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

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Beamforming Design for ISCC systems with Reconfigurable Intelligent Surfaces

Beamforming Design for ISCC Systems with Reconfigurable Inte...

引用

international conference on Communication Technology (ICCT)

作者： Ruihang Yang Dezhi Wang Chen Zhu Boyu Ning Zhengyu Zhu Chongwen Huang Zhaohui Yang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Polytechnic Institute Zhejiang University Hangzhou China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China College of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The deployment of various emerging wireless services has necessitated that sixth-generation (6G) wireless networks integrate sensing, computing and communication within the same radio spectrum. However, it is challenging to improve the performance of the integrated sensing, communication, and computation (ISCC) systems in complex propagation environment. In this paper, we investigate the beamforming design problem in reconfigurable intelligent surfaces (RIS)-aided ISCC systems, where an RIS can provide additional spatial degree of freedom. To this end, we first formulate a joint beamforming problem for RIS-aided ISCC, aiming to concurrently improve the performance for multiple target sensing, multi-dimensional computation, and multi-stream communication. Then we present joint optimization of various performances (JOVP) algorithm that minimizes the sum mean square error (MSE) of various demands under power constraints. Next, we analyze the impact of key system parameters on the overall performance of ISCC, providing valuable guidelines for system design. Finally, simulation shows that the sensing error of JOVP algorithm is reduced by at least 46.7% compared with the existing popular algorithms, while the computation rate and communication rate are increased by more than 49.1% and 22.2%, respectively.

关键词： Wireless sensor networks Array signal processing Wireless networks System performance Mean square error methods Reconfigurable intelligent surfaces Communications technology sensors Optimization Guidelines

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Semi-supervised Multi-source Domain Adaptation in Wearable Activity Recognition

Semi-supervised Multi-source Domain Adaptation in Wearable A...

引用

international conference on distributed computing in sensor systems (DCOSS)

作者： Avijoy Chakma Abu Zaher Md Faridee Raghuveer Rao Nirmalya Roy Information Systems University of Maryland Baltimore County Army Research Lab United States

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

The scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. The traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sources – providing motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.

关键词： Deep learning Adaptation models Buildings distributed databases Performance gain Data models Labeling

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Towards Data Gravity and Compliance Aware distributed Deep Learning on Hybrid Clouds

Towards Data Gravity and Compliance Aware Distributed Deep L...

引用

ieee international conference on High Performance computing Workshops (HiPCW)

作者： Avinash Maurya Jaiaid Mobin M. Mustafa Rafique High Performance Distributed Systems Laboratory (HPDSL) Rochester Institute of Technology New York USA

To store large volumes of data concurrently from a diverse set of sources, data stores such as data silos, lakes, and warehouses, have been widely embraced by various organizations. Thanks to data fabric architectures, such scattered data (both structurally and geographically), can be accessed transparently at scale while adhering to various administrative regulations (e.g. governance, privacy, compliance, etc.). However, modern workload schedulers and distributed deep learning (DDL) runtimes are oblivious to the uneven data distribution across different storage services and compliance regulations, leading to sub-optimal resource utilization and training completion times. Al-though state-of-art workflow schedulers such as Apache Hadoop Yarn, Horovod, etc. exploit data locality, they require application developers to explicitly map data and resources available across various cloud services during job submission. These approaches are redundant and counterproductive for next-generation data fabric architectures that feature automated transparency and compliance abstractions for accessing disparate data sources with uneven data distribution. To this end, we propose an algorithm based on greedy programming that leverages the meta-data catalog of data fabric to efficiently determine training schedules based on data gravity, compliance, and resource availability. Our simulations based on synthetic data and resource distribution profiles demonstrate significant improvements in execution times and resource utilization compared to traditional DDL scheduling approaches in hybrid multi-cloud environments.

关键词： Training Deep learning distributed databases Computer architecture Organizations Fabrics Regulation

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Optimal Utilization of Water for Smart Farming Using Internet of Things (IoT)

Optimal Utilization of Water for Smart Farming Using Interne...

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Innovation in Technology (INOCON), ieee international conference for

作者： Madhusudhana Reddy Barusu Puvvadi Naga Pavithra Pallem Setty Rani Chandrika Department of Electronics and Communication Engineering Ravindra College of Engineering for Women Kurnool Andhra Pradesh India

The agriculture is a very important sector of Indian economy. The farming is facing various challenges present days. The main problem faced in today’s agriculture by farmers is the lack of knowledge of the requirement of the water for the crops. The farmers provide the excess amount of the water or insufficient water for the crop. In this competitive world, farmers not only depend on farming for their livelihood, but also on other occupations. Hence, there may be disturbance in watering crops. This may lead to a decrease in water levels for the crops and sometimes excess water which may also lead to damage of the crop. To get rid of this problem, smart farming system is developed using Internet of Things (IoT). Using this, the farmer comes to know about the moisture content in his field through messages that will be received. Not only just a message, in a smart agriculture system he also can water the field automatically. The main advantage of this paper is that farmers need not to spend his time to decide how much water need for specific crops. Here, moisture sensors placed in the field with the predefined quantity of the water, programmed it with a keyword mois. When the moisture reaches or above the scale value, the motor gets automatically turned off and vice versa. A soil moisture sensor is used to calculate the moisture values in the soil and gives an input to the smart farming system, consists of ESP32 and the data obtained from the sensor is sent to the cloud. The thingspeak is used as cloud, where the data gets stored. The proposed system also updates the information regarding the status of moisture levels regularly with messages to the owner. Later, the website is designed using thingspeak URL and the messages are displayed in the website.

关键词： Smart agriculture Cloud computing Soil moisture Moisture Crops sensor systems Real-time systems

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Iterative learning control for distributed parameter systems using sensor-actuator network 16

Iterative learning control for distributed parameter systems...

引用

16th ieee international conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Patan, Maciej Klimkowicz, Kamil Patan, Krzysztof Univ Zielona Gora Inst Control & Computat Engn Zielona Gora Poland

ISBN: (纸本)9781728177090

The paper discusses an effective approach to iterative learning control synthesis for an important class of distributed parameter systems described by partial differential equations of diffusion-advection type. Such systems are frequently encountered in mathematical modelling of numerous physical processes. The optimal tracking problem is formulated in the spirit of the optimum experimental design theory for a measuring procedure provided with a sensor network over the multidimensional spatial domain. For such a general experimental setup, the convergence of the iterative control law is provided and decentralized control synthesis discussed as well. The study is illustrated with the application of the developed numerical scheme to the process of laser heating of a silicon wafers example.

关键词： Decentralized control

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Taming Resource Heterogeneity In distributed ML Training With Dynamic Batching 1

Taming Resource Heterogeneity In Distributed ML Training Wit...

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1st ieee international conference on Autonomic computing and Self-Organizing systems (ACSOS)

作者： Tyagi, Sahil Sharma, Prateek Indiana Univ Bloomington Bloomington IN 47405 USA

ISBN: (纸本)9781728172774

Current techniques and systems for distributed model training mostly assume that clusters are comprised of homogeneous servers with a constant resource availability. However, cluster heterogeneity is pervasive in computing infrastructure, and is a fundamental characteristic of low-cost transient resources (such as EC2 spot instances). In this paper, we develop a dynamic batching technique for distributed data-parallel training that adjusts the mini-batch sizes on each worker based on its resource availability and throughput. Our mini-batch controller seeks to equalize iteration times on all workers, and facilitates training on clusters comprised of servers with different amounts of CPU and GPU resources. This variable mini-batch technique uses proportional control and ideas from PID controllers to find stable mini-batch sizes. Our empirical evaluation shows that dynamic batching can reduce model training times by more than 4x on heterogeneous clusters.

关键词： Iterative methods

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Advanced Development On Real Time Monitoring Of Bridges Using Wireless Fidelity

Advanced Development On Real Time Monitoring Of Bridges Usin...

引用

international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： D. Ferlin Deva Shahila Valantina Stephen L. Padma Suresh Ashwini. A Shally. S. P Anushia R M Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of EEE Saveetha School of Engineering Thandalam Chennai India Computer Science and Engineering Baselios Mathews II College of Engineering Kollam Department of ECE DMI College of Engineering Chennai India Department of CSE DMI College of Engineering Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Wireless fidelity technology enables communication without the use of wires or connections between two or more people across large distances. Wireless technology encompasses communications that utilize both radio frequencies (RF) and infrared waves. The origin of wireless technology can be traced back to the invention of electromagnetic waves by the German physicist, geophysicist, and engineer, Heinrich Hertz. In the late nineteenth century, the commercial wireless telegraph service was established by the Italian entrepreneur Gugliello Marconi, more than half a century after the invention of the first commercially available wired telegraph in 1832. In the early twentieth century, Marconi was one of the pioneers of radio communications, transmitting radio signals to mobile receivers on ships. Wireless has always been more advanced than wired technology, and it's usually more expensive too, but it's also given us the added benefit of being able to move around and get and send *** this project, we'll be using MATLAB software to design and test the full model. We'll also be looking at wireless bridge health monitoring systems. ZigBee acts as the wireless network for short-range ieee 802.11 communication between sensors in the bridge.

关键词： Wireless sensor networks Technological innovation Wireless networks Wires Bridge circuits Zigbee Receivers

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SynchroSim: An Integrated Co-simulation Middleware for Heterogeneous Multi-robot System

SynchroSim: An Integrated Co-simulation Middleware for Heter...

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international conference on distributed computing in sensor systems (DCOSS)

作者： Emon Dey Jumman Hossain Nirmalya Roy Carl Busart Department of Information Systems Center for Real-Time Distributed Sensing and Autonomy University of Maryland Baltimore County USA DEVCOM Army Research Lab USA

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi-agent systems in battlefield environments where communication is critical in determining the system’s behavior and usability. Due to the necessity of simulators of separate paradigms (co-simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose SynchroSim, an integrated co-simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS-3 as a network simulator. Also, we design our algorithm considering the Perception-Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability (≈11%), and average packet delay (≈10%) compared to the fixed window size-based synchronization approach.

关键词： Packet loss sensor systems Synchronization Multi-robot systems Middleware Usability Robots

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Video Streaming Platform Using distributed Environment in Cloud Platform 7

Video Streaming Platform Using Distributed Environment in Cl...

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7th international conference on Advanced computing and Communication systems, ICACCS 2021

作者： Korupolu, Mythresh Jannabhatla, Srikanth Kommineni, Venkata Surendra Kalyanam, Hemanth Vasantham, Vijaykumar Koneru Lakshmaiah University Computer Science and Engineering Guntur India

ISBN: (纸本)9781665405201

Nowadays, there are so many Video content delivery platforms. Each platform has its own design and technology for delivering the content. There are so many Video streaming algorithms, from those we approach an Adaptive Bitrate Streaming algorithm. As so many Organizations are using the Content caching mechanism for delivering the content. We thought that we take a step forward to that which is multi-Caching for producing the best experience. There are many advantages to multi-tier caching Technology Content delivery and Quality of Experience is increased, it is going to be a better experience for the end-users. Here, a distributed environment plays a key role in it. There are few challenges to implement for Video streaming platform which are jitter, bandwidth, and data loss. We accept those challenges by using the cloud platforms. This video streaming platform is useful to distinct types of users. This is the way of enjoyable experience to the user over the internet. We are done the research on the special video streaming platform. Here, we used an algorithm which is Adaptive Bitrate Algorithm. It is hard to write the code so, we choose the S3Bubble, for storing and delivering the content by using Amazon web services. To access the user for the content, we create a user interface by using the UI-UX, it makes easy to access what he wants. We develop a design by using the WordPress. For deploying the content over the internet by using AWS server. © 2021 ieee.

关键词： Video streaming

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Intelligent Wireless Self-Sustained Sensing Cubic Node Toward AIOT Ready Smart City

Intelligent Wireless Self-Sustained Sensing Cubic Node Towar...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Manjuan Huang Tingting Zhao Guoqing Jin Xiaojing Mu Huicong Liu Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electrical Engineering Soochow University Suzhou CHINA Key Laboratory of Optoelectronic Technology & Systems Ministry of Education International R&D Center of Micro-nano Systems and New Materials Technology Chongqing University Chongqing CHINA

This paper presents an intelligent wireless self-sustained sensing cubic node, consisting of a rotational electromagnetic generator (R-EMG) for power supply and a versatile triaxial piezoelectric sensor (TPS) composed of three piezoelectric beams. This system enhances internet of things (IoT) sensing through cloud-based artificial intelligence (AI) computing. The cubic node provides power generation and multi-functional sensing capabilities, including vibration acceleration, frequency, and tilting angle. The design of the R-EMG ensures stable output at different rotational speeds, reaching a maximum output power of 66 mW at 550 rpm with an external load of 1.1 kΩ, allowing for self-sustaining energy generation for the node. Additionally, the cubic design enables the orthogonal placement of the three piezoelectric beams, enabling a broad response to vibrational energy from various directions. This flexibility allows for diverse applications across different frequencies and scenarios.

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