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Multi-Agent systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 ieee.

关键词： Distributed Reinforcement Learning IoT Network Management Multi-Agent systems Resource Optimization

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Natural Language Processing in Geographic Information systems 3

Natural Language Processing in Geographic Information System...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic Information systems (GIS) with Natural Language Processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 ieee.

关键词： Structured Query Language

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Controlling and Performance Evaluation of DC Microgrid Using Adaptive Control Integrated with Blue Whale Optimization 1

Controlling and Performance Evaluation of DC Microgrid Using...

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1st ieee international conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Peddiny, Vijoy Kumar Datta, Brajagopal Banerjee, Abhik National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India

ISBN: (纸本)9798331542108

This paper introduces a new method for regulating and assessing DC microgrid performance: adaptive control combined with the Blue Whale Optimization (BWO) algorithm. Improving DC microgrid efficiency, stability, and dependability under a range of load and generation scenarios is the goal. The adaptive control technique responds to microgrid variations by dynamically adjusting control settings in real-time. These control settings are optimized using the BWO algorithm, which is based on the foraging behavior of blue whales and guarantees optimal system performance. Numerous simulations were run in order to evaluate the suggested system's efficacy. Comparing the results to conventional control approaches, it is clear that there have been significant gains in voltage regulation, power quality, and overall system efficiency. Adaptive control combined with BWO significantly reduces disturbances and increases the microgrid's resistance to errors and variations. This methodology exhibits exceptional flexibility and resilience in handling the varied and sporadic characteristics of sustainable energy resources inside the microgrid. By providing a bio-inspired optimization strategy, the findings expand DC microgrid control methodologies. This work highlights the possibility of using evolutionary algorithms in conjunction with adaptive control to attain the best possible operational performance in contemporary energy systems. The suggested approach paves the way for further study on intelligent control strategies in smart grid applications while also improving the operational reliability of DC microgrids. © 2025 ieee.

关键词： Self adjusting control systems

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embedded System for real-time Traffic Signs Recognition with Augmented reality Implemented in ARM

Embedded System for Real-time Traffic Signs Recognition with...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Amani Chebbah Hana Ben Fredj Chokri Souani Faculté des Sciences de Monastir Université de Monastir Monastir Tunisia Faculty of Science of Monastir Laboratory of Electronics and Micro-electronics Monastir Tunisia Institut Supérieur des Sciences Appliquées et de Technologies de Sousse University of Sousse Tunisia Faculty of Science of Monastir Laboratory of Electronics and Micro-electronics

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Automatic detection and recognition of traffic signs improve driving safety by alerting drivers to hazards they may not perceive. This paper proposes an embedded real-time methodology for traffic sign detection and recognition, enhanced with augmented reality to increase driver vigilance. The algorithm operates in three stages: detection, recognition, and augmented reality merging. Color filtering and shape matching are used to extract Regions Of Interest (ROIs), followed by a Haar cascade classifier for detection and categorization. The recognition stage utilizes the ORB algorithm. Implemented on an ARM platform, the system achieves 96 % accuracy in detection and recognition at 12 fps in real-world conditions.

关键词： Performance evaluation Accuracy Shape Urban areas Merging Feature extraction real-time systems Hazards Classification algorithms Augmented reality

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real-time Crop Health Assessment with MobileNet for Agricultural applications 3

Real-Time Crop Health Assessment with MobileNet for Agricult...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Saidulu, Bukya Pushpalatha, R. Rajvanshi, Saurabh Alsalami, Zaid Singh, K. Ranjith Rani, L. Pattathu Department of Cse CMR Technical Campus Telangana Hyderabad India Department Of Computer Science Tamil Nadu Erode India Uttaranchal University Dehradun India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Kcg College of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331542375

The growth of sustainable and efficient farming systems has necessitated the use of automated crop health monitoring systems. In the current study, a real-time crop health assessment is proposed using a deep learning algorithm called MobileNet based on a lightweight CNN. The method under consideration includes using the camera of a smartphone to take detailed photographs of crops growing in the fields, and with the help of the MobileNet model, distinguish diseases, nutrient deficiencies, and other pathologies. To provide some resistance to variations in environmental circumstances, the current images are preprocessed through resizing, normalization, and augmentation. MobileNet model fine-tuning is made using a richer dataset that includes both healthy and disease crop images The model achieves an accuracy of 94.5% and micro-average of precision, recall, and F1-score of 95.3%, 93.7%, and 94.5% respectively. real-time efficiency tests are performed on disparate mobile devices and show that the system took less than 2 seconds on average to generate results and had a model size of 12 MB, suitable for usage on even minimalresource devices. Moreover, the trained model demonstrates relatively irgendeepstance to image brightness and orientation, which is rather advantageous for its practical application on the agricultural fields. This paper presents a possible practical and effective approach to field use, which will allow for improving precision agriculture, as well as the preservation of the environment and agricultural lands. © 2025 ieee.

关键词： convolutional neural network deep learning disease detection mobile devices MobileNet precision agriculture real-time crop health assessment

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Edge and Fog computing in Cyber-Physical systems

Edge and Fog Computing in Cyber-Physical Systems

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Gangrade, Priyanshi Kushwah, Virendra Singh Date, Saroj S. Jagdale, Archana D. Upreti, Kamal Singh, Vinai K. School of Cse and Ai Vit Bhopal University Sehore India Csmss Chh. Shahu college of Engineering Dept. of Ai and Ds Maharashtra Chh. Sambhajinagar India Barclays Services Corp 745 Seventh Avenue New York United States Department of Computer Science Christ University Delhi Ncr Ghaziabad India Gl Bajaj Group of Institutions Department of Computer Science & Engineering Mathura India

ISBN: (纸本)9798331527495

The benefits of cyber-physical system advances include low latency and high bandwidth data processing in areas such as automotive, healthcare, and business automation. Traditional environments are often located in centralized and remote locations and cannot meet the demand. Edge computing and cloud computing have become fundamental concepts that will bring computing closer to the center of the data. Edge computing can reduce latency and bandwidth consumption by processing data on or near IoT devices. Fog computing adds another layer to this by distributing work and storage across multiple nodes, thus providing a scalable and flexible infrastructure. This article discusses the principles, benefits, and challenges of integrating edge and cloud computing into a CPS environment. It leverages the power of proximity-based edge computing and the centralized capabilities of cloud computing to provide scalable, instantaneous responses to CPS applications or time to optimize services. The demonstration shows a variety of things from smart cities to the use of IoT in healthcare in CPS. The article also covers some specific security and privacy issues and future directions in distributed computing, including the role of AI and 5G, which are supposed to offer additional resources in various applications. © 2025 ieee.

关键词： Data reduction

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real-time Aerial Image Edge Detection with MicroWatt Power ISA on FPGA

Real-Time Aerial Image Edge Detection with MicroWatt Power I...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Prashant V Joshi Sudharshan K M Majunath G Asuti Poornashree S J Sunitha K Research School of ECE REVA University Bengaluru India Department of ECE BNMIT Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This research investigates the implementation of real-time aerial image edge detection using the Canny edge detection algorithm with the MicroWatt Power Instruction Set Architecture (ISA)-Open Core Processor on Field Programmable Gate Arrays (FPGAs). Canny edge detection is particularly advantageous for aerial image processing due to its ability to produce precise and well-defined edges, making it ideal for applications in surveillance, agriculture, and environmental monitoring where detail preservation is critical. The proposed system utilizes the MicroWatt Power ISA-Open core processor to optimize resource utilization, achieving high throughput and low latency while maintaining energy efficiency. The work uses the Xilinx Vivado EDA tool to calculate the power dissipation of the FPGA implementation, demonstrating significant reductions in power consumption compared to traditional processing methods. The experimental results showcase the effectiveness of the Canny edge detector in producing accurate edge maps while minimizing resource usage, thus enabling real-time processing capabilities. This work contributes to the advancement of low-power embedded systems for aerial imaging, highlighting the potential of FPGAs combined with the MicroWatt Power ISA for efficient and effective image analysis.

关键词： Image edge detection Instruction sets Surveillance Imaging Throughput real-time systems Power dissipation Resource management Environmental monitoring Field programmable gate arrays

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Efficient Resource Management for real-time AI systems in the Cloud using Reinforcement Learning

Efficient Resource Management for Real-Time AI Systems in th...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Jagadeesh Vedula Saravanakumar Baskaran Sathyanarayanan Sayiram Rajkumar Muthusamy Ketan Gupta N. Yuvaraj Digital Transformation Houston Texas USA Microsoft Corporation Seattle Washington USA IBM San Jose CA USA NTT Data Irving Texas USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The research utilizes reinforcement learning to enable dynamic resource allocation, ensuring that resources are provisioned on demand rather than relying on static allocation methods defined at design time.

关键词： Cloud computing Costs System performance Reinforcement learning Dynamic scheduling Load management real-time systems Planning Resource management Artificial intelligence

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Waste Sorting System Using AI: Development of An embedded Solution for Efficient Waste Management

Waste Sorting System Using AI: Development of An Embedded So...

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international Image Processing, applications and systems conference (IPAS)

作者： Amal Remili Mouna Zouari Mahdi Jihen Frikha Elleuch Ahmed Fakhfakh Dorra Sellami Electrical Engineering Department National Engineering School of Sfax Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

关键词： Waste management Measurement Computer vision Computational modeling Image processing real-time systems Plastics Artificial intelligence System validation Sorting

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DynKDD: Dynamic Graph-Based Network Intrusion Detection Using Graph Neural Networks 19

DynKDD: Dynamic Graph-Based Network Intrusion Detection Usin...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Mir, Aabid A. Zuhairi, Megat F. Musa, Shahrulniza Namoun, Addallah Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia AI Centre Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia

ISBN: (纸本)9798331507817

The inability to capture the temporal dynamics of network interactions limits traditional intrusion detection systems (IDSs) in detecting sophisticated threats that evolve over time. This research introduces DynKDD, a dynamic graph-based version of the NSL-KDD dataset, designed to capture the temporal relationships between network entities and their interactions. The proposed solution leverages Graph Neural Networks (GNNs) to detect network intrusions by analysing both the spatial and temporal evolution of network traffic. The methodology involves transforming the static NSL-KDD dataset into a series of time-ordered graph snapshots by assigning synthetic timestamps and constructing dynamic graphs. The snapshots represent the evolution of network interactions, enabling advanced dynamic graph learning models to process and detect intrusions. Five state-of-the-art GNN models, Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Sample and Aggregation (GraphSAGE), Graph Isomorphism Networks (GIN) and Graph Auto-Encoders (GAE), were trained and tested on the DynKDD dataset, and their performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Evaluation results demonstrate that GraphSAGE consistently outperforms other models, achieving an accuracy of 95.80% on the DynKDD dataset, a notable improvement over the static NSL-KDD dataset. Other models follow a similar trend with a significant improvement on the constructed DynKDD dataset. The results validate the superiority of dynamic graph-based intrusion detection systems in identifying evolving network threats. With its ability to model evolving cyberattacks, DynKDD holds immense potential for real-world applications in protecting critical infrastructure from advanced and persistent threats, offering a transformative approach to network intrusion detection. © 2025 ieee.

关键词： Network intrusion

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