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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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distributed Tracing for InterPlanetary File System 1

Distributed Tracing for InterPlanetary File System

引用

1st international Symposium on Parallel computing and distributed systems, PCDS 2024

作者： Gupta, Sushant Kumar Miller, Marshall David Han, Rachel Guo, Haorui Stanford University Dept. of Computer Science Palo Alto United States

ISBN: (纸本)9798350349658

The InterPlanetary File System (IPFS) is on its way to becoming the backbone of the next generation of the web. However, it suffers from several performance bottlenecks, particularly on the content retrieval path, which are often difficult to debug. This is because content retrieval involves multiple peers on the decentralized network and the issue could lie anywhere in the network. Traditional debugging tools are insufficient to help web developers who face the challenge of slow loading websites and detrimental user experience. This limits the adoption and future scalability of IPFS. In this paper, we aim to gain valuable insights into how content retrieval requests propagate within the IPFS network as well as identify potential performance bottlenecks which could lead to opportunities for improvement. We propose a custom tracing framework that generates and manages traces for crucial events that take place on each peer during content retrieval. The framework leverages event semantics to build a timeline of each protocol involved in the retrieval, helping developers pinpoint problems. Additionally, it is resilient to malicious behaviors of the peers in the decentralized environment. We have implemented this framework on top of an existing IPFS implementation written in Java called Nabu. Our evaluation shows that the framework can identify network delays and issues with each peer involved in content retrieval requests at a very low overhead. © 2024 ieee.

关键词： Decentralized systems

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

引用

2024 international conference on Advances in Data Engineering and Intelligent computing systems, ADICS 2024

作者： Ghosh, Tanumay Singal, Omisha Lodh, Namit Suchitra, S. School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350364828

Using a cutting-edge neural network framework, 'PulseSync BP' estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare. © 2024 ieee.

关键词： Blood pressure

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Sustainable Energy Planning for Community Microgrids Considering Economic, Environmental, and Resilience Factors

Sustainable Energy Planning for Community Microgrids Conside...

引用

2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Uddin, Moslem Mo, Huadong Dong, Daoyi School of Engineering and Technology The University of New South Wales CanberraACT2610 Australia School of Systems and Computing The University of New South Wales CanberraACT2610 Australia School of Engineering Australian National University Ciicada Lab CanberraACT2601 Australia

ISBN: (纸本)9781665410205

This study presents a framework for sustainable energy planning of community microgrids (MGs), integrating optimal design and decision-support tools. A rural community in New South Wales, Australia, is considered as a case study for this investigation. The proposed microgrid framework is evaluated based on economic viability, environmental sustainability, and community resilience. The economic analysis reveals an attractive net present cost of 3.26 million over the MG's 25-year lifetime, with a competitive levelized cost of energy of 0.196 per kWh. The environmental impact assessment quantifies a significant reduction of 394.429 tonnes of CO2- equivalent greenhouse gas emissions annually through the integration of 200 kW of solar photovoltaic and 258 kW of wind turbines. The resilience assessment demonstrates a high energy reliability with zero unmet loads facilitated by backup systems and decision-making tools. The findings contribute to the field of sustainable energy planning by providing a comprehensive and integrated approach that addresses the complex interplay of economic, environmental, and resilience factors in the context of community MGs. © 2024 ieee.

关键词： Clean energy

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Multi-Class Multi-Load Handling MAC Protocol for WBAN Based on ieee 802.15.6 Standard Using Reinforcement Learning

Multi-Class Multi-Load Handling MAC Protocol for WBAN Based ...

引用

2024 ieee international conference on computing, Applications and systems, COMPAS 2024

作者： Rana, Sahab Uddin Hossain, Md. Mortuza Kazary, Sumaya Rahman, Md. Obaidur Dhaka University of Engineering and Technology Gazipur Department of Computer Science and Engineering Gazipur1707 Bangladesh

ISBN: (纸本)9798331529765

The capacity to exchange data and provide real-time monitoring for patient care makes wireless body area networks (WBANs) vital for modern medical treatment systems. WBANs are used for various traffic loads, including high-rate and extremely low-rate continuous monitoring data. The communication standard of ieee-802.15.6 describes physical and MAC layer functions for WBANs. The ieee 802.15.6 superframe is used to handle multiple types of traffic load in WBAN based on predefined user priorities. Since various kinds of data traffic have fixed periods, this results in an ineffective use of the superframe. In this study, we develop a multi-class multi-load handling MAC (MMH-MAC) protocol for WBANs. This protocol maximizes the use of the ieee 802.15.6 superframe through reinforcement learning (i.e., Q-learning). The proposed MAC protocol classifies the data, calculates the node's Q-value, and allocates the time slot for different loads based on the largest Q-value using the TDMA technique. The operation of the MMH-MAC protocol is examined via comprehensive NS-2 simulations and thoroughly contrasted with the present protocols. © 2024 ieee.

关键词： Reinforcement learning

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distributed Denial of Services (DDoS) Botnet Attack Prevention in Internet of Things (IoT) Devices Using AI 3

Distributed Denial of Services (DDoS) Botnet Attack Preventi...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Garg, Kaushiv Gill, Kanwarpartap Singh Chauhan, Rahul Rawat, Devyani Banerjee, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350319125

The expeditious adoption of Internet of Things (IoT) devices has facilitated the emergence of complex cybersecurity risks, notably distributed Denial of Service (DDoS) botnet assaults, which pose a substantial danger. With the exponential growth of interconnected devices inside the Internet of Things (IoT), the task of identifying malicious linkages becomes more challenging. The identification of these hazards has significant importance for intrusion detection systems (IDS), with the use of machine learning (ML) having been a prevailing practice due to the continuous advancement of cyberattacks. In order to effectively combat such dangers, it is essential that machine learning technologies has the capability to observe a diverse range of attack patterns, hence illustrating the intricacies involved in targeting network traffic. Regrettably, a significant portion of the datasets accessible to the machine learning community lack comprehensive representation of well recognized attack patterns. This is particularly true for datasets pertaining to distributed Denial of Service (DDoS) attacks, such as ACK and PUSH-ACK floods. The objective of this project is to address the existing disparity by curating a compilation of assault approaches that are underutilized. This dataset provides IDS developers with the necessary resources to enhance their detection rates. This research work enhances the security of IoT devices against DDoS botnet assaults, hence contributing to the overall safety of the IoT ecosystem. © 2023 ieee.

关键词： ACK and PUSH-ACK Botnet Atack distributed Denial of Services (DDoS) Internet of Things (IoT) Intrusion Detection System Machine Learning

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Automated Traffic Control for Sustainable Urban Mobility 3

Automated Traffic Control for Sustainable Urban Mobility

引用

3rd international conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Sudhakaran, Pradeep Koushik, C.R. George, Josh G. Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331532420

Nowadays, urban areas worldwide face several significant challenges in managing traffic, which leads to increases fuel consumption, pollution and travel time. Video-based object detection can collect useful data from video frames like vehicle count, speed, type of vehicle, license plates and more. This can be done efficiently and in a cost-effective way and will help improve safety and traffic management. Traditional systems rely on multiple older algorithms and different sensors to capture data that lacks accuracy. The proposed system integrates computer vision techniques and deep learning models like YOLOv8 using a single Convolutional Neural Network that divides the image into a grid. The count of the detected objects is further stored and using K-Means Clustering Algorithm, data is analyzed and further used for alerting the users about places with heavy congestion. Based on this congestion data, traffic lights will be changed based on the severity of the congestion instead of using regular timer-based traffic signals, thus, improving traffic control and safety. © 2024 ieee.

关键词： Convolutional neural networks

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Interpretable Prediction of Student Dropout Using Explainable AI Models

Interpretable Prediction of Student Dropout Using Explainabl...

引用

2024 international Research conference on Smart computing and systems Engineering, SCSE 2024

作者： Padmasiri, Prasadini Kasthuriarachchi, Sanvitha Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350375688

The research addresses the challenge of student dropouts in education institutions, recognizing the pivotal role of education in shaping the society. A research gap is identified, with limited studies effectively combining Machine Learning (ML) algorithms and Explainable AI (XAI) techniques for predicting student dropout. To bridge this gap, a prediction model is developed, integrating ML with XAI to enhance transparency and interpretability. A web interface is introduced, enabling users to input attributes and receive predictions with explanations interpreted using LIME. Further, global interpretation using SHAP is conducted, offering insights into the overall impact and importance of features in the ML model across its dataset. The model is evaluated by selecting academic professionals through purposive sampling technique, employing Likert scale questions, resulting in a mean value exceeding 3.4 and affirming the model's effectiveness in predicting student dropouts with transparency. © 2024 ieee.

关键词： Students

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Algorithm for Resource Allocation and computing Offloading in 6G Networks: Deep Reinforcement Learning-based 9

Algorithm for Resource Allocation and Computing Offloading i...

引用

9th international conference on Mechatronics Engineering (ICOM)

作者： Saeed, Mamoon M. Saeed, Rashid A. Ali, Elmustafa Sayed Mokhtar, Rania A. Khalifa, Othman O. Univ Modern Sci UMS Fac Engn Dept Commun & Elect Engn Sanaa Yemen Taif Univ Coll Comp & Informat Technol Dept Comp Engn POB 11099 Taif 21944 Saudi Arabia Red Sea Univ Fac Engn Dept Elect & Elect Engn Portsudan Sudan Sudan Univ Sci & Technol Fac Engn Dept Elect Engn Khartoum Sudan Inter Islamic Univ Malaysia Dept Elec & Comp Engn Kl Malaysia Libyan Ctr Engn Res & Info Tech Bani Walid Libya

ISBN: (纸本)9798350349795;9798350349788

The emergence of the sixth generation (6G) wireless networks brings new challenges and opportunities for efficient computing offloading and resource allocation. This paper proposes a novel Deep Reinforcement Learning-based computing Offloading and Resource Allocation (DRL-CORA) algorithm for 6G networks. The algorithm leverages the power of deep reinforcement learning to dynamically determine the optimal computing offloading decisions and resource allocation strategies. The Deep Reinforcement Learning-based DCORA algorithm for computation offloading and resource allocation is effective, as demonstrated by our simulations. When compared directly, the suggested DCORA algorithm performs 15% better than other baseline systems.

关键词： Deep reinforcement learning resource allocation network of 6G computer offloading mobile edge computing

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Estimating the Impact of Communication Schemes for distributed Graph Processing 21

Estimating the Impact of Communication Schemes for Distribut...

引用

21st ieee international Symposium on Parallel and distributed computing (ISPDC)

作者： Ye, Tian Kuppannagari, Sanmukh R. De Rose, Cesar A. F. Wijeratne, Sasindu Kannan, Rajgopal Prasanna, Viktor K. Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre Brazil

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

Extreme scale graph analytics is imperative for several real-world Big Data applications with the underlying graph structure containing millions or billions of vertices and edges. Since such huge graphs cannot fit into the memory of a single computer, distributed processing of the graph is required. Several frameworks have been developed for performing graph processing on distributed systems. The frameworks focus primarily on choosing the right computation model and the partitioning scheme under the assumption that such design choices will automatically reduce the communication overheads. For any computational model and partitioning scheme, communication schemes - the data to be communicated and the virtual interconnection network among the nodes - have significant impact on the performance. To analyze this impact, in this work, we identify widely used communication schemes and estimate their performance. Analyzing the trade-offs between the number of compute nodes and communication costs of various schemes on a distributed platform by brute force experimentation can be prohibitively expensive. Thus, our performance estimation models provide an economic way to perform the analyses given the partitions and the communication scheme as input. We validate our model on a local HPC cluster as well as the cloud hosted NSF Chameleon cluster. Using our estimates as well as the actual measurements, we compare the communication schemes and provide conditions under which one scheme should be preferred over the others.

关键词： distributed Graph Processing Performance Estimation Communication Schemes Cluster computing

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Smart Healthcare systems: IoT and AI for Heart Disease Prediction 5

Smart Healthcare Systems: IoT and AI for Heart Disease Predi...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Vishnu, K. Sebastian, Thomas Binu, P.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350379945

Fast food consumption and changes in lifestyle are associated with an increase in heart related problems. This study looks at how cloud computing, the Internet of Things and machine learning can be combined for CVD risk prediction and real-time monitoring. The IoT-ML-Cloud paradigm aims to increment the precision of Cardio Vascular Disease prediction by merging these technologies. The system comprises three main modules: Prediction, Data collection and User engagement. Utilizing Arduino and ESP32 modules. An Android application facilitates patient data input and real-time monitoring and predictions using ML models deployed on the Microsoft Azure cloud. Bluetooth technology utilised to facilitates real-time data transfer to the cloud. This paper proposes Bi-LSTM algorithm with an accuracy of 96.55% for the heart disease prediction. Along with highlighting significant discoveries from numerous ML algorithms in the diagnosis of Cardio Vascular Disease, it also highlights the significance of cloud computing and data mining for large-scale medical data analysis. © 2024 ieee.

关键词： Electronic health record

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