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Design and Development of IoT Smart Driving Protocol for Unmanned Ground Vehicles

Design and Development of IoT Smart Driving Protocol for Unm...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Santhosh Kumar Seelam S Nagakishore Bhavanam Vasujadevi Midasala Edara Sreenivasa Reddy Department of Computer Science and Engineering University College of Engineering and Technology Acharya Nagarjuna University Guntur Andhra Pradesh India Department of Computer Science and Engineering Managalayatan University-Jabalpur (MUJ) Barela Jabalpur Madhya Pradesh India Department of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

UGVs have enormous potential in industries such as defense, agriculture, and autonomous transportation. Efficiency and effectiveness can be achieved by UGVs through the sophistication of their control systems, mainly with regard to navigation and decision-making. In this study, a new IoT -based smart direction control system is proposed for UGVs. the system presented herein integrates state-of-the-art sensor fusion techniques against the backdrop of a robust and adaptive communication framework for enhanced navigation accuracy and reduced latency, with the overall goal of improving system efficiency. Equipped with GPS, IMU, and LiDAR sensors, our system provides accurate environmental perception and real-time adaptability to various terrains. the heart of this device is a high-performance, military-grade microcontroller, which ensures the reliability and handling of numerous data streams simultaneously with minimal latency. On the software side, modular design makes use of edge computing and machine learning algorithms to facilitate adaptive control and real-time decision-making. the performance was verified by rigorous testing in simulated environments and real-world field trials. the results show an improvement in the navigation accuracy and system responsiveness compared to previous solutions. A comparative deep analysis shows the superiority of our approach to hardware design, software integration, and prototype testing.

关键词： Technological innovation Irrigation Accuracy Navigation Microcontrollers Software Real-time systems Blockchains Autonomous vehicles Testing

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： N. Venkateswaran Ramprabu J Ramaprabha Marimuthu Nagella Venkata Ramana Mahesh Kotha Balasubbareddy Mallala Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Management Studies Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering (AIML) CMR Technical Campus Hyderabad Telangana India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

With the improvement in EV's popularity, controlling the load demand of EVCS's has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. the PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). the proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management.

关键词： Training Accuracy Demand side management Scalability Transportation Electric vehicle charging Mathematical models Convolutional neural networks Vehicle dynamics Genetic algorithms

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Coordinated Planning and control of Two Mobile Robots for Joint Cargo Transportation

Coordinated Planning and Control of Two Mobile Robots for Jo...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Borna Paro Jelena Rogina Luka Petrović Marija Seder Ivan Marković Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Automated cargo transport is a frequent task in intralogistics and industrial scenarios, but is usually carried out by a single robot, thus limited to its payload capabilities. In this paper, we present a method for coordinated motion planning and control of two autonomous mobile robots for the joint transport of cargo that is too large for a single robot. We consider the two robots with a large cargo on top of them as an any-shape single kinematic chain with cargo. We plan an optimal collision free path for the entire differential drive kinematic chain that ensures the transport of the cargo to the desired target location. the implemented algorithm sends control commands to each robot separately, which achieves the coordinated execution of the planned path. Finally, the proposed method is validated in simulated and real-world scenarios.

关键词： Service robots Robot kinematics Measurement by laser beam Transportation Kinematics Path planning Planning Mobile robots Collision avoidance Autonomous robots

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A Low-Cost Automatic DEF Refueling controller for Diesel Generators 25th

A Low-Cost Automatic DEF Refueling Controller for Diesel Gen...

引用

25th international conference on Internet computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer science, Computer engineering and Applied computing, CSCE 2024

作者： Mosuli, Dania Koc, Hakduran College of Science and Engineering University of Houston-Clear Lake 2700 Bay Area Blvd HoustonTX77058 United States

ISBN: (纸本)9783031859229

this paper presents a design and implementation of a low-cost automatic controller for Diesel Exhaust Fluid (DEF) tanks. the controller monitors error codes from the generator’s Engine control Unit (ECU) and readings from a temperature and level sensors and refuels the generator’s DEF tank when needed. the proposed controller presents a low-cost and easy to install solution for DEF refueling automation;eliminating cumbersome refueling process that requires constant user monitoring. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： controller Diesel Exhaust Fluid (DEF) tank generator low cost low power TM4c123

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Deep Learning Based Air Quality Forecasting Using Satellite Data

Deep Learning Based Air Quality Forecasting Using Satellite ...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： K Navya Sree Mancharla Rakesh Reddy Saraswathi. S Department of Computer science and engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. this research focuses on the development of an advanced air quality forecasting system using machine learning techniques. the system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. the model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. the system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. this research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies.

关键词： Accuracy Atmospheric measurements Prevention and mitigation Urban areas Weather forecasting Air pollution Pollution measurement Forecasting Public healthcare Diseases

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Fuzzy Lateral control for Unmanned Vehicles: Addressing Probabilistic Packet Loss Issue Under Random Sampling 8th

Fuzzy Lateral Control for Unmanned Vehicles: Addressing Pro...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Hu, Aogui Cao, Zhiru Peng, Chen Wu, Jiancun School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this paper, the fuzzy control problem of unmanned vehicles is studied. In the unmanned vehicle systems, the lateral offset, yaw rate and heading offset are sampled and sent to60;the control center, and may unfor... 详细信息

ISBN: (纸本)9789819602933

In this paper, the fuzzy control problem of unmanned vehicles is studied. In the unmanned vehicle systems, the lateral offset, yaw rate and heading offset are sampled and sent to the control center, and may unfortunately be dropped due to transmission conflicts. To solve the packet loss issues, a fuzzy model is first established to approximate the nonlinear lateral dynamics of the unmanned vehicles. the measured signals are assumed to be sampled in a random way, induced by which, the phenomenon of probabilistic packet loss is considered. then, a method of input delay considering double stochasticity delay is used to model a probability distribution of the packet loss problem under stochastic sampling. Furthermore, relaxed sufficient conditions are given to ensure the robust mean square exponential stability and H∞ performance of the closed-loop system. Finally, the effectiveness of the lateral controller design is verified via a simulation example. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Packet loss

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects

Boosting Software Quality: A Machine Learning Technique for ...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Akilan E. Daniel Alwin Infant P Jainish G R Computer science & Engineering Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Computer Science and Engineering Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. this work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. the objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. these indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. these algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. this approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges.

关键词： Computational modeling Software algorithms Software quality Predictive models Prediction algorithms Data models Software reliability Decision trees Software measurement Embedded software

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A Hybrid Approach for Cardiovascular Disease Risk Evaluation

A Hybrid Approach for Cardiovascular Disease Risk Evaluation

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： U. Sesadri Shaik Jumlesha S.Hrushikesava Raju S. Adinarayna Nabanita Choudhury Vijaya Chandra Jadala Department of CSE Vardhaman College of Engineering Hyderabad Telangana Department of CSE Annamacharya Institute of Technology and Science Tirupati Chittoor(dt) AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam Faculty of Computer Technology Assam down town University Guwahati Assam School of Computer Science and Artificial Intelligence SR University Hanumakonda Warangal Telangana India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. there were specific tools to determine the risk of cardiovascular disease. Among many diseases, the mortality rate of the people is increasing due to a lack of quality lifestyle. In order to provide a guide to the individual based on their specific characteristics and lifestyle, would classify the risk into specific categories like severe (High), Moderate, and normal(Low) with pre-defined threshold constraints. the risk assessment estimators alert the individual to control certain modifiable attributes irrespective of their non-modifiable elements. the integration of random forests and intelligent rule mining would generate the CVD risk prediction. Efficiency as well as interpretability are important in this competitive hybrid model. the ultimate benefits assured are extending the lives of the patients by knowing the importance of daily work, habits, and their control food, presenting a powerful opportunity by practicing better personalization monitoring and improved patient care, and improved informed decisions for immediate action. the early prevention initiation by identifying the risk severity optimizes follow-up of better practices and treatment styles, resulting in improved decision making for better health care of the individual.

关键词： Measurement Calculators Prevention and mitigation Mortality Medical services Cardiovascular diseases Informatics Random forests Monitoring Mobile computing

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Event-Driven Quantized control of DC Microgrids Under Hybrid Attacks 8th

Event-Driven Quantized Control of DC Microgrids Under Hybri...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Li, Fuqiang Gao, Lisai Li, Zhe Li, Kang Peng, Chen College of Sciences Henan Agricultural University Zhengzhou450002 China School of Electronic and Electrical Engineering University of Leeds LeedsLS29JT United Kingdom School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9789819602247

this paper investigates the quantized fuzzy control of nonlinear DC microgrids subjected to hybrid attacks, taking into the consideration of resilient event-triggered mechanism (ETM), quantization and premise mismatching. First, the attack information is integrated into a new resilient ETM mechanism, which can save network bandwidth as well as actively exclude attack-induced dropouts. Second, a fuzzy switched system model is established, which offers a unified platform to investigate the influence of a range of contributing factors. third, using a piecewise Lyapunov functional, criteria for mean-square exponential stability are obtained, and sufficient conditions for controller design are derived. Finally, simulation results confirm the effectiveness of the proposed methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microgrids

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance control

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Sohum Kabra Prithvi Nadagouda Yash Nikam Manoj S. Kavedia Department of Electronics and Telecommunications Engineering (EXTC) Thadomal Shahani Engineering College Mumbai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices.

关键词： Temperature sensors Temperature measurement Home appliances Analytical models Voltage measurement Machine learning Logic gates Predictive models Real-time systems Intelligent sensors

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