Relevant bibliographies by topics / Artificial intelligence · GPS · Internet of things · Raspberry Pi

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Artificial intelligence · GPS · Internet of things · Raspberry Pi'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Artificial intelligence · GPS · Internet of things · Raspberry Pi"

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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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Dissertations / Theses on the topic "Artificial intelligence · GPS · Internet of things · Raspberry Pi"

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Riffelli, Stefano. "Sustainable comfort in indoor environments: global comfort indices and virtual sensors." Doctoral thesis, Urbino, 2022. http://hdl.handle.net/11576/2700929.

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Book chapters on the topic "Artificial intelligence · GPS · Internet of things · Raspberry Pi"

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Upadhyay, Divya, and Ayushi Agarwal. "Smart Bin." In Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4991-2.ch009.

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The 21st century is the era of the digital world and advanced technologies. This chapter contributes to the Swachh Bharat mission by presenting the concept of smart bin using IoT. The Smart bin presented in this chapter is GPS-enabled and comprises sensors and a camera. A prototype for the proposed model is analysed, and network architecture is designed to communicate the critical information. The proposed system will update the status and condition of the bin to the nearest authority to improve the city's pollution and cleanliness. The prototype is deployed using a microcontroller Raspberry Pi and Google Maps to obtain the bins' real-time location. IoT fill-level sensors will help the garbage carrying truck in identify the nearest empty container without wasting time and resources. Google Maps will help in sensing the optimised routes to the drivers. The microcontroller will be used to integrate the different devices and cameras to provide real-time bin collection, overflowing/under flowing state and tracking information, and suggestions and notifications for effective disposal.
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Rachalwar, Om. "OVERVIEW OF ARTIFICIAL INTELLIGENCE ON EDGE DEVICES." In Futuristic Trends in Artificial Intelligence Volume 3 Book 3. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bkai3p2ch4.

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Artificial Intelligence (AI) is one of the most important applications in the field of Internet of Things (IoT) devices. This will focus on the development of AI on edge devices and their ability to develop an AI model. The study will be a review of different resources available on the topic with a touch of some personal conclusions drawn through Raspberry Pi 3 and Raspberry Pi 4. A conclusion can be drawn that edge devices are easy to use and even easier to program with the preferred tasks but they will not produce results like their traditional computing counterparts due to their size and processing power. The results obtained from Edge devices are satisfactory and since they are smaller in size and more accessible, they also increase the use cases of AI. The chapter will also focus on different problem statements like object detection and try to test the same on some of the hardware categories and determine the performance of the model and draw a conclusion on which hardware will perform best for developing certain AI models and which will create optimal results.
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Femilda Josephin J. S., Ferni Ukrit M., Alice Nithya A., Arindam Gogoi, and Vanshika Dewangan. "Autonomous Crop Care System Using Internet of Things." In Edge Computing and Computational Intelligence Paradigms for the IoT. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8555-8.ch015.

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In today's world, the quality of the crops is of utmost importance. Crops need to be effectively cared for, and steps are needed to ensure their healthy growth. Smart Irrigation is a major topic that has been implemented in certain regions, but the accumulation of various sensors is the key to the effective safety of crops. In the chapter, various sensors are being deployed and used in synchronization. The primary ones included in the system are the water level and moisture sensor, which works in correspondence with the water motor; the proximity (PIR) sensor, which works in accordance with the buzzer and the webcam; and finally, the light-dependent resistor (LDR), which works in relation with the artificial light. The analog data received from the sensors are transmitted to the raspberry-pi and then sent over the network using a Wi-Fi module to Ubidots, where the data will be analyzed, and necessary actions will be taken. The components to be used in the system will guarantee overall prolific, scalable, and ardent implementation.
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Werapun Warodom, Kamhang Amatawit, and Wachiraphan Aekawat. "Design Of Home Automation Framework With Social Network Integration." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-503-6-219.

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In present days, home devices with network capabilities are widely used and augmented for delivering several Home Services (HS) to more end users. The technology integration offers new and exciting opportunities to increase the device connectivity within a home for many proposals of home automation. This paper is designed and implemented for a cost effective, flexible, manageable home automation system. The proposed system consists of three main components: a device control using a microcontroller (e.g., Raspberry PI with control circuit), a notification system (Social network integration) and a user control from anywhere, any time and any device. This system can support a wide range of home appliances connecting through the network as the Internet of Things (IoT) concept. A user is able to easily control home automation system by several ubiquitous devices. Air conditioners and fans are sample devices to be controlled by our system in order to demonstrate the simplicity and effectiveness that will save usage energy.
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Conference papers on the topic "Artificial intelligence · GPS · Internet of things · Raspberry Pi"

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Zhang, Xihua. "The Design and Implementation of a Campus Autonomous Delivery Robot Based on Raspberry Pi." In 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI). IEEE, 2024. http://dx.doi.org/10.1109/iotaai62601.2024.10692819.

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Kumar, Anuj, and Velagapudi Vasu. "Real-Time Monitoring of Shop Floor Machines with Open Sources Technologies: A Case Study." In 3rd International Conference on Recent Advances in Materials and Manufacturing Technologies. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-rqefv9.

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Over the past few years, the development in the field of Internet of Things technologies, Big Data, and artificial intelligence has helped industries to digitalize the shop floor machine and enable the real-time monitoring and storing of data in the cloud. Real-time monitoring of shop floor machines plays a crucial role in manufacturing industries to maintain their machines, reduce downtime, and increase operational efficiency. This study presents a cost-effective solution for real-time monitoring of shop floor machines using open-source technologies, including Raspberry Pi as an edge computing device, Node-RED as a Gateway, and ESP-32 as a microcontroller. The proposed solution demonstrates the potential to revolutionize machine monitoring in manufacturing environments, offering preventive maintenance, and optimizing operational efficiency. The system gathers information from PLC with NODE-RED and various sensors such as SCT-13, ZMPT101B, and MPU 6050, installed on the shop floor machine. These sensors collect real-time data that reflect the health, performance, and power consumption of machines. The real-time sensor data also storing in the cloud as well as a local database for further analysis. Our research bridges the gap between edge computing and cloud-based monitoring, offering practical benefits to manufacturing industries, widespread adoption of our approach promises more efficient processes and resource utilization.
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Katkar, Gowthami Vinod, A. Mary Posonia, D. Ushanandini, K. Abirami, and B. Ankayarkanni. "Google pi using raspberry pi for visually impaired." In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574549.

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Chen, Shanhu, Danchun Li, Junneng Liu, and Runkai Wei. "Raspberry Pi based intelligent greenhouse Internet of Things platform." In IoTAAI 2023: 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence. ACM, 2023. http://dx.doi.org/10.1145/3653081.3653213.

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Khan, Iliyas, Hema Kumar Goru, G. L. Madhumati, Chandaluri Yaswanth Reddy, and Boni Pavan Kumar. "Voice-Activated Learning Framework Using Raspberry Pi for Children’s Education." In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574612.

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Sathya, M., S. Sivananda, R. Mekala, R. Chinnaiyan, C. Prathap, and R. Muthulakshmi. "Raspberry Pi-Based Smart Energy Meter Using Internet of Things with Artificial Intelligence." In 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS). IEEE, 2023. http://dx.doi.org/10.1109/iccams60113.2023.10525946.

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Yusro, Muhammad, Wisnu Djatmiko, Cahya Ade Putra Kirana, Dhani Rohmadon, Rosyid Ridlo Al-Hakim, and Agung Pangestu. "Mo-SSeS: A Motorcycle Smart Security System Using Raspberry Pi Based on the Internet of Things." In 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2024. http://dx.doi.org/10.1109/aims61812.2024.10512991.

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Sharma, Prachi, Parshwanath V. Jain, Dhruv Akar, and Ahzam Afaq. "LoRa-Enabled NodeMCU Nodes for Efficient Agricultural Monitoring in IoT: Integration with Raspberry Pi Web Application." In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT). IEEE, 2024. http://dx.doi.org/10.1109/aiiot58432.2024.10574650.

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Chen, Ivy, and Ang Li. "An Intelligent Lock System to Improve Learning Efficiency using Artificial Intelligence and Internet of Things." In 3rd International Conference on Artificial Intelligence and Machine Learning (CAIML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121204.

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According to recent statistics, 75.4% of people with access to the internet are addicted to their phones. 78 percent of teenagers check their mobile devices at least hourly [2]. The purpose of this paper is to propose a tool that lowers users’ dependence on their electronic devices. The tool named Phone Cage is created with the aim of locking electronic device for a set period of time. The application involves the user setting a specific mobile application for a specified amount of time. The phone cage provides the user a display countdown of the remaining time frame through which the locked application is inaccessible. The app provides access only when the set timer reaches the zero mark. This tool is created using Tinker cad, 3D- printer, Thunkable, Firebase console, and Raspberry Pi Zero. This will act as perfect remedy for individuals with addiction to their phones. It will also be a way for parents to control their children’s use of mobile phones. Therefore, noting that a significant number of people lack selfcontrol when it comes to cell phone usage, the cage will be of great help. The project will therefore have great impact to the community by allowing families to spend more time together and not on their phones. It will also help adults place more focus on their jobs and not on their phones.
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