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International Conference on Computing and networking Technology (ICCNT)

作者： Prajwal Manohar Bhopale B. R. Chandavarkar Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Underwater Wireless Sensor Networks (UWSNs) have received tremendous concern and interest in recent years. They find many applications, from underwater exploration and monitoring to naval and marine operations. However, due to the harsh and challenging aquatic environmental conditions, the networks have to face challenges such as limited bandwidth, long latency, and packet loss, which makes the communication channel unreliable. Numerous approaches have been proposed to enhance reliable data transmission in UWSNs. These approaches range from protocol modification to specialized algorithms tailored to UWSN requirements. This paper explores UWSNs, focusing on their applications, challenges, and reliability protocols. It provides a comparative analysis of different reliability protocols, highlighting their strengths and weaknesses. Furthermore, it examines available simulators and testbeds for UWSNs and their approaches to ensuring reliable data transfer.

关键词： Surveys Wireless sensor networks Protocols Packet loss Focusing Communication channels Data transfer Reliability Monitoring Faces

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Hierarchical Caching for Digital Twins Communications Over mURLLC-Based 6G Distributed-Computing Mobile Networks Using FBC

Hierarchical Caching for Digital Twins Communications Over m...

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International Conference on Distributed Computing Systems Workshop

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350354713

ISBN: (纸本)9798350354720

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements over 6G. Among multiple 6G mURLLC services, digital twins (DT) has been widely envisioned as a major intelligent application to support efficient interactions between physical and virtual objects. Moreover, multi-tier caching, which is one of the key distributed computing techniques, stores the frequently-demanded data items at different wireless network tiers to efficiently reduce mURLLC streaming delay and data move. However, how to efficiently cache mURLLC-based DT data items at different caching tiers of wireless networks and how to statistically upper-bound both delay and error-rate for DT communications remain challenging problems. To overcome these difficulties, in this paper we propose a multi-tier caching mechanism to support DT communications over 6 G mobile networks. First, we propose the DT data adaptive collection scheme applying finite blocklength coding (FBC) to dynamically encode a physical object into its virtual representation according to the current network and wireless channel statuses. Second, we develop inter-tier and intra-tier collaborative caching mechanisms, where DT data items are selectively cached at different wireless network caching tiers according to their popularities including: router tier, massive-multiple-input-multiple-output (MIMO) basestation tier, and mobile device tier. Third, our proposed intertier collaborative caching mechanisms maximize the aggregate $\epsilon$ effective capacity across all three caching tiers, and our proposed intra-tier collaborative caching mechanisms minimize the sum of data transmission delay for all DT data items cached in each caching tier. Finally, we numerically validate and evaluate our developed multi-tier hierarchical caching schemes over 6G DTe

关键词： 6G mobile communication Wireless networks Aggregates Collaboration Distributed databases Quality of service Encoding

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Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

Seed Feature Maps-based CNN Models for LEO Satellite Remote ...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Lu, Zhichao Ding, Chuntao Wang, Shangguang Cheng, Ran Juefei-Xu, Felix Boddeti, Vishnu Naresh Sun Yat-sen University School of Software Engineering Zhuhai China Beijing Jiaotong University School of Computer and Information Technology Beijing China Beijing University of Posts and Telecommunications Key Laboratory of Networking and Switching Technology Beijing China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China New York University New YorkNY United States Michigan State University Department of Computer Science and Engineering East LansingMI United States

ISBN: (纸本)9798350304855

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3 × fewer parameters and 2.2 × fewer FLOPs. © 2023 IEEE.

关键词： Neural network models

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Integrated Sensing, Communications, and Powering for Statistical-QoS Provisioning Over Next-Generation Massive-MIMO Mobile Networks

Integrated Sensing, Communications, and Powering for Statist...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

The integrated sensing and communication (ISAC) has been proposed as a key technique to support the next generation traffic with stringent quality-of-service (QoS) requirements through sensing and communicating using the same radio frequency and hardware. Simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. However, how to integrate ISAC with SWIPT to support the stringent QoS traffic has imposed many new challenges not encountered before. To overcome these challenges, in this paper we propose an integrated sensing, communications, and powering (ISACP) technique for supporting both sensing and communication QoS provisioning while delivering the power over the next-generation wireless networks using massive multiple-input and multiple-output (massive MIMO) communications. First, we develop the system models for the ISACP scheme to simultaneously sense the targeted mobile users (MUs), transmit the information, and deliver the power. Second, we propose a hypothesis testing based scheme to estimate the MU’s angle-of-arrival using the sensing signal. Third, we employ the Cramér-Rao bound (CRB) to measure the performance of sensing and maximize the energy-efficiency with satisfying requirements of both sensing and communication performances. Finally, we use numerical analyses to validate and evaluate our proposed ISACP scheme.

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A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing

A Deep Q-Learning Framework for Enhanced QoE and Energy Opti...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Sabiha Tasnim Sumona Syed Sabbir Hasan Abu Yousuf Tamzid Palash Roy Md. Abdur Razzaque Redowan Mahmud Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Fog computing, an evolution of cloud computing, has become increasingly popular for its ability to lessen the burden of such a centralized computing paradigm by distributing tasks generated by IoT across fog layers. Effectively managing real-time, delay-sensitive, and diverse IoT applications to enhance the Quality-of-Experience (QoE) presents significant challenges due to the dispersed nature and limited resources of fog nodes. Previous studies in fog computing task offloading have typically focused on either energy consumption or service delay. This paper introduces an optimization framework for task offloading within fog computing environments that aims to balance improved user QoE with reduced energy consumption, employing Mixed-Integer Linear Programming (MILP). Given the NP-hard nature of this framework, we have devised a Deep Q-Learning (DQL) based model for task offloading, termed ELTO-DQL, which aims for near-optimal solutions in polynomial time. Experimental results indicate that the ELTO-DQL model enhances energy efficiency and QoE by up to 19% and 15% respectively, outperforming contemporary benchmarks.

关键词： Energy consumption Q-learning Computational modeling Mixed integer linear programming Quality of experience Internet of Things Resource management

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Predictive Modeling of Cardiovascular Disease Using ML Algorithms

Predictive Modeling of Cardiovascular Disease Using ML Algor...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Vijayalakshmi, M. Cherukuvada, Srikanth Chinnappa, A. Kavitha, G. Soujanya, A. Nareshkumar, R. Care College of Engineering Artificial Intelligence and Data Science Trichy India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Kattankulathur India Department of Mathematics Namakkal India Ibrahimpatnam India

ISBN: (纸本)9798350348910

One of the most significant contributors to morbidity in the global population is the ability to forecast a heart attack. Clinical data analysis is considered one of the most significant aspects for making a forecast, and that aspect is cardiovascular illness, a highly important disease. This can be a very useful tool. Using ML to its full potential, this investigation aims to achieve improved accuracy in diagnosing cardiac disease. In this research, we provide a machine knowledge heart attack diagnosis (MLHAP) technique that involves the study of different risk indicators, and prediction for heart attacks is carried out via ML methodologies, including Support Vector Machines, Logistic Regression, Naive Bayes, Knn, and XGBoost. In this method, the ML-HAP method predicts heart attacks. This approach aims to reduce the mortality rate from heart attacks. The forecast has been optimized as much as possible based on many parameters, which has been the primary focus. KNN, with its parameters optimized, made the most accurate prediction of any of the algorithms. The accuracy obtained using KNN Optimization is 85.24, while the accuracy obtained using Logistic Regression is 78.68. The prediction made by machine learning models for recognizing the signs of a heart attack is extremely effective, particularly when using boosting algorithms. © 2023 IEEE.

关键词： Support vector machines

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Editorial for the Special Issue on 6G Requirements,Vision,and Enabling Technologies

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engineering 2022年第1期8卷 1-2页

作者： Ping Zhang Xuemin(Sherman)Shen Jianhua Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

As the commercialization process of the fifth-generation(5G)communication systems accelerates,research on the sixth genera-tion(6G)is being placed on the agenda in academic and industrial communities all over the world.6G systems are expected to fur-ther enhance the performance of 5G systems and continue to pen-etrate into all aspects of society,promoting the technological integration of communications with other disciplines,such as arti-ficial intelligence,materials science,and ***,a spe-cial issue on the development of the future 6G is both timely and *** special issue contains eight papers detailing recent cutting-edge research achievements from the perspectives of 6G requirements,visions,and enabling technologies.

关键词： integration intelligence fifth

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

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IEEE Symposium on High-Performance computer Architecture

作者： Yuzong Chen Ahmed F. AbouElhamayed Xilai Dai Yang Wang Marta Andronic George A. Constantinides Mohamed S. Abdelfattah Computer Systems Lab Cornell University Systems and Networking Research Group Microsoft Research Department of Electrical and Electronic Engineering Imperial College London

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders their deployment. In this paper, we improve the accessibility of LLMs through BitMoD 1 , an algorithm-hardware co-design solution that enables efficient LLM acceleration at low weight precision. On the algorithm side, BitMoD introduces fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights. Through the careful design of these new data types, BitMoD is able to quantize LLM weights to very low precision (e.g., 4 bits and 3 bits) while maintaining high accuracy. On the hardware side, BitMoD employs a bitserial processing element to easily support multiple numerical precisions and data types; our hardware design includes two key innovations: First, it employs a unified representation to process different weight data types, thus reducing the hardware cost. Second, it adopts a bit-serial dequantization unit to rescale the per-group partial sum with minimal hardware overhead. Our evaluation on six representative LLMs demonstrates that BitMoD significantly outperforms state-of-the-art LLM quantization and acceleration methods. For discriminative tasks, BitMoD can quantize LLM weights to 4 -bit with < 0.5% accuracy loss on average. For generative tasks, BitMoD is able to quantize LLM weights to 3-bit while achieving better perplexity than prior LLM quantization scheme. Combining the superior model performance with an efficient accelerator design, BitMoD achieves an average of $1.69 \times$ and $1.48 \times$ speedups compared to prior LLM accelerators ANT and OliVe, respectively. 1 Code is available at: https://***/yc2367/BitMoD-HPCA-25

关键词： Technological innovation Quantization (signal) Accuracy Machine learning algorithms Costs Large language models Machine learning computer architecture Hardware Energy efficiency

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Mutual Funds Investment Risk Analysis Using Machine Learning 2

Mutual Funds Investment Risk Analysis Using Machine Learning

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Dasgupta, Anupam Ravuri, Nandhika Jhansi Nallakaruppan, Musiri Kailasanathan Gite, Shilpi Natesan, Deepa Balusamy, Balamurugan Vellore Institute of Technology School of Electronics and Communication Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Information Technology and Engineering Vellore India Symbiosis Institute of Technology Aiml Department Pune India Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur Chennai India Shiv Nadar Institution of Eminence Noida India

ISBN: (纸本)9798350384277

The involvement of machine learning (ML) in the finance sector has ushered in a new era of investment management, profoundly influencing the mutual funds landscape. It has redefined strategies and optimized financial decision-making. This paper provides a comparative analysis of ML technologies such as AdaBoost Regressor, Support Vector Machines, Gradient Boosting, and Random Forest. The positive results indicated by the paper highlight the pivotal role of ML in revolutionizing mutual funds management in India, reflecting the nation's aspirations for financial innovation and inclusion aligning it with global levels of efficiency in investment strategies. The efficiency of the models was tested with performance metrics such as Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Performance Error (MAPE). Our evaluation concluded Random Forest was the best performer with an R2 Score of 0.925, MSE of 0.246, RMSE of 0.496, MAE of 0.271, and MAPE of 0.096. © 2024 IEEE.

关键词： Decentralized finance

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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