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Lapse: Latency & Power-Aware Placement of Data Stream Applications on Edge computing 14

Lapse: Latency & Power-Aware Placement of Data Stream Applic...

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14th international conference on cloud computing and Services Science, CLOSER 2024

作者： Kayser, Carlos Henrique de Assunção, Marcos Dias Ferreto, Tiago School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Dept. of Software Engineering and IT École de Technologie Supérieure Univ. of Quebec Montreal Canada

ISBN: (纸本)9789897587016

Data Stream Processing (DSP) systems have gained considerable attention in edge computing environments to handle data streams from diverse sources, notably IoT devices, in real-time at the network’s edge. However, their effective utilization concerning end-to-end processing latency, SLA violations, and infrastructure power consumption in heterogeneous environments depends on the adopted placement strategy, posing a significant challenge. This paper introduces Lapse, an innovative cost-based heuristic algorithm specifically crafted to optimize the placement of DSP applications within edge computing environments. Lapse aims to concurrently minimize latency SLA violations and curtail the overall power consumption of the underlying infrastructure. Simulation-driven experiments indicate that Lapse outperforms baseline strategies, substantially reducing the power consumption of the infrastructure by up to 24.42% and SLA violations by up to 75%. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Electric power utilization

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Edge-cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models 24

Edge-Cloud Collaborative Motion Planning for Autonomous Driv...

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24th IEEE international conference on communication Technology, ICCT 2024

作者： Chen, Jiao Dai, Suyan Chen, Fangfang Lv, Zuohong Tang, Jianhua Han, Laiquan South China University of Technology School of Intelligent Engineering China Guangzhou College of Commerce School of Information Engineering China

ISBN: (纸本)9798350363760

Integrating large language models (LLMs) into autonomous driving enhances personalization and adaptability in open-world scenarios. However, traditional edge computing paradigm still faces significant challenges in processing complex driving data, particularly regarding real-time performance and system efficiency. To address these challenges, this study introduces EC-Drive, a novel edge-cloud collaborative autonomous driving system with data drift detection capabilities. EC-Drive utilizes drift detection algorithms to selectively upload critical data, including new obstacles and traffic pattern changes, to the cloud for processing by GPT-4, while routine data is efficiently managed by smaller models on edge devices. This approach not only reduces inference latency but also improves system efficiency by optimizing communication resource usage. Experimental validation confirms the system's robust processing capabilities and practical applicability in real-world driving conditions, demonstrating the effectiveness of this edge-cloud collaboration framework. © 2024 IEEE.

关键词： Motion planning

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An Integrated Dynamic Behavior Analysis and Adaptive Detection Framework for Enhanced cloud Malware Detection

An Integrated Dynamic Behavior Analysis and Adaptive Detecti...

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2024 international conference on Innovative computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Keerthika Devi, S. Sandhya, G.R. Krithika, J. Joseph, Linda School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331543617

As malware threats continue to evolve and pose significant risks to cloud computing environments, there is a pressing need for advanced detection systems that can effectively address these challenges. This research introduces the Dynamic Behavior Analysis and Adaptive Detection Network (DBA-ADN), a novel framework designed to enhance malware detection through a combination of dynamic behavior analysis and adaptive detection techniques. DBA-ADN integrates real-time behavioral data with sophisticated adaptive algorithms to achieve superior detection performance. The study involves a comprehensive comparison of DBA-ADN against several benchmark systems, including MDCD, GloVe with CNN, cloudIntellMal, Hybrid Deep Learning Models, and DenseNet-121. The results reveal that DBA-ADN outperforms these established methods, achieving an impressive accuracy of 98.20% and an F1 Score of 0.99. This enhanced performance is attributed to DBA-ADN's ability to accurately identify both known and novel malware threats while minimizing false positives and false negatives. The framework's innovative approach addresses key limitations observed in existing solutions, as difficulties in handling sophisticated malware and inefficiencies in traditional detection techniques. By leveraging dynamic behavior analysis and adaptive algorithms, DBA-ADN not only improves detection accuracy but also reduces the complexity of deployment compared to its counterparts. The findings highlight DBA-ADN's potential to set a new benchmark in malware detection within cloud environments, offering a robust solution to the ongoing challenges of cybersecurity. This research contributes significantly to the field by providing a highly effective and adaptable approach to malware detection, laying the groundwork for future advancements and continued innovation in the domain. © 2024 IEEE.

关键词： cloud security

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Research on Garbled Circuits-Based Identity Authentication and Ciphertext Hidden Access Control in Vehicular Networks 24

Research on Garbled Circuits-Based Identity Authentication a...

引用

9th international conference on cloud computing and Internet of Things, CCIOT 2024

作者： Jiao, Zhilong Bai, Fenhua Zhang, Chi Zhang, Xiaohui Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology China Kunming China Department of Computing Beijing University of Technology China Beijing China

ISBN: (纸本)9798400717161

Since most communications in vehicular networks rely on wireless transmissions, information broadcast by vehicles is highly susceptible to interception and eavesdropping by third parties, making it vulnerable to various attacks. This can result in communication messages being tampered with and the leakage of vehicle locations and identity privacy. From a security perspective, this paper introduces Yao's garbled circuit mechanism, combining random bytes with the solutions of linear equations as identity information assigned to vehicle nodes. By employing dual authentication, the proposed approach addresses issues such as privacy protection, anonymity, replay attacks, and collusion attacks in vehicular networks, thereby enhancing security. Moreover, considering the post-authentication access control scenario, this paper proposes a novel LSSS matrix transformation algorithm for ciphertext hidden access control strategy. It realizes a partially hidden policy, which makes matrix generation more efficient by supporting both AND and OR gates, thus improving the access control efficiency while ensuring security. Performance analysis indicates that in terms of identity authentication, the scheme reduces computational costs by an average of 96.25%, and in access control, the scheme reduces key generation time by an average of 80%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ciphertext

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Air Purification Robotics using cloud and Deep Q Networks for Autonomous Systems 1

Air Purification Robotics using Cloud and Deep Q Networks fo...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Reddy, Elangovan Guruva Damodhar, T. S. Balaji Yuvaraj, S. Vathani, B. Santha Sivakumar, S. Malathi, N. Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram India Madanapalle Institute of Technology and Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Mahalashmi Women's College of Arts and Science Department of Management Studies Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues. © 2024 IEEE.

关键词： Robotics

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IoT Enabled Automotive Onboard Telemetry Recorder 2

IoT Enabled Automotive Onboard Telemetry Recorder

引用

2nd IEEE international conference on Contemporary computing and communications, InC4 2024

作者： Kanishk, K.U. Sri Abiram, G.B. Rajalakshmi, T. SRM Institute of Science and Technology Dept. of Electronics and Communication Engineering Kattankulathur India

ISBN: (纸本)9798350383652

Addressing the global challenge of millions of annual deaths in accidents. This IoT-enabled Automotive Onboard Telemetry Recorder system, which uses MEMS sensors to monitor driver behavior and vehicle performance continuously, and GSM/GPS System, replaces traditional vehicle black boxes. In the event of an accident, the system uses the cloud-based IoT platform to alert the emergency services of the GPS-tracked vehicle location immediately. The recorded data is a valuable investigative resource, providing insights for post-incident analysis. An app also provides real-time monitoring and driver alerts, enhancing safety practices compactly and efficiently. © 2024 IEEE.

关键词： Global positioning system

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Turbo Powered Symmetric and Asymmetric Traffic Encryption Methods in Multi-cloud Environment

Turbo Powered Symmetric and Asymmetric Traffic Encryption Me...

引用

2023 international conference on Sustainable communication Networks and Application, ICSCNA 2023

作者： Pranav, M. Bharadwaj, U. Bhanu, P. Vijaya Teja, D. Surya Srithar, S. Balajee, R.M. Koneru Lakshmaiah Education Foundation Department of Computerscience and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350313987

In this current digital world, the term 'cloud' represents limitless possibilities. It refers to a large network of computers that use the Internet to store, process, and manage data and services. This provides unmatched scalability and accessibility, and it is cost-efficient. Most simply, it is the delivery of on-demand IT Resources like storage, network, and databases of our choice over the internet. However, the adaptation of cloud resources has raised problems like data security, compliance, data privacy, downtime, and availability of the cloud. This article mainly focuses on the challenges in cloud security and presents some of its probable solutions to reduce the risks caused by cloud security issues. The research study starts out by outlining cloud security and going over the various cloud security concerns. The best behaviors for protecting cloud data are further explained, including using strong passwords and multi-factor authentication, encrypting data while it's in transit and at rest, as well as putting access control policies. By recognizing these problems, and by applying the most effective security measures, organizations/businesses can safely take advantage of cloud computing. © 2023 IEEE.

关键词： cloud computing cloud Security Compliance Data Protection Security Challenges

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Intelligent computing for Healthcare: A Survey 3

Intelligent Computing for Healthcare: A Survey

引用

3rd international conference on Intelligent Systems, Advanced computing, and communication, ISACC 2025

作者： Vijeta Singh, K.M. Singh, Ashish Kalinga Institute of Industrial Technology School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798331523893

The healthcare sector is increasingly adopting intelligent systems to address the growing demand of medical services. The system transforms medical services by leveraging advanced computational techniques and healthcare innovation. The advanced technologies of computing models include Artificial Intelligence (AI), Machine Learning (ML), Edge computing (EC), Quantum computing, cloud computing, Neuromorphic computing, and many more. It significantly improves resource utilization, patient outcomes, efficiency, diagnostic accuracy, and real-time decision-making. This survey systematically reviews the latest IC models, analyzing their architectures, operational mechanisms, and performance metrics. This survey includes the strengths and weaknesses of different IC models. A comparative analysis of existing approaches is provided to identify their strengths, limitations, and applicability to various healthcare scenarios. A generic IC framework will help to understand the development of new computing models. In addition, this survey paper discussed the features and applications of IC in healthcare. The insights presented in this paper aim to bridge existing gaps and provide a foundation to advance IC-driven healthcare solutions. © 2025 IEEE.

关键词： Supervised learning

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Enhanced Pipeline Safety: cloud-Based Leak Prediction Using XGBoost 16

Enhanced Pipeline Safety: Cloud-Based Leak Prediction Using ...

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16th IEEE international conference on Computational Intelligence and communication Networks, CICN 2024

作者： Jagadeesh, V. Sivakumar, P. Rajalakshmi Engineering College Department of Eelectrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331505264

The oil and gas industry face significant risks from pipeline leaks, which can result in explosions, environmental damage, and loss of life. This paper introduces a cloud-based explosion risk detection system utilizing flow and pressure sensors for real-time monitoring. Sensor data is transmitted to the cloud, where an advanced AI model, specifically the XGBoost algorithm, analyzes the data to detect leaks and assess risks. The XGBoost model, optimized through Bayesian Optimization adjusting parameters such as step size, max depth, and total estimators-achieved an accuracy score of 97.09% and a cross-validation score of 97.01 %, demonstrating its robust performance. Trained on extensive historical leak patterns, the model predicts potential leak scenarios. Additionally, simulations model various leak scenarios to rigorously test and refine the system's responses. By leveraging cloud computing and XGBoost, the system offers scalable, real-time leak detection, accurately identifies leakage, and triggers early warning alarms. The system continuously monitors pipeline pressure and flow data in real-time, enhancing pipeline safety by enabling rapid detection and response to potential leaks. The integration of these technologies ensures a comprehensive, proactive risk management solution, significantly enhancing pipeline safety, optimizing operational efficiency, and minimizing environmental and human impacts while advancing industry standards. © 2024 IEEE.

关键词： Gas industry

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Resource Allocation in cloud computing Using Quasi-Opposition Learning Based Aquila Optimizer

Resource Allocation in Cloud Computing Using Quasi-Oppositio...

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international conference on 6G communications Networking and Signal Processing, SGCNSP 2023

作者： Kumari, K. Aruna Muvva, Vijaya Bhaskar Reddy Kumar, K. Sudheer Rai, B. Karunakara Shakir, Aboothar Mahmood Department of CSE SRKR Engineering College Bhimavaram Chinaamiram India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9789819778751

The resource allocation is focus on enhancing the service provider proceedings and obtaining customer satisfaction by Service Level Agreements (SLA). cloud technology has significantly transformed the organizations to manage and allocate their resources. The cloud users are choosing sources of application distribution exclusive of cooperating Quality of Service (QoS) necessities. To overcome this issue, this paper proposed a Quasi-Opposition Learning based Aquila Optimizer (QOL-AO) for resource allocation in cloud. The virtualization supports to implement task using resource availability and reduces the response time. The QOL-AO employs the hunting approach for obtain the effective and efficient resource allocation in cloud. The Performance of QOL-AO is estimate through execution time, response time and energy consumption for various number of Virtual Machines (VMs). The QOL-AO attained less execution time of 18.568 ms and response time of 0.095 s than existing algorithms like Emperor Penguin Optimization (EPO) and Particle Swarm Optimization (PSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： cloud platforms

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