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

共 30781 条记录，以下是1161-1170 订阅

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REVENUE FORECASTING USING distributed MACHINE LEARNING IN ONLINE ADVERTISING systems

REVENUE FORECASTING USING DISTRIBUTED MACHINE LEARNING IN ON...

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2023 ieee international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Murugan, P. Esakkiammal, S. Kanagavalli, N. Priya, P. Krishna Martis, Jason Elroy Kavitha, P. Pallavaram Tamilnadu Chennai India Department of Library Central University of Tamil Nadu Neelakudi Tamilnadu Thiruvarur India Department of CSE Rajalakshmi Institute of Technology Kuthambakkam Tamilnadu Chennai India KL Business School KL Education Foundation Guntur Andrha Pradesh Vaddeswaram India Department of Information Science and Engineering NMAM Institute of Technology Nitte Deemed to be University Karnataka Karkala India Department of Computer Science and Engineering R.M.K. Engineering College Tamilnadu India

ISBN: (纸本)9798350305708

This research delves into the utilization of distributed machine learning methods for revenue forecasting in the realm of online advertising systems. As the domain of digital advertising continues to expand rapidly, the accurate prediction of revenue becomes indispensable for optimizing resource allocation and making well-informed decisions. Conventional approaches frequently encounter difficulties in managing the sheer volume and intricacy of advertising data. In contrast, distributed machine learning harnesses the capabilities of parallel processing and distributed computing to efficiently process and analyze large datasets. This investigation explores diverse distributed machine learning algorithms, including gradient boosting, random forests, and neural networks, within the context of revenue forecasting. The research assesses their performance, scalability, and precision in comparison to traditional techniques. Through empirical experiments and case studies, we showcase the potential of distributed machine learning to elevate the accuracy of revenue forecasting in online advertising systems, ultimately contributing to more informed business strategies and enhanced revenue generation. © 2023 ieee.

关键词： algorithm scalability distributed machine learning online advertising systems parallel processing revenue forecasting

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Establishing a High-Performance and Productive Ecosystem for distributed Execution of Python Functions Using Globus Compute

Establishing a High-Performance and Productive Ecosystem for...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Ananthakrishnan, Rachana Babuji, Yadu Bryan, Josh Chard, Kyle Chard, Ryan Clifford, Ben Foster, Ian Gorenstein, Lev Kesling, Kevin Hunter Janidlo, Chris Katz, Daniel S. Mello, Reid Pauloski, J. Gregory Wang, Lei University of Chicago United States Argonne National Laboratory United States University of Illinois Urbana-Champaign United States

ISBN: (纸本)9798350355543

The research computing ecosystem is increasingly heterogeneous and diverse. Democratizing access to these essential resources is critical for accelerating research progress. However, the gap between a high-level workload, such as Python in a Jupyter notebook, and the resources and interfaces exposed by HPC systems is significant. Users must securely authenticate, manage network connections, deploy and manage software, provision and configure nodes, and manage workload execution. Globus Compute reduces these barriers by providing a managed, fire-and-forget model that enables execution of Python functions across any resource to which a user has access. However, while Globus Compute has relieved users from many of the challenges of remote computing, we have observed some inefficiencies that remain in terms of use. For example, many users wrap external applications, such as C/C++, Fortran, and even MPI applications, in Python functions and users must deploy many endpoints on a single computer to exploit different configurations. In this paper we describe enhancements to Globus Compute to address these barriers: an asynchronous, future-based executor interface for submitting and monitoring tasks, shell and MPIbased function types, and a multi-user endpoint that can be deployed by administrators and used by authorized users. © 2024 ieee.

关键词： n/a

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Vehicle Dynamical sensor Monitoring Toward Forest Soil Moisture Measuring System 12

Vehicle Dynamical Sensor Monitoring Toward Forest Soil Moist...

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12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Kiga, Yoshinori Tazawa, Takumi Iwase, Masami School of Science and Technology for Future Life Tokyo Denki University Department of Robot Mechatronics Tokyo120-8551 Japan

ISBN: (纸本)9798350358056

In this research, we develop a sensor monitoring system for vehicle dynamics aimed at developing a forest soil moisture measurement system for landslide disaster prevention. The identification of landslide hazard warning areas is necessary to prevent landslide disasters. Soil moisture is one of the factors. Common methods of surveying this soil moisture content include borings and satellites. However, borehole investigations may omit areas that truly need to be investigated without a priori knowledge of the locations to be measured. In addition, it is difficult to specify the timing of measurements in satellite surveys. Therefore, this research aims to establish a system that is safe, wide-ranging, and capable of setting the timing and range of measurement as needed. One approach to meet these requirements is to mount sensors on remotely operated vehicles to measure soil moisture content. In general, vehicles for such purposes need to travel in rough environments, and in order to travel well in rough environments, it is necessary to detect abnormal driving conditions such as slipping or skidding. For this purpose, we develop a sensor monitoring for the vehicle traveling in rough terrain and through forest. © 2023 ieee.

关键词： filtering IMU Vehicle dynamics monitoring

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Analyzing IoT Temperature sensor Application on IBM Bluemix Cloud 8

Analyzing IoT Temperature Sensor Application on IBM Bluemix ...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Kaliyar, Rohit Kumar Bhardwaj, Aditya Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798350396638

The next wave in the era of computing will be outside the realm of desktop computing. In the IoT paradigm, objects surrounding us will communicate and share information. Advanced sensor technologies used in IoT will give rise to new challenges of generating an enormous amount of data that must be processed, transferred, and presented quickly and efficiently. To make the vision of IoT a reality, high-end computing devices are needed. So, cloud-based processing is required because cloud computing provides low-cost, on-demand, and high-availability computing. As an example, this research study considers the scenario in which machines and servers become overheated after a long period of operation and enter a beyond- threshold temperature state, resulting in machine damage. So, the machine's temperature needs to be looked at to make sure nothing dangerous happens. To solve this issue, this study has developed an IoT application that is evaluated in IBM Bluemix cloud that would sense the machine's temperature under analysis and display the appropriate message based on whether that machine is beyond the threshold temperature or not. This, in turn, will help the system administrator prevent dangerous device conditions. © 2023 ieee.

关键词： Internet of things

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Keynote: Rising to the Challenge of Autonomous Vehicles

Keynote: Rising to the Challenge of Autonomous Vehicles

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21st ieee international conference on Pervasive computing and Communications (PerCom)

作者： Rajkumar, Raj Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781665453783

Autonomous vehicles (AVs) have garnered immense interest and investments for more than a decade and a half. Nevertheless, large-scale AV deployments do not seem viable in the near future. In this talk, the speaker will address questions like "What went wrong?", "Is AI the answer?", "Can (and how do) we course-correct?" and "Which future contributions will matter?". Finally, challenges that must be addressed by the research and engineering communities will be discussed.

关键词： Autonomous vehicles Real-time systems Pervasive computing Connected vehicles Wireless sensor networks Transportation Software

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Secure and Location Based Energy Efficient Protocol for Wireless sensor Networks 2

Secure and Location Based Energy Efficient Protocol for Wire...

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2nd international conference on Smart Technologies and systems for Next Generation computing, ICSTSN 2023

作者： Senthil Kumaran, R. Parkavi, R. Jayasudha, M. Akshaya, V. IFET College of Engineering Department of Ece Villupuram India Rajalakshmi Engineering College Department of Aiml Chennai India

ISBN: (纸本)9798350348002

Wireless sensor networks play a major role in applications that require challenging human contact, which can greatly benefit from the use of wireless sensor networks. WSN is widely used in real-time applications, including security systems, environmental monitoring systems, disaster management, and health monitoring, among others. Due to the fact that sensor nodes are placed in remote locations, it is difficult to find their position and optimize the energy used by the sensor nodes. So, it's crucial to increase network lifespan by locating them, enhancing energy efficiency, and giving the network security in order to improve network performance. By merging the WSN network's routing idea into the wireless data network's hierarchical clustering routing algorithm LEACH, specifically Quadrant Based LEACH, a locationbased routing protocol (QB-LEACH) is suggested. The proposed work comprises three phases, namely, location estimation using Grey Wolf optimisation, energy-efficient clustering by the Quadrant Based-LEACH algorithm, and detection of malicious nodes by a trust-based model. Simulation results show that the proposed Secure and Location Based Energy Efficient Protocol (SLEEP) outperforms in terms of packet delivery ratio, average route path length, average delay, and energy consumption. © 2023 ieee.

关键词： sensor nodes

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PIM-Opt: Demystifying distributed Optimization Algorithms on a Real-World Processing-In-Memory System 24

PIM-Opt: Demystifying Distributed Optimization Algorithms on...

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international conference on Parallel Architectures and Compilation Techniques (PACT)

作者： Rhyner, Steve Luo, Haocong Gomez-Luna, Juan Sadrosadati, Mohammad Jiang, Jiawei Olgun, Ataberk Gupta, Harshita Zhang, Ce Mutlu, Onur Swiss Fed Inst Technol Zurich Switzerland NVIDIA Zurich Switzerland Wuhan Univ Wuhan Peoples R China Univ Chicago Chicago IL USA

ISBN: (数字)9798400706318

ISBN: (纸本)9798400706318

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to its effectiveness, simplicity, and generalization performance (i.e., test performance on unseen data). Processor-centric architectures (e.g., CPUs, GPUs) commonly used for modern ML training workloads based on SGD are bottlenecked by data movement between the processor and memory units due to the poor data locality in accessing large training datasets. As a result, processor-centric architectures suffer from low performance and high energy consumption while executing ML training workloads. Processing-In-Memory (PIM) is a promising solution to alleviate the data movement bottleneck by placing the computation mechanisms inside or near memory. Several prior works propose PIM techniques to accelerate ML training;however, prior works either do not consider real-world PIM systems or evaluate algorithms that are not widely used in modern ML training. Our goal is to understand the capabilities and characteristics of popular distributed SGD algorithms on real-world PIM systems to accelerate data-intensive ML training workloads. To this end, we 1) implement several representative centralized parallel SGD algorithms, i.e., based on a central node responsible for synchronization and orchestration, on the real-world general-purpose UPMEM PIM system, 2) rigorously evaluate these algorithms for ML training on large-scale datasets in terms of performance, accuracy, and scalability, 3) compare to conventional CPU and GPU baselines, and 4) discuss implications for future PIM hardware. We highlight the need for a shift to an algorithm-hardware codesign to enable decentralized parallel SGD algorithms in real-world PIM systems, which significantly reduces the communication cost and improves scalability. Our results demonstrate three major findings: 1) The general-purpose UPMEM PIM system can be a viable alternat

关键词： Processing-In-Memory Optimization Stochastic Gradient Descent distributed Optimization Algorithms ML ML training Scalability

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Spark-based distributed Intelligent Network Intrusion Detection System for Unified Dataset

Spark-based Distributed Intelligent Network Intrusion Detect...

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2023 international conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology conference, ATCON-1 2023

作者： Verma, Jyoti Bhandari, Abhinav Singh, Gurpreet Punjabi University Patiala Department of Cse Punjab India Pit Department of Cse Punjab Rajpura India

ISBN: (纸本)9781665456272

The proliferation of cloud computing is directly responsible for the current transformation phase that the information technology sector is going through. The concept of cloud computing is still in its infancy, yet it is altering the information technology industry. Due to the distributed and open nature of cloud services, they are vulnerable to various threats, including malicious activities and intrusions. Cloud services are also prone to be hacked. Conventional network intrusion detection systems (NIDS) are ineffective against today's high-volume network traffic because they are trained using a single dataset. The infrastructure and application pose limitations, making processing enormous network traffic in real-time challenging. To protect the cloud from the numerous cloud-based dangers that exist, it is essential to embody Network intrusion detection systems (NIDS) which are equipped with intelligence. This research presents a solution to a modern problem: the development of a distributed and sophisticated NIDS framework using cloud-based solutions. An intelligent NIDS for cloud platforms is proposed in this article, along with an orchestration of a Docker-based Spark cluster over Kubernetes, which is hosted on AWS EC2 instances. The ANN-based NIDS that has been proposed attains an accuracy of 96.3% and encourages Precision scores of 97.2%, Recall scores of 97.5%, and F1-scores of 97.3%. © 2023 ieee.

关键词： Cloud computing

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Scaling Machine Learning at the Edge-Cloud: A distributed computing Perspective 19

Scaling Machine Learning at the Edge-Cloud: A Distributed Co...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Marozzo, Fabrizio Orsino, Alessio Talia, Domenico Trunfio, Paolo Dimes University of Calabria Rende Italy

ISBN: (纸本)9798350346497

The widespread diffusion of Internet of Things (IoT) devices has led to an exponential growth in the volume of data generated at the edge of the network. With the rapid spread of machine learning (ML)-based applications, performing compute and resource-intensive learning tasks at the edge has become a critical issue, resulting in the need for scalable and efficient solutions that can overcome the resource constraints of edge devices. This paper analyzes the problem of scaling ML applications and algorithms at the edge-cloud continuum from a distributed computing perspective. In particular, we first highlight the limitations of traditional distributed architectures (e.g., clusters, clouds, and HPC systems) when running ML applications that use data generated at the edge. Next, we discuss how to enable traditional ML algorithms combining the benefits of edge computing, such as low-latency processing and privacy preservation of personal user data, with those of cloud computing, such as virtually unlimited computational and storage capabilities. Our analysis provides insights into how properly separated parts of a ML application can be deployed across edge-cloud architectures in order to optimize its execution. More-over, examples of ML applications and algorithms appropriately adapted for the edge-cloud continuum are shown. © 2023 ieee.

关键词： Internet of things

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Design of analog pressure transmitter (4-20 mA) for fast reactors 3

Design of analog pressure transmitter (4-20 mA) for fast rea...

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3rd international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Shri Vidhatri, M.M. Sivaramakrishna, M. Chitrakkumar, S. IIIT-H Hyderabad India Innovative Sensor Section IGCAR Kalpakkam India

ISBN: (纸本)9781665494007

Pressure transmitters, directly or indirectly, play a key role in many systems in nuclear reactors and industries worldwide. The Innovative sensor section(ISS) in Indira Gandhi Center for Atomic Research(IGCAR) developed a pressure transmitter with a simple circuit to reduce complexity and signal-to-noise ratio. This pressure transmitter utilises a completely analog circuit to convert the change in pressure into changing current in the 4-20 mA range. The circuit also provides the output as changing frequency with changing pressure for IoT module compatibility. © 2023 ieee.

关键词： Transmitters

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