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artificial intelligence For Internet of Things (AIIoT), International Conference on

作者： K. Manaswitha Titus Issac Salaja Silas J Sebastian Terance Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications.

关键词： Location awareness Measurement Navigation Autonomous aerial vehicles Routing Resource management Internet of Things

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artificial intelligence Meets Tactical Autonomy: Challenges and Perspectives

Artificial Intelligence Meets Tactical Autonomy: Challenges ...

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IEEE International Conference on Cognitive machine intelligence (CogMI)

作者： Danda B. Rawat Department of Electrical Engineering and Computer Science (EECS) DoD Center of Excellence in Artificial Intelligence & Machine Learning Howard University Washington DC 20059 USA

artificial intelligence (AI) enabled systems have shown tremendous impact in our national defense and in our society due to recent advances in artificial neural networks, deep learning, machine learning, and Internet of Things, big data, computing and communications. New AI capabilities can improve efficiency, trust, and efficacy for mission critical applications for tactical autonomy with minimal supervision from human operators in multi-domain battlefield (MDB) environments that are complex, contested and unpredictable. Although AI-enabled tools have been responsive to people and complementary to human capabilities, in order to realize its full potential in tactical applications, there are several challenges to be addressed for making trustworthy, ethical, fair, real-time explainable AI-enabled autonomous systems. Collaborations between platforms/systems as well as joint human-machine learning/teaming could address many of these issues to provide trusted and shared understanding and delivering cost-effective and adaptive systems to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed using a shared set of resources. In this paper, we present some challenges and perspectives for AI enabled tactical autonomy.

关键词： Deep learning Ethics Mission critical systems Collaboration Big Data Real-time systems Internet of Things

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A Hybrid Deep learning Model for Optimizing Electric Vehicle Battery and Navigation Systems

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： R. Arul Jose P. Ebby Darney R. Santhana Krishnan J. Relin Francis Raj D. Jansi Rani P. Sundaravadivel Department of Electrical and Electronics Engineering Rajarajeswari College of Engineering Bengaluru India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. Data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency.

关键词： Deep learning Training Computational modeling Battery management systems Real-time systems Data models Batteries State of charge Optimization Long short term memory

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Robust Decentralized Federated learning Using Collaborative Decisions

Robust Decentralized Federated Learning Using Collaborative ...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： A. Gouissem K. Abualsaud E. Yaacoub T. Khattab M. Guizani Computer Science and Engineering Qatar University Qatar Electrical Engineering Qatar University Qatar Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence UAE

Federated learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset.

关键词： Training Wireless communication Data privacy Databases Simulation Collaboration Electrocardiography

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Understanding Cross-Model Perceptual Invariances Through Ensemble Metamers

arXiv

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arXiv 2025年

作者： Lukas, Lukas Mueller, Jonas Leo Loeffler, Christoffer Schwinn, Leo Eskofier, Bjoern Zanca, Dario Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander University Erlangen Germany School of Informatics Pontificia Universidad Católica de Valparaíso Valparaiso Chile Data Analytics and Machine Learning Lab Technische Universität München Munich Germany Institute of AI for Health Helmholtz Zentrum München Munich Germany

Understanding the perceptual invariances of artificial neural networks is essential for improving explainability and aligning models with human vision. Metamers—stimuli that are physically distinct yet produce identical neural activations—serve as a valuable tool for investigating these invariances. We introduce a novel approach to metamer generation by leveraging ensembles of artificial neural networks, capturing shared representational subspaces across diverse architectures, including convolutional neural networks and vision transformers. To characterize the properties of the generated metamers, we employ a suite of image-based metrics that assess factors such as semantic fidelity and naturalness. Our findings show that convolutional neural networks generate more recognizable and human-like metamers, while vision transformers produce realistic but less transferable metamers, highlighting the impact of architectural biases on representational invariances. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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machine learning for Wireless Metaverse: Fundamentals, Use Case, and Future Directions

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IEEE Internet of Things Magazine 2025年

作者： Khan, Latif U. Yaqoob, Ibrar Salah, Khaled Hong, Choong Seon Niyato, Dusit Han, Zhu Guizani, Mohsen Abu Dhabi University United Arab Emirates Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning United Arab Emirates Charles Sturt University Artificial Intelligence and Cyber Futures Institute Bathurst 2795 Australia Khalifa University Department of Computer and Information Engineering United Arab Emirates Kyung Hee University Department of Computer Science and Engineering South Korea Nanyang Technological University School of Computer Science and Engineering Singapore University of Houston Electrical and Computer Engineering Department Houston 77004 TX United States

Today’s wireless systems are posing key challenges in terms of quality of service and quality of physical experience. Metaverse has the potential to reshape, transform, and add innovations to the existing wireless systems. A metaverse is a collective virtual open space that can enable wireless systems using digital twins, digital avatars, and interactive experience technologies. machine learning (ML) is indispensable for modeling twins, avatars, and deploying interactive experience technologies. In this paper, we present the role of ML in enabling metaverse-based wireless systems. We discuss key fundamental concepts for advancing ML in the metaverse-based wireless systems. Moreover, we present a case study of deep reinforcement learning for metaverse sensing. Finally, we discuss the future directions along with potential solutions. © 2018 IEEE.

关键词： avatars blockchain digital twins machine learning Metaverse

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A Comprehensive Review on Probable Measures for Addressing Security Limitations in Internet of Drones

A Comprehensive Review on Probable Measures for Addressing S...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Gaurav Gupta C Menaka Padmanabhan P Department of Computer Science and Engineering Tula’s Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Artificial Intelligence & Machine Learning Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

The Internet of Drones (IoD) has garnered significant interest in recent years due to its portable and automated nature and is being utilized across various domains such as the military, search and rescue operations, and entertainment. However, due to the inherently unsecured radio transmission paths in the Internet of Drones (IoD), there are numerous security concerns associated with the data collected, generated, or processed by drones. This paper aims to address the prominent challenges of security and privacy in the IoD. It undertakes a thorough examination of security concerns and corresponding solutions for IoD security. The discussion encompasses the security requirements associated with IoD and highlights the most recent advancements in research pertaining to IoD security. Authentication methods and Blockchain-based schemes are highlighted in this overview of numerous significant security technologies. Through an extensive analysis, we hereby present an examination of the challenges encountered by existing methodologies and propose potential avenues for future research in the field of IoT security. This review highlights the necessity of implementing suitable security measures to tackle IoT security concerns. Furthermore, it emphasizes the importance of developing security solutions that carefully consider the trade-off among the level of security provided and the cost efficiency of implementation.

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Energy Efficiency Optimization in Massive MIMO Systems with Low-Resolution ADCs

Energy Efficiency Optimization in Massive MIMO Systems with ...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Abdulwaheed Musa Abdullateef Ola Adebayo Oluwasijibomi Peace Balogun Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Centre for Artificial Intelligence and Machine Learning Systems Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg South Africa

Massive multiple-input multiple-output (MIMO) is a promising 5G technology, but the high energy consumption from numerous radio components is a key challenge. Prior work has explored higher than 4-bit ADCs, but very low 1-2 bit remain uninvestigated. This paper provides an in-depth examination of the resolution versus power savings and signal quality degradation trade-offs with 1–3-bit analog-to-digital converters (ADCs) in Massive MIMO systems. Mathematical modeling and simulations quantify the exponential reduction in power consumption along with losses in key performance metrics like signal-to-interference-plus-noise ratio (SINR), throughput, and spectral efficiency with lower ADC resolutions stemming from increased quantization noise. Advanced linear detection techniques are shown to effectively recover the signal quality degradation. The analysis provides valuable insights on jointly optimizing ADC configuration and signal processing to maximize overall system efficiency. Energy savings of over 50% are demonstrated with 1-bit ADCs along with the ability to maintain acceptable performance metrics using linear detectors. The practical implementation feasibility is also discussed. This work enables ultra-low-resolution ADC deployments in energy-efficient Massive MIMO systems.

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Incorporation of Deep learning (DL) Techniques to make Intelligent and Adaptable Wireless Communication Systems

Incorporation of Deep Learning (DL) Techniques to make Intel...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Anamika Tiwari Sandeep Kumar A Rengarajan Department of Artificial Intelligence & Machine Learning Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula’s Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

The emergence of Deep learning (DL) was a response to the widespread availability of mobile data and the growing complexity of wireless networks. It is a subfield of machine learning that has been proposed in academic literature. This technology is regarded as a breakthrough in the management of mobile data traffic. Wireless systems generate a significant amount of varied data, which presents computational issues. In order to tackle this problem, a variety of optimization methodologies are being investigated. DL increases wireless communication by enhancing its intelligence and adaptability. This paper offers an overview of the DL platform and several methodologies employed to enable deep learning in wireless communications. Wireless networking involves various disciplines, including networking, routing, and scheduling, all of which incorporate DL. This text encompasses a comprehensive range of topics. The topic encompassed several optimization methodologies and the latest breakthroughs in this domain.

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Development of Internet of Things Circuitry for Intelligent Interactions of Smart Meters with Users

Development of Internet of Things Circuitry for Intelligent ...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Abdulhakeem Oladele Abdulfatai Abdulwaheed Musa Nasir Faruk Department of Electrical & Computer Engineering Kwara State University Malete Nigeria Department of Electrical & Computer Engineering Centre for Artificial Intelligence and Machine Learning Systems Kwara State University Malete Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria

In modern times, people’s fast-paced lifestyles make it challenging to keep track of essential recurring bills such as electricity, water, and gas. This paper proposes a unified smart metering system utilizing the Internet of Things (IoT) technology to address this need. This system comprises two interconnected sub-systems that manage resource consumption. The first subsystem is a unified metering sub-system, while the second is a cloud-controlled sub-system that stores and analyzes data. Both sub-systems communicate wirelessly, and the server is automatically updated as resources are consumed. The design employs an intelligent interaction approach that leverages information and communication technologies to improve user experiences, task performance, and quality of life. The expected outcome is the development of a mobile application that integrates a billing portal for payment processing. The implementation of this solution is expected to enhance resource utilization, promote sustainability practices, and revolutionize the user experience of interacting with utility services.

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