang Zhong. "Design of a Multi-modal Sensor Fusion Unmanned Vehicle System based on Computer Vision." Journal of Physics: Conference Series 2504, no. 1 (2023): 012033. http://dx.doi.org/10.1088/1742-6596/2504/1/012033.
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Abstract With the development of artificial intelligence and Internet of Things technology, more and more intelligent products are appearing around us, such as sweeping robots, library service robots and drug delivery robots, etc. In the practical application of these intelligent robots, the core technology of spatial positioning is inseparable, and considering the cost, signal interference and positioning accuracy, it is necessary to study the positioning technology with low cost, small size and strong anti-interference factors. To solve the problem of poor performance of positioning accuracy when using low-cost sensors in the physical environment, we use a four-wheel drive vehicle model as a carrier to build an unmanned vehicle system based on multi-modal sensor fusion, binocular vision localization and other technologies. The core of the system is the MM32F3277G9P chip from MindMotion and the Raspberry Pi embedded development board. The proposed vision information is based on the Intel Realsense T265 camera, which is fused with the data from the nine-axis inertial measurement unit (IMU) and the dual-frequency global positioning system (GPS), so that the positioning algorithm can continuously provide robust and accurate state estimation results in the physical environment through the complementary advantages.
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Patil, Abhay. "Artificial Intelligence System for Effective Detection of Animal Intervention in Croplands." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 518–23. http://dx.doi.org/10.22214/ijraset.2021.38009.
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Abstract: Animal intervention is significant intimidation to the potency of the crops, which influences food security and decreases the value to the farmers. This suggested model displays the growth of the Internet of Things and Machine learning technique-based resolutions to surmount this obstacle. Raspberry Pi commands the machine algorithm, which is interfaced with the ESP8266 Wireless Fidelity module, Pi-Camera, Speaker/Buzzer, and LED. Machine learning algorithms similar to Regionbased Convolutional Neural Network and Single Shot Detection technology represents an essential function to identify the target in the pictures and classify the creatures. The experimentation exhibits that the Single Shot Detection algorithm exceeds than Region-based Convolutional Neural Network algorithm. Ultimately, the Twilio API interfaced software decimates the data to the farmers to take conclusive work in their farm territory. Keywords: Region-Based Convolutional Neural Network (R-CNN), Tensor Flow, Raspberry Pi, Internet of Things (IoT), Single Shot Detector (SSD)
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Shirshetti, Mr U. S. "AutoDry : Automation in Food Drying." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45052.
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Abstract — Traditional food dehydration methods are often inefficient, labor-intensive, and inconsistent in preserving food quality. This research introduces AutoDry, a smart food drying system that utilizes artificial intelligence and Internet of Things (IoT) to automate food dehydration processes. The system detects food type using a camera, automatically sets the drying parameters, and allows remote monitoring via a mobile application. Developed using Raspberry Pi, temperature sensors, and Java-based software, AutoDry aims to reduce energy consumption, labor requirements, and post-harvest food wastage. The system is scalable for both domestic and industrial use, empowering small-scale farmers to preserve and sell surplus produce efficiently. Experimental results indicate improved drying precision and usability, making AutoDry a sustainable and market-ready solution. Keywords — Food Dehydration, IoT, Artificial Intelligence, Raspberry Pi, Automation, Smart Agriculture
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Mekala, R., and M. Sathya. "Raspberry Pi-Based Smart Energy Meter Using Internet of Things with Artificial Intelligence." Engineering World 5 (December 31, 2023): 201–9. http://dx.doi.org/10.37394/232025.2023.5.23.
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There are numerous challenges with existing domestic energy meter reading systems, in constructions, narrow bandwidths, low rates, poor real-time, and slow two-way communications. This paper used an Automatic Meter Reading device with wireless technology to solve the problems. The proposed approach uses the Internet of Things (IoT) to communicate between the Electricity Board and the user section, allowing the customer's electricity usage and bill information to be transmitted. The customer receives information on bill amounts and payments through IoT. In the past decade, the Indian power sector accomplished a great deal in policy reforms, private sector participation in generation and transmission, and the development of new manufacturing technology and capabilities, still more to accomplish and obstacles to overcome for capitalization. Therefore, the private sectors are very active in investing in various parts of the value chain. Nevertheless, the majority engagement of private investors is taking place in the generation. This trend is driven by de-licensing of generation, fiscal incentives for large-scale capacity increases, and competitive buying of electricity. Accordingly, with the changes implemented in the industry, the structure of the market has been transformed from vertically integrated to competitive. The effectiveness of the market has been increased throughout time as a consequence of several rules and regulations that have had the intended effect. Mobility in the power market has risen, and so has the number of competitors; legislation has produced a competitive marketplace, which will in the future totally open the market in the power sector.
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Patil, Vaishnavi. "IOT and AI Implementations on Remote Healthcare Monitoring System." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 3045–49. http://dx.doi.org/10.22214/ijraset.2024.59562.
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Abstract: "IoT and AI Implementations on Remote Healthcare Monitoring System using Raspberry Pi 3" seeks to revolutionize healthcare by integrating cutting-edge technologies into a cohesive system for remote patient monitoring. Leveraging the power of Raspberry Pi 3, Internet of Things (IoT) devices will be employed to collect real-time health data from patients in diverse locations. This data will then undergo sophisticated analysis through Artificial Intelligence (AI) algorithms, enhancing the system's ability to identify anomalies, trends, and potential health risks with a high degree of accuracy. The Raspberry Pi 3 serves as a versatile and cost-effective hub, managing the connectivity and processing requirements of the IoT devices. The system aims to provide continuous and proactive healthcare support by enabling remote monitoring of vital signs and timely interventions based on AI- driven insights. The outcomes of this project not only contribute to the advancement of healthcare technology but also address the growing need for scalable and accessible healthcare solutions in a rapidly evolving digital landscape.
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Xuan-Kien, Dang, Anh-Hoang Ho Le, Nguyen Xuan-Phuong, and Mai Ba-Linh. "Applying artificial intelligence for the application of bridges deterioration detection system." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 1 (2022): 149–57. https://doi.org/10.12928/telkomnika.v20i1.20783.
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Recently, advances in sensor technologies, data communication paradigms, and data processing algorithms all affect the feasibilities of the bridges structural health monitoring and deterioration detection, and other implementations of monitoring operations. The paper proposes a method to design an irregularity detection and monitoring system for road bridges that combines internet of things (IoT) and artificial intelligence (AI) technologies. Raspberry Pi 4 embedded computer integrating IoT and AI technology with convolutional neural network (CNN) is employed to simultaneously monitor remote bridges on websites and apps via Google Firebase cloud database. The first step of successful testing in the laboratory showed that the system can work stably and coincide with the proposed goals.
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Samyuktha, R., and B. Gayathri. "Internet of Things (IoT) Based surgery with Innovative Combination of Artificial Intelligence and Human Intelligence." International Journal on Cybernetics & Informatics 10, no. 2 (2021): 01–05. http://dx.doi.org/10.5121/ijci.2021.100201.
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While examining the historical backdrop of clinical procedure, we can understand that medical practitioner have/had created and refined instruments for complicated surgeries. Development in clinical progressions is on a standard with that saw in the sickness causing specialists and infections. Since ancient time, clinical practices were performed using obtrusive medical procedures without sedation, which resulted in high mortality and post-surgery complications. This led to the emergence of effective, safe and user friendly medical instruments and procedures with little to moderately death rate. At present obtrusive methodologies are negligibly practiced, but provides less twisted related complexities, fast organ work return, and more limited hospitalizations. The success of these methods has prompted for higher acknowledgment of picture guided surgeries. We present an Internet Of things (IOT) and Artificial Intelligence (AI) based model that includes a computer generated experience based (VR-based) User interface and some benefits and limitations. It can be done by Raspberry pi, android application and also done by Sap cloud system.
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Bhise, Asha K., and Dr S. G. Kanade. "Artificial Intelligence Based Smart Home Energy Management System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44655.
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A home automation system controls lighting, temperature, multimedia systems, and appliances. Since these devices and sensors are connected to common infrastructure, they form the Internet of Things. A home automation system links multiple controllable devices to a centralized server. These devices have a user interface for controlling and monitoring, which can be accessed by using a tablet or a mobile application, which can be accessed remotely as well. Ideally, anything that can be connected to a network can be automated and controlled remotely. Smart homes must be artificially intelligent systems that need to adapt themselves based on user actions and surroundings. These systems need to carefully analyze the user needs and the conditions of the surroundings in order to predict future actions and also minimizes user interaction. Traditional home automation systems that provide only remote access and control are not that effective in terms of being ‘smart’, so in this paper we put forward the use of concepts of different machine learning algorithms along with computer vision to shape together a smart learning automated system that controls lighting, sound and other devices based on the user’s emotion. Keywords-Machine learning(ML), AI(Artificial intelligence), Smart home(SM), Internet of things (IoT), MQTT, Raspberry pi
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Sarimole, Frencis Matheos, and Ahas Eko Septianto. "Implementation of IoT-Based Facial Recognition for Home Security System Using Raspberry Pi and Mobile Application." International Journal Software Engineering and Computer Science (IJSECS) 4, no. 2 (2024): 453–62. http://dx.doi.org/10.35870/ijsecs.v4i2.2554.
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The rapid advancement of technologies such as Artificial Intelligence (AI), computer vision, and the Internet of Things (IoT) has significantly impacted various fields, particularly in security systems. Traditional security measures, such as door locks, are increasingly inadequate in ensuring the safety of homes. To address this issue, we have developed a prototype of a home security system based on Raspberry Pi, integrated with a real-time mobile application. This intelligent system is designed to monitor residential areas, detect unfamiliar individuals, and send immediate notifications to the homeowner's mobile device. Utilizing Raspberry Pi in conjunction with OpenCV for motion and facial recognition, as well as a web server, the system demonstrates high accuracy in detecting motion and faces. It promptly notifies the homeowner in the event of suspicious activity. This prototype represents an efficient and effective solution to enhancing home security by leveraging modern technology.
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Dahdouh, Yousra, Anouar Abdelhakim Boudhir, and Ahmed Mohamed Ben. "Embedded artificial intelligence system using deep learning and Raspberry Pi for the detection and classification of melanoma." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1104–11. https://doi.org/10.11591/ijai.v13.i1.pp1104-1111.
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Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computerassisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
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Prof, D. P. Radke, Bawankar Vaibhav, Deshmukh Sagar, Lambat Gauri, and Kharwade Bhargavi. "Artificial Intelligence Based Self-Driving Car." Advancement of Computer Technology and its Applications 3, no. 1 (2020): 1–9. https://doi.org/10.5281/zenodo.3698550.
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<em>In this modern era, the Automobile Industry is getting updated day by day with the implementation of the latest technologies in vehicles. Artificial Intelligence and Machine Learning are some of the best trending and useful technologies in the world. Google, the expert in technologies, working on such projects since 2010 and continuously making modifications to it to date. In this paper, we focus on the implementation of all these advanced technologies in the vehicle to make the vehicle automated. Vehicles can detect and analyze their surroundings with the help of OpenCV by which vehicles can take decisions according to the surrounding conditions and drive on itself without human aid. Machine Learning helps the vehicle to understand the traffic signals and signboards so that a vehicle can drive according to it. These technologies help the vehicle to drive in rush traffics to minimize the use of clutch and brake. Since there is no human interaction, the human error will not be there and hence it will strictly follow the traffic rules and also minimize the percentage of accidents. Again with the help of IoT (Internet of Things) technology, the vehicle can notify all emergency stations (like a police station, fire station, etc.) about any emergency (like an accident). Hence by using all the latest technologies, our vehicle is increasing the efficiency of roads, fuel, emergency stations, etc. It also makes a better driving experience by giving relaxation to the driver.</em>
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Dahdouh, Yousra, Abdelhakim Boudhir Anouar, and Mohamed Ben Ahmed. "Embedded artificial intelligence system using deep learning and raspberrypi for the detection and classification of melanoma." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 1104. http://dx.doi.org/10.11591/ijai.v13.i1.pp1104-1111.
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<div lang="EN-US"><span>Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.</span></div>
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Moshynska, Alina, and Oleksandr Khrokalo. "REMOTE VEHICLE DIAGNOSTIC SYSTEM DEVELOPMENT BASED ON THE INTERNET OF THINGS TECHNOLOGY." Information and Telecommunication Sciences, no. 1 (June 28, 2024): 28–32. http://dx.doi.org/10.20535/2411-2976.12024.28-32.
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Background. Advanced artificial intelligence and IoT gateways are working together in the automotive industry to predict potential vehicle problems by analysing sensor data and optimizing quality control processes. Manufacturers can detect anomalies, improve product reliability, and eliminate manufacturing defects or malfunctions in advance. Predictive analytics also lead to improved fuel efficiency, performance and overall vehicle reliability. Objective. The purpose of this work is to develop a model for remote diagnosis of vehicle faults using a Raspberry Pi model B microcomputer and a SIM7600G-H GSM module. Configure data modules, install the necessary software and configure it, demonstrate step-by-step actions, and perform diagnostics and testing of this module for data transmission. Methods. A prototype was created on the basis of Raspberry Pi 4. and provides monitoring of machine operation in remote mode using the SIM7600E-H LTE Cat-4 4G/3G module. The design has small dimensions, easy installation, requires only initial adjustment and has a wide range of improvements. Results. This prototype uses a diagnostic OBD-II car scanner ELM327 with Bluetooth connection support, a Raspberry PI 4 model B microcomputer with 8 GB of RAM, 4 USB connectors (2 ports type USB3 and 2 ports type USB2), a Gigabit Ethernet port, a USB-C power supply port, and two micro HDMI 4K display connectors. On top of the module there are 48 pins (contacts) with which you can connect modules of different types and directions. The SIM7600G-H communication module is connected to these pins. The last element of the prototype is the SIM card of one of the telephone service providers and the micro SD card, which will act as the main memory element on which the operating system will be written and data will be stored. Conclusions. The article proposes the development of a device model using Internet of Things technologies, which is capable of providing remote diagnosis of car malfunctions. This model is based on the use of the SIM7600G-H module, which provides data transmission through the mobile network. The developed model allows you to read data from various car sensors, as well as transfer this data to a remote device for further analysis. This makes it possible to quickly detect malfunctions and make timely decisions on their correction.
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Phawinee, Suphawimon, Jing-Fang Cai, Zhe-Yu Guo, Hao-Ze Zheng, and Guan-Chen Chen. "Face recognition in an intelligent door lock with ResNet model based on deep learning." Journal of Intelligent & Fuzzy Systems 40, no. 4 (2021): 8021–31. http://dx.doi.org/10.3233/jifs-189624.
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Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.
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Dobrojevic, Milos, and Nebojsa Bacanin. "IoT as a Backbone of Intelligent Homestead Automation." Electronics 11, no. 7 (2022): 1004. http://dx.doi.org/10.3390/electronics11071004.
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The concepts of smart agriculture, with the aim of highly automated industrial mass production leaning towards self-farming, can be scaled down to the level of small farms and homesteads, with the use of more affordable electronic components and open-source software. The backbone of smart agriculture, in both cases, is the Internet of Things (IoT). Single-board computers (SBCs) such as a Raspberry Pi, working under Linux or Windows IoT operating systems, make affordable platform for smart devices with modular architecture, suitable for automation of various tasks by using machine learning (ML), artificial intelligence (AI) and computer vision (CV). Similarly, the Arduino microcontroller enables the building of nodes in the IoT network, capable of reading various physical values, wirelessly sending them to other computers for processing and furthermore, controlling electronic elements and machines in the physical world based on the received data. This review gives a limited overview of currently available technologies for smart automation of industrial agricultural production and of alternative, smaller-scale projects applicable in homesteads, based on Arduino and Raspberry Pi hardware, as well as a draft proposal of an integrated homestead automation system based on the IoT.
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Dr.Trupti, Kaushiram Wable. "Design and Implementation of Collaborative Cloud-Edge System Using Raspberry Pi for Video Surveillance System with AIoT to Analyse Effective Performance Parameters of Network." Research and Applications: Emerging Technologies 6, no. 2 (2024): 30–35. https://doi.org/10.5281/zenodo.11607879.
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<em>The video surveillance can avoid many crimes as well as it will help to reduce crime rate in society as well we can save many lives. But currently implemented IoT system having various limitations like insufficient storage capacity and inadequate processing of information. Thus we can integrate traditional IoT system with Artificial Intelligence (AI) models to improve storage capacity & processing called as Artificial Intelligence of Things (AIoT). This system mainly focuses on performance parameter of video surveillance system the parameter consist of Response Latency Time, Network Bandwidth & Storage on server. In proposed system divided in two part, First part include Edge node implemented with Raspberry Pi as IoT system which having video input then it perform image processing & store output on edge node, second part include cloud node which is train with AI model as AI system to extract image and analyzed performance of system. So Cloud-Edge Collaborative system refers as Artificial Intelligence of Things (AIoT). In this research I conclude comparative study of traditional Cloud Computing System with Collaborative Cloud-Edge Computing system which shows that, the Response Latency Time improve by 5 times, Network Bandwidth improve by 10 times and storage capacity improve by 5 times of traditional Edge Computing System.</em>
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Hizem, Moez, Leila Bousbia, Yassmine Ben Dhiab, Mohamed Ould-Elhassen Aoueileyine, and Ridha Bouallegue. "Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications." Sensors 25, no. 8 (2025): 2496. https://doi.org/10.3390/s25082496.
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The advent of Tiny Machine Learning (TinyML) has unlocked the potential to deploy machine learning models on resource-constrained edge devices, revolutionizing real-time monitoring in Internet of Medical Things (IoMT) applications. This study introduces a novel approach to real-time electrocardiogram (ECG) anomaly detection by integrating TinyML with edge Artificial Intelligence (AI) on low-power embedded systems. We demonstrate the feasibility and effectiveness of deploying optimized models on edge devices, such as the Raspberry Pi and Arduino, to detect ECG anomalies, including arrhythmias. The proposed workflow encompasses data preprocessing, feature extraction, and model inference, all executed directly on the edge device, eliminating the need for cloud resources. To address the constraints of memory and power consumption in wearable devices, we applied advanced optimization techniques, including model pruning and quantization, achieving an optimal balance between accuracy and resource utilization. The optimized model achieved an accuracy of 92.3% while reducing the power consumption to 0.024 mW, enabling continuous, long-term health monitoring with minimal energy requirements. This work highlights the potential of TinyML to advance edge AI for real-time medical applications.
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Mohamed, A. Torad, Bouallegue Belgacem, and M. Ahmed Abdelmoty. "A voice controlled smart home automation system using artificial intelligent and internet of things." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 4 (2022): 808–16. https://doi.org/10.12928/telkomnika.v20i4.23763.
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The objective of this work is to take a step further in this direction by incorporating voice control and artificial intelligence (AI) into internet of things (IoT)-based smart home systems to create more efficient automated smart home systems. Accordingly, a home automation system proposal is presented, in which the related functions can be controlled by voice commands using an android or web application via a chat form. The user issues a voice command, which is deciphered by natural language processing (NLP). To accommodate the user’s request, the NLP classifies it into operation commands. Arduino and Raspberry Pi are used to translate the commands extracted from NLP into reality. Based on this, home applications can be controlled. Also, the utilities consumption could be calculated, saved, and paid on time. This is in addition to the introduction of a machine learning (ML)-based recommendation system for automated home appliance control. In this approach, the mobile or web application is considered as the central controller, deciding the appropriate actions to fulfill the user’s desires. The presented work has been put into practice and tested. It proved to be applicable, as well as having the potential for making home life more comfortable, economic, and safe.
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Ridolfi, Lorenzo, David Naseh, Swapnil Sadashiv Shinde, and Daniele Tarchi. "Implementation and Evaluation of a Federated Learning Framework on Raspberry PI Platforms for IoT 6G Applications." Future Internet 15, no. 11 (2023): 358. http://dx.doi.org/10.3390/fi15110358.
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With the advent of 6G technology, the proliferation of interconnected devices necessitates a robust, fully connected intelligence network. Federated Learning (FL) stands as a key distributed learning technique, showing promise in recent advancements. However, the integration of novel Internet of Things (IoT) applications and virtualization technologies has introduced diverse and heterogeneous devices into wireless networks. This diversity encompasses variations in computation, communication, storage resources, training data, and communication modes among connected nodes. In this context, our study presents a pivotal contribution by analyzing and implementing FL processes tailored for 6G standards. Our work defines a practical FL platform, employing Raspberry Pi devices and virtual machines as client nodes, with a Windows PC serving as a parameter server. We tackle the image classification challenge, implementing the FL model via PyTorch, augmented by the specialized FL library, Flower. Notably, our analysis delves into the impact of computational resources, data availability, and heating issues across heterogeneous device sets. Additionally, we address knowledge transfer and employ pre-trained networks in our FL performance evaluation. This research underscores the indispensable role of artificial intelligence in IoT scenarios within the 6G landscape, providing a comprehensive framework for FL implementation across diverse and heterogeneous devices.
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Mugunthan, S. R. "Smart Environment: AI-Driven Predictions and Forecasting of Air Quality." December 2023 5, no. 4 (2023): 390–403. http://dx.doi.org/10.36548/jscp.2023.4.005.
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Addressing the critical issue of air quality in the Coimbatore region, this study introduces a novel approach for continuous monitoring and forecasting of air pollution. By utilizing the Internet of Things (IoT) technology integrated with Artificial Intelligence (AI) methods, this research focuses on monitoring and forecasting three major pollutants such as Ozone (O3), Ammonia (NH3), and Carbon Monoxide (CO). The proposed IoT-based sensor nodes collect the real-time data and give the resultant data as an input to the Naive Bayes (NB) for classification and Auto-Regression Integrating Moving Average (ARIMA) for optimization. The optimized model parameters are obtained and then validated by using performance metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Deploying a machine learning algorithm on a Raspberry Pi-3, the proposed system ensures efficient monitoring and forecasting of air pollutants 24/7 through an online open-source dashboard.
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Ario, Muhamad Keenan, David Leon, Muhammad Rizki Pratama, and Gentrya Wirya Pamungkas. "Designing IoT-Based Smarthome System With Chatbot." Engineering, MAthematics and Computer Science (EMACS) Journal 4, no. 3 (2022): 113–17. http://dx.doi.org/10.21512/emacsjournal.v4i3.8787.
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Smart home system aim to maximize surveillance, monitoring, and security. This system is integrated with telecommunications and control systems from the microcontroller to create the Internet of Things (IoT). Nowadays, home appliances are integrated with the Smart System that connected to the internet. On the other hand, messenger applications now integrated with chat bot with Artificial Intelligence to make user easier to communicate. This trend made a possibility to implement a system where a home appliance can be operated by only using a messenger application. In this research, a Smart home system designed with a client-server system based on Raspberry Pi as microcontroller and Telegram Messenger as interface that perform the control communication. The process separated into three stages: design, implementation, and result. The design consists of designing the server, interface, and Smart Home control system. To test the performance, the Messenger Bot are compared with other direct controller application. The result show that the Telegram Messenger application is suitable and more convinient for being the IoT controller.
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R, Dr Jamuna. "AI Based Predictive Maintenance for Vehicles." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41755.
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Predictive maintenance is a critical aspect of ensuring the longevity and optimal performance of vehicles. This paper presents a novel approach for vehicle health monitoring through the integration of Artificial Intelligence techniques and the Internet of Things (IoT) for predictive maintenance. The system utilizes a (RPI) platform combined with an OBD2 interface, using an ELM327 Bluetooth device to retrieve real-time vehicle data. The AI model processes these data inputs to predict potential vehicle failures and recommend maintenance actions, reducing downtime and improving overall efficiency. By leveraging machine learning algorithms, the system analyzes various vehicle parameters such as engine performance, fuel efficiency, and emission levels to generate actionable insights. This intelligent system enables early detection of faults, thus lowering repair costs, enhancing safety, and improving vehicle reliability. The proposed system is a cost-effective and scalable solution for vehicle maintenance, making it a valuable tool for both individual vehicle owners and fleet management operations. Keywords: RPI Raspberry Pi, OBD2-Onboard Diagnosis Device
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Indrabayu, Indrabayu, Intan Sari Areni, Anugrayani Bustamin, and Rizka Irianty. "A real-time data association of internet of things based for expert weather station system." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 432. http://dx.doi.org/10.11591/ijai.v11.i2.pp432-439.
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The wind carries moisture into an atmosphere and hot or cold air into a climate, affecting weather patterns. Knowing where the wind is coming from gives essential insight into what kind of temperatures are to be expected. However, the wind is affected by spatial and temporal variabilities, thus making it difficult to predict. This study focuses on finding data associations from the weather station installed at Hasanuddin University Campus based on internet of things (IoT) using Raspberry Pi as a gateway that associated all the meteorological data from sensors. The generation of association rules compares the Apriori and FP-growth algorithms to determine relations among itemsets. The results show that high humidity and warm temperature tend to associate with a westerly wind and occur at night. In contrast, conditions with less humid and moderate temperatures tend to have southerly and southeasterly wind.
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Mariam, Fiza, Niharika Nandi S P, Ganavi B S, and Prof Gowrishankar B S. "Design and Implementation of Smart Shopping Trolley with Mobile Cart Application." International Journal of Engineering Research in Computer Science and Engineering 9, no. 7 (2022): 79–82. http://dx.doi.org/10.36647/ijercse/09.07.art017.
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As there is a lot of advancement in the field of technology there are many eye-catching inventions in the domains of machine learning, artificial intelligence, internet of things and so on, due to these advancements, with the customer point of view people start expecting a lot. Nowadays people go to shopping on a daily basis. Customers have very little time to spare for standing in queues for billing purpose. In this paper, we are presenting a smart shopping cart with mobile cart application which is a prominent solution for the above-mentioned problem. this smart shopping cart comes with raspberry Pi controller, RFID Reader, RFID Tags, L298N Motor Drivers, Pi Camera, Load Cell and a Mobile Cart application. The customer needs scan the product using RFID reader which is attached to the cart, the load cell will get the weight of the items in cart and all the price details will be reflected in the online application. After all the items are scanned the final bill will be displayed on the application and the bill payment can be done using the same application
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Navulur, Sridevi, A. S. C. S. Sastry, and M. N. Giri Prasad. "Agricultural Management through Wireless Sensors and Internet of Things." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3492. http://dx.doi.org/10.11591/ijece.v7i6.pp3492-3499.
Full textAbstract:
Agriculture plays a significant role in most countries and there is an enoromous need for this industry to become “Smart”. The Industry is now moving towards agricultural modernization by using modern smart technologies to find solutions for effective utilization of scarce resources there by meeting the ever increasing consumtion needs of global population. With the advent of Internet of Things and Digital transformation of rural areas, these technologies can be leveraged to remotely monitor soil moisture, crop growth and take preventive measures to detect crop damages and threats. Utilize artificial intelligence based analytics to quickly analyze operational data combined with 3rd party information, such as weather services, expert advises etc., to provide new insights and improved decision making there by enabling farmers to perform “Smart Agriculture”. Remote management of agricultural activities and their automation using new technologies is the area of focus for this research activity. A solar powered remote management and automation system for agricultural activities through wireless sensors and Internet of Things comprising, a hardware platform based on Raspberry Pi Micro controller configured to connect with a user device and accessed through the internet network. The data collection unit comprises a set of wireless sensors for sensing agricultural activities and collecting data related to agricultural parameters; the base station unit comprising: a data logger; a server; and a software application for processing, collecting, and sending the data to the user device. The user device ex: mobile, tablet etc. can be connected to an internet network, whereby an application platform (mobile-app) installed in the user device facilitates in displaying a list of wireless sensor collected data using Internet of Things and a set of power buttons. This paper is a study and proposal paper which discusses the factors and studies that lead towards this patent pending invention, AGRIPI.
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Sridevi, Navulur, S. C. S. Sastry A., and N. Giri Prasad M. "Agricultural Management through Wireless Sensors and Internet of Things." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (2017): 3492–99. https://doi.org/10.11591/ijece.v7i6.pp3492-3499.
Full textAbstract:
Agriculture plays a significant role in most countries and there is an enoromous need for this industry to become “Smart”. The Industry is now moving towards agricultural modernization by using modern smart technologies to find solutions for effective utilization of scarce resources there by meeting the ever increasing consumtion needs of global population. With the advent of Internet of Things and Digital transformation of rural areas, these technologies can be leveraged to remotely monitor soil moisture, crop growth and take preventive measures to detect crop damages and threats. Utilize artificial intelligence based analytics to quickly analyze operational data combined with 3rd party information, such as weather services, expert advises etc., to provide new insights and improved decision making there by enabling farmers to perform “Smart Agriculture”. Remote management of agricultural activities and their automation using new technologies is the area of focus for this research activity. A solar powered remote management and automation system for agricultural activities through wireless sensors and Internet of Things comprising, a hardware platform based on Raspberry Pi Micro controller configured to connect with a user device and accessed through the internet network. The data collection unit comprises a set of wireless sensors for sensing agricultural activities and collecting data related to agricultural parameters; the base station unit comprising: a data logger; a server; and a software application for processing, collecting, and sending the data to the user device. The user device ex: mobile, tablet etc. can be connected to an internet network, whereby an application platform (mobile-app) installed in the user device facilitates in displaying a list of wireless sensor collected data using Internet of Things and a set of power buttons. This paper is a study and proposal paper which discusses the factors and studies that lead towards this patent pending invention, AGRIPI.
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Norhashim, Nurhakimah, Shahrul Ahmad Shah, Nadhiya Liyana Mohd Kamal, Zulhilmy Sahwee, Mohd Amzar Azizan, and Muhammad Izzat Afiq Ab Norizan. "Face Recognition System at the Airport Based on Internet of Things and Cloud Technologies." Karya Journal of Aerospace and Avionics System 1, no. 1 (2025): 31–40. https://doi.org/10.37934/kjaas.1.1.3140.
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Nowadays, the level of airport security system has been questioned, as there are a few cases involving unexpected events, such as hijacking. Based on the report, the suspected passenger used fake passport and documents, for example, the MH370 incident, whereby after an investigation was conducted by Interpol, two of the passengers were using fake passports and documents. Therefore, airport security could be enhanced at the security, immigration and boarding gates with the combination of new technologies, such as artificial intelligence, biometric technology and big data. This project aims to enhance the airport security system at the boarding gate and indirectly smooth travel experiences for passengers. This system is known as Smart Face Surveillance Camera (SFSC) system, which is able to identify passengers and cabin crew identities in real-time, including their vaccination status before boarding. Passengers need to enter their personal details, such as flight numbers and have their image taken using SFSC at the departure gate. Then, at the boarding gate, cameras will verify the passenger’s face, whereby the data should be identical as that stored in Cloud, which can be monitored by authorities in the case where a passenger’s identity becomes suspicious. This system uses the combination of Raspberry Pi and cameras. It would benefit the society by enhancing airport security and most importantly, individuals will feel safe to fly in the aircraft with seamless travel experiences.
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Stavropoulos, Georgios, John Violos, Stylianos Tsanakas, and Aris Leivadeas. "Enabling Artificial Intelligent Virtual Sensors in an IoT Environment." Sensors 23, no. 3 (2023): 1328. http://dx.doi.org/10.3390/s23031328.
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The demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degradation in the end-to-end communication, monitoring, and orchestration of the various connected devices. In order to solve the problem of increasing sensor demands, this paper suggests replacing physical sensors with machine learning (ML) models. These software-based artificial intelligence models are called virtual sensors. Extensive research and simulation comparisons between fourteen ML models provide a solid ground decision when it comes to the selection of the most accurate model to replace physical sensors, such as temperature and humidity sensors. In this problem at hand, the virtual and physical sensors are designed to be scattered in a smart home, while being connected and run on the same IoT platform. Thus, this paper also introduces a custom lightweight IoT platform that runs on a Raspberry Pi equipped with physical temperature and humidity sensors, which may also execute the virtual sensors. The evaluation results of the devised virtual sensors in a smart home scenario are promising and corroborate the applicability of the proposed methodology.
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Panduman, Yohanes Yohanie Fridelin, Radhiatul Husna, Noprianto, et al. "An Application of SEMAR IoT Application Server Platform to Drone-Based Wall Inspection System Using AI Model." Information 16, no. 2 (2025): 91. https://doi.org/10.3390/info16020091.
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Recently, artificial intelligence (AI) has been adopted in a number of Internet of Things (IoT) application systems to enhance intelligence. We have developed a ready-made server with rich built-in functions to collect, process, display, analyze, and store data from various IoT devices, the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform, in which various AI techniques have been implemented to enhance its capabilities. In this paper, we present an application of SEMAR to a drone-based wall inspection system using an object detection AI model called You Only Look Once (YOLO). This system aims to detect wall cracks at high places using images taken via a camera on a flying drone. An edge computing device is installed to control the drone, sending the taken images through the Kafka system, storing them with the drone flight data, and sending the data to SEMAR. The images are analyzed via YOLO through SEMAR. For evaluations, we implemented the system using Ryze Tello for the drone and Raspberry Pi for the edge, and we evaluated the detection accuracy. The preliminary experiment results confirmed the effectiveness of the proposal.
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Daher, Ali Walid, Ali Rizik, Marco Muselli, Hussein Chible, and Daniele D. Caviglia. "Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge." Sensors 21, no. 19 (2021): 6526. http://dx.doi.org/10.3390/s21196526.
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Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.
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Jitender, Kumar Singh Jadon. "Operation of Wireless Humanoid Robot using Graphene Embedded Bend Sensor and Internet of Things Technology." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 1 (2022): 19–22. https://doi.org/10.35940/ijeat.A3805.1012122.
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<strong>Abstract: </strong>The use of Robots is a trending technology but automation and Artificial Intelligence are not fully achieved till date. This paper aims to propose an innovative system to integrate human intelligence with Robotics. The robots which have been designed to work in harsh conditions are controlled using graphene-based flexible bend sensors. These sensors are applied to the human body and are powered by solar energy. Here a flexible sensor is applied on each bend on the human body and respective data of bend angle is transmitted to the raspberry pi 3 model B kits which are programmed to act accordingly and the same bend is obtained in the Robot. The sensor which we have used in this project removes the messy wiring and there is no need to wear any kind of suit. The required movements for the robot are produced by a human after applying the sensors on each joint. It looks like a pasting that is pasted across the joint. These sensors are made from a biocompatible material, thus does not have any dermatological ill effect on the operator. The graphene-based sensor has a subsequent role in robotics as they develop position matrices that determine the current position of various members of the humanoid robot. Robotic application demands sensors with a higher degree of repeatability, precision, and reliability which is obtained using the Graphene-based bend sensors. Each sensor is self-capable to carry out motion of one degree of motion. The use of an accelerometer attached along with the sensor helps to control the speed of robotic operation. This system is suitable to control the robot from a distance and uses it in critical conditions with the intelligence of the human being who is operating it, the rise in temperature leads to an increase in the time-lapse in command and action. But still, it can be treated as the substitute for artificially intelligent robots as we have not reached the level of intelligence in human beings. This work is based on the combined concepts of mechanical, computer, and electronics engineering.
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Jadon, Jitender Kumar Singh. "Operation of Wireless Humanoid Robot using Graphene Embedded Bend Sensor and Internet of Things Technology." International Journal of Engineering and Advanced Technology 12, no. 1 (2022): 19–22. http://dx.doi.org/10.35940/ijeat.a3805.1012122.
Full textAbstract:
The use of Robots is a trending technology but automation and Artificial Intelligence are not fully achieved till date. This paper aims to propose an innovative system to integrate human intelligence with Robotics. The robots which have been designed to work in harsh conditions are controlled using graphene-based flexible bend sensors. These sensors are applied to the human body and are powered by solar energy. Here a flexible sensor is applied on each bend on the human body and respective data of bend angle is transmitted to the raspberry pi 3 model B kits which are programmed to act accordingly and the same bend is obtained in the Robot. The sensor which we have used in this project removes the messy wiring and there is no need to wear any kind of suit. The required movements for the robot are produced by a human after applying the sensors on each joint. It looks like a pasting that is pasted across the joint. These sensors are made from a biocompatible material, thus does not have any dermatological ill effect on the operator. The graphene-based sensor has a subsequent role in robotics as they develop position matrices that determine the current position of various members of the humanoid robot. Robotic application demands sensors with a higher degree of repeatability, precision, and reliability which is obtained using the Graphene-based bend sensors. Each sensor is self-capable to carry out motion of one degree of motion. The use of an accelerometer attached along with the sensor helps to control the speed of robotic operation. This system is suitable to control the robot from a distance and uses it in critical conditions with the intelligence of the human being who is operating it, the rise in temperature leads to an increase in the time-lapse in command and action. But still, it can be treated as the substitute for artificially intelligent robots as we have not reached the level of intelligence in human beings. This work is based on the combined concepts of mechanical, computer, and electronics engineering.
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Karras, Aristeidis, Anastasios Giannaros, Christos Karras, et al. "TinyML Algorithms for Big Data Management in Large-Scale IoT Systems." Future Internet 16, no. 2 (2024): 42. http://dx.doi.org/10.3390/fi16020042.
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In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.
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Kashfur Rahman, Md Tauqeer Hussain, Shaik Ayaan, and Mrs. Heena Yasmeen. "Automated Attendance System Using Opencv With Face And Iris Detection." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 285–91. https://doi.org/10.62647/ijitce2025v13i2spp285-291.
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The "Automated Attendance System Using OpenCV, Face and Iris Detection" is an advanced solution designed to replace outdated and insecure traditional attendance systems. By leveraging modern technologies such as OpenCV, artificial intelligence (AI), and Internet of Things (IoT) integration, the system ensures accurate, secure, and user-friendly attendance tracking. It utilizes real-time face detection and recognition to identify individuals, while incorporating eye-blink detection as a liveness check to prevent spoofing attempts using photos or videos. OpenCV serves as the core computer vision engine for detecting facial features, while AI-driven models improve recognition accuracy and adaptability. Eye aspect ratio (EAR) calculations help confirm the user’s physical presence by detecting natural blinking patterns. In secure environments, iris detection adds an extra biometric layer for identity verification. The system can be implemented on IoT-enabled devices such as Raspberry Pi for portability and real-time processing. This solution is ideal for educational institutions, corporate offices, and smart environments. It automates attendance logging, generates reports, and synchronizes data with centralized systems. The integration of computer vision and AI not only improves efficiency but also ensures security and scalability, making this system a powerful step forward in smart attendance technology.
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La Tona, Giuseppe, Massimiliano Luna, Annalisa Di Piazza, and Maria Carmela Di Piazza. "Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi." Applied Sciences 9, no. 10 (2019): 2120. http://dx.doi.org/10.3390/app9102120.
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As the adoption of distributed generation and energy storage grows and the attention to energy efficiency rises, Energy Management is assuming a growing importance in smart homes. Energy Management Systems (EMSs) should be easily deployable on smart homes and seamlessly integrate with the Internet of Things (IoT) ecosystem, including generators and storage devices. This paper redesigns a previously presented EMS to reduce its computational complexity, implement it on a Raspberry Pi, and make it compatible with the IoT paradigm. The EMS manages the power flows between smart home loads, renewable generators, electrical storage, and power grid. It communicates with a network of wireless sensors for electrical appliances and with a cloud-based utility data aggregator. The EMS uses Artificial Intelligence and a Dynamic Programming algorithm to fulfill two objectives at the same time: lowering the end user’s electricity bill and reducing the uncertainty on the power exchanged between the end user and the grid manager. The latter goal is obtained by an effective compensation of forecasting errors. A test bench emulating four smart homes was used to measure the effectiveness of the EMS and the efficiency of the proposed implementation. The results show an uncertainty of the aggregated exchanged power of only 2.88% and a reduction of the electrical bill for end-users of up to 3.23%. Furthermore, the EMS can complete its most onerous task in less than 9 min. The good performance of the proposed EMS makes it a candidate for fast adoption by the market.
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Gupta, Jayshree. "Flood Guard: A Holistic Approach with IoT and AI Technologies." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1005–10. http://dx.doi.org/10.22214/ijraset.2024.61979.
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Abstract: The frequency and severity of floods have been increasing due to climate change, posing significant risks to both human lives and infrastructure. In response to this challenge, Flood Guard presents a comprehensive solution leveraging Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The core of the system relies on a combination of pretrained YOLO (You Only Look Once) models for person detection and TensorFlow for flood detection within video frames. This enables the system to identify individuals in flood-affected areas, aiding in rescue and evacuation efforts. Moreover, the integration of YOLO object detection facilitates the precise counting of individuals, enhancing the efficiency of response teams. In addition to software components, Flood Guard incorporates a range of hardware devices including ultrasonic sensors, humidity sensors, pressure sensors, water flow sensors, temperature sensors, and a Raspberry Pi-based system. These sensors collectively monitor various environmental parameters such as water level, temperature, humidity, and pressure, providing realtime data crucial for flood prediction and early warning systems. The integration of Raspberry Pi with these sensors enables localized data collection and processing, enhancing the system's scalability and adaptability. Furthermore, Flood Guard incorporates a user-friendly interface accessible via an online platform, where stakeholders can visualize real-time sensor data through graphs, enabling informed decision-making. Additionally, the system utilizes Telegram for instant alerts, notifying authorities and civilians about impending flood events and their respective locations. Overall, Flood Guard offers a proactive approach to flood management by combining advanced AI algorithms, IoT devices, and real-time data visualization. By providing early warnings, precise detection of individuals in flood-prone areas, and comprehensive sensor data analysis, Flood Guard aims to mitigate the impact of floods and enhance the resilience of communities facing this growing threat.
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Subiyantoro, Eko, and Abdul Munif. "Pengembangan Teaching aidss Universal Internet of Things Sistem Berbasis Revolusi Industri 4.0 Untuk Meningkatan Kompetensi Guru SMK Bidang Keahlian Teknologi Informasi." Jurnal Kewidyaiswaraan 7, no. 1 (2022): 237–45. http://dx.doi.org/10.56971/jwi.v7i1.204.
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Revolusi Industri 4.0 sudah dimanfaatkan berbagai konvergensi antara teknologi informasi dan teknologi operasional yang dapat menghasilkan digitalisasi sistem produksi (digital twin), dan dimanfaatkannya teknologi komputasi awan (cloud computing) yang dikombinasikan dengan teknologi kecerdasan buatan (artificial intelligence), Internet of Things (IoT), dan big data sehingga dimungkingkan terbentuknya Cyber-Physical System (CPS) dan industri cerdas (smart factory). Sayangnya tidak semua Sekolah Menegah Kejuruan (SMK) mampu menyediakan media atau alat bantu ajar atau Teaching aidss untuk mendukung proses pembelajaran yang langsung praktikum pada R.I 4.0. Padahal pendidikan vokasi diminta untuk mengimplementasikan model pembelajaran Project Based Learning (PjBL) guna menjawab persoalan terkait kebutuhan tenaga kerja saat ini. Teaching aidss Universal IoT System (UnIoTSys) dimaksudkan untuk membuat miniatur dan IoT dalam satu perangkat yang multifungsi. UnIoTSys merupakan teaching aidss yang dapat digunakan dalam proses transfer pengetahuan dan teknologi terdiri dari dua bagian utama hardware dan software. Hardware merupakan module-module yang saling terintergrasi dan fleksibel dalam penggunaanya (plug and play) yang terdiri dari SBC Raspberry Pi, LCD hdmi, module sensor, module relay, terminal AC output 220, projectboard, dan komponen-komponen elektronika. Software merupakan perangkat lunak open source GNU General Public License (GPL) yang terdiri dari sistem operasi raspbian, sd card formatter, python up to version 3, flask webserver, dan library-library lain. Hasil pelatihan Guru-Guru SMK bidang keahlian teknologi informasi dengan memanfaatkan teaching aidss UnIoTSys dalam tiga angkatan pelatihan memperoleh hasil rerata sangat baik 92,77% dan dibuktikan dengan pembuatan projek-projek yang berbasis Revolusi Industri 4.0.
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Ko, Jungbeom, Hyunchul Kim, and Jungsuk Kim. "Real-Time Sound Source Localization for Low-Power IoT Devices Based on Multi-Stream CNN." Sensors 22, no. 12 (2022): 4650. http://dx.doi.org/10.3390/s22124650.
Full textAbstract:
Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users cannot avoid touching them for face-to-face interactions, which contradicts the voice-activated AI philosophy. In this paper, we propose a deep neural network-based real-time sound source localization (SSL) model for low-power internet of things (IoT) devices based on microphone arrays and present a prototype implemented on actual IoT devices. The proposed SSL model delivers multi-channel acoustic data to parallel convolutional neural network layers in the form of multiple streams to capture the unique delay patterns for the low-, mid-, and high-frequency ranges, and estimates the fine and coarse location of voices. The model adapted in this study achieved an accuracy of 91.41% on fine location estimation and a direction of arrival error of 7.43° on noisy data. It achieved a processing time of 7.811 ms per 40 ms samples on the Raspberry Pi 4B. The proposed model can be applied to a camera-based humanoid robot that mimics the manner in which humans react to trigger voices in crowded environments.
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Cornetta, Gianluca, and Abdellah Touhafi. "Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices." Electronics 10, no. 5 (2021): 600. http://dx.doi.org/10.3390/electronics10050600.
Full textAbstract:
Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.
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Lightbody, Dominic, Duc-Minh Ngo, Andriy Temko, Colin C. Murphy, and Emanuel Popovici. "Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection." Future Internet 16, no. 3 (2024): 88. http://dx.doi.org/10.3390/fi16030088.
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The growth of the Internet of Things (IoT) has led to a significant rise in cyber attacks and an expanded attack surface for the average consumer. In order to protect consumers and infrastructure, research into detecting malicious IoT activity must be of the highest priority. Security research in this area has two key issues: the lack of datasets for training artificial intelligence (AI)-based intrusion detection models and the fact that most existing datasets concentrate only on one type of network traffic. Thus, this study introduces Dragon_Pi, an intrusion detection dataset designed for IoT devices based on side-channel power consumption data. Dragon_Pi comprises a collection of normal and under-attack power consumption traces from separate testbeds featuring a DragonBoard 410c and a Raspberry Pi. Dragon_Slice is trained on this dataset; it is an unsupervised convolutional autoencoder (CAE) trained exclusively on held-out normal slices from Dragon_Pi for anomaly detection. The Dragon_Slice network has two iterations in this study. The original achieves 0.78 AUC without post-processing and 0.876 AUC with post-processing. A second iteration of Dragon_Slice, utilising dropout to further impede the CAE’s ability to reconstruct anomalies, outperforms the original network with a raw AUC of 0.764 and a post-processed AUC of 0.89.
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Ramakrishnam Raju, S. V. S., Bhasker Dappuri, P. Ravi Kiran Varma, Murali Yachamaneni, D. Marlene Grace Verghese, and Manoj Kumar Mishra. "Design and Implementation of Smart Hydroponics Farming Using IoT-Based AI Controller with Mobile Application System." Journal of Nanomaterials 2022 (July 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/4435591.
Full textAbstract:
Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil-based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence-based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI-SHES with Internet of Things (IoT) environment. The proposed AI-SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real-time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android-based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI-SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI-SHES with IoT disclose superior performance in terms of accuracy, F-measure with 99.29%, and 99.23%, respectively.
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Qayyum, Faiza, Harun Jamil, Naeem Iqbal, and Do-Hyeun Kim. "IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid." Smart Cities 6, no. 5 (2023): 2196–220. http://dx.doi.org/10.3390/smartcities6050101.
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The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.
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D’Agostino, Pietro, Massimo Violante, and Gianpaolo Macario. "A Scalable Fog Computing Solution for Industrial Predictive Maintenance and Customization." Electronics 14, no. 1 (2024): 24. https://doi.org/10.3390/electronics14010024.
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This study presents a predictive maintenance system designed for industrial Internet of Things (IoT) environments, focusing on resource efficiency and adaptability. The system utilizes Nicla Sense ME sensors, a Raspberry Pi-based concentrator for real-time monitoring, and a Long Short-Term Memory (LSTM) machine-learning model for predictive analysis. Notably, the LSTM algorithm is an example of how the system’s sandbox environment can be used, allowing external users to easily integrate custom models without altering the core platform. In the laboratory, the system achieved a Root Mean Squared Error (RMSE) of 0.0156, with high accuracy across all sensors, detecting intentional anomalies with a 99.81% accuracy rate. In the real-world phase, the system maintained robust performance, with sensors recording a maximum Mean Absolute Error (MAE) of 0.1821, an R-squared value of 0.8898, and a Mean Absolute Percentage Error (MAPE) of 0.72%, demonstrating precision even in the presence of environmental interferences. Additionally, the architecture supports scalability, accommodating up to 64 sensor nodes without compromising performance. The sandbox environment enhances the platform’s versatility, enabling customization for diverse industrial applications. The results highlight the significant benefits of predictive maintenance in industrial contexts, including reduced downtime, optimized resource use, and improved operational efficiency. These findings underscore the potential of integrating Artificial Intelligence (AI) driven predictive maintenance into constrained environments, offering a reliable solution for dynamic, real-time industrial operations.
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Adel, Ahmed, Ahmed Goda, Mostafa Sadek, and Mohammed Salama. "Enhancing Accessibility and Independence of Visually Impaired Individuals through AI, ML and IoT: The Development of a Smart Robot Assistant." International Uni-Scientific Research Journal 4 (2023): 61–76. http://dx.doi.org/10.59271/s45078.023.2301.11.
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This paper presents the development of a smart robot assistant designed to enhance accessibility and independence for visually impaired individuals by leveraging Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies. The system incorporates various sensors, including cameras, microphones, and distance sensors, with Raspberry Pi 4 and Nvidia RTX 3050 ti hardware to provide features such as voice recognition, obstacle detection, and navigation. The software is programmed using Python version 3.9.16 and utilizes important libraries such as Tensorflow, OpenCV, and PyAudio. Our study shows that the smart robot assistant can offer numerous benefits, including increased mobility, safety, and independence, by recognizing and responding to voice commands, identifying obstacles and avoiding collisions, and providing audio feedback on its location and surroundings. However, successful adoption requires addressing several challenges, including improving the accuracy and reliability of obstacle detection, ensuring privacy and security, and reducing costs. The propose strategies to overcome these challenges, such as leveraging AI and ML technologies, collaborating with stakeholders, and promoting regulatory frameworks. In conclusion, this paper highlights the potential of AI, ML, and IoT technologies in developing smart robots to enhance the accessibility and independence of visually impaired individuals. By addressing the aforementioned challenges and incorporating user feedback, these systems have the potential to significantly improve the quality of life for this population.
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SM, Priyanka, Sharath GK, V. Ganesh, and Varun AV. "Density Based Traffic Management System for Smart Cities." Journal of Image Processing and Image Restoration 1, no. 1 (2023): 1–5. http://dx.doi.org/10.48001/joipir.2023.111-5.
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In most cities throughout the world, traffic congestion is a serious issue that has turned into a nightmare for the locals. It is brought on by signal delay, improper traffic signal timing, etc. Traffic does not affect the hard-coded traffic light delay, which is independent of it. Consequently, there is an increasing need for systematic quick automatic systems to optimise traffic control. This essay is intended to Upon detecting the level of traffic at the intersection, the signal time adjusts automatically. This work makes use of a Raspberry Pi microcontroller. Modern technology is being used to optimise traffic flow in cities, including the density-based traffic management system for smart cities. This technology uses a variety of sensors and cameras to gather real-time traffic information that is then analysed to spot congested regions and forecast future traffic trends. This data is used by the system to dynamically change traffic signal timings, redirect traffic to less crowded routes, and give drivers and pedestrians real-time information via mobile apps and electronic signage. This system offers a more effective, safe, and sustainable solution to managing urban traffic by utilising cutting-edge technologies like artificial intelligence, machine learning, and the Internet of Things (IoT). This system decreases travel times and carbon emissions while enhancing the quality of life for city dwellers.
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Lin, Shiu-Shin, Kai-Yang Zhu, Xian-Hao Zhang, Yi-Chuan Liu, and Chen-Yu Wang. "Development of a Microservice-Based Storm Sewer Simulation System with IoT Devices for Early Warning in Urban Areas." Smart Cities 6, no. 6 (2023): 3411–26. http://dx.doi.org/10.3390/smartcities6060151.
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This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.
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Noh, Sun-Kuk. "Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet." Computational Intelligence and Neuroscience 2021 (March 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/5544784.
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Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers’ health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.
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Ahmed, H. Eldeeb, Mohamed Abdelsalam Ahmed, M. Shehata Ahmed, Sayed Kamel Ali Hesham, and Fouad Sara. "Health monitoring of historic buildings using machine learning in real-time internet of things (IoT)." Health monitoring of historic buildings using machine learning in real-time internet of things (IoT) 32, no. 2 (2023): 725–33. https://doi.org/10.11591/ijeecs.v32.i2.pp725-733.
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In this paper, structural health monitoring (SHM) is used to detect the damage level for the historic building. The damaged level is defined based on the support vector machine (SVM) algorithm to extract the damage feature. Physical checks allow us to detect any damage or structural degeneration. Supervised training machine learning (ML) is used as a tool to examine accelerometer data to ascertain the condition of structures following an occurrence. The three training models, the SVM, the random forest linear classification, and the k-nearest neighbor (KNN) model are tested and compared to classify data. The data obtained from structural health monitoring, teams of responders, and investigators can be used to manage the most vulnerable structures. The accuracy of the SVM algorithm was found up to 94% accurate and precise, at a high level. The internet of things (IoT) architecture is also introduced with SVM learning algorithms for early warning. The proposed system makes use of an SHM system to identify seismic events or accelerations. The IoT system SHM uses real data from the structure, allowing for online damage identification and ongoing monitoring. A dashboard is used to represent the monitoring data and the damage level.