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An Efficient Big Data Gathering in Wireless sensor Network using Reconfigurable Node Distribution Algorithm 4

An Efficient Big Data Gathering in Wireless Sensor Network u...

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4th international conference on Cognitive computing and Information Processing, CCIP 2022

作者： Mallikarjunaswamy, S. Basavaraju, N.M. Sharmila, N. Mahendra, H.N. Pooja, S. Deepak, B.L. JSS Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India JSS Academy of Technical Education Bengaluru India JSS Science and Technology University Mysuru India K S Insitute of Technoloy Dept of Electronics and Communication Engineering Bengaluru India R.L.Jalappa Institute of Technology Dept of Computer Science and Engineering Kodigehalli India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456487

In recent days, communication process is performed in wireless sensor networks (WSNs). the WSN is effectively reconstructed and accurately communicates the information to each corner of high-density nodes of cellular area. the main objective of this paper is to improve the data gathering of all sensor nodes and efficiently optimize the energy utilisation of each node in WSNs. the cluster head is identified in highly node distributed WSNs. the sink nodes during big data gathering are effectively utilized in WSNs with the help of Reconfigurable Node Distribution Algorithm (RNDA). the proposed algorithm procedure has been followed to address the big data gathering location in WSNs and mobilize the sink nodes in optimized location for proper communication in WSNs. the performance analysis and comparison between proposed and existing methods have been carried out with respect to energy efficiency, packet delivery ratio, packet ratio and transmission energy. the simulation result shows that the proposed method reduces energy consumption by 2 % in high density sensor node network communication process. the proposed method effectively selects the cluster head in WSNs to enhance the throughput packet delivery ratio and transmission energy. © 2022 ieee.

关键词： Big data

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Issues and Algorithm of distributed Shared Memory 4

Issues and Algorithm of Distributed Shared Memory

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4th international conference on Innovative computing (ICIC)

作者： Ahmad, Mian Basam Sagheer, Sundas Univ Management & Technol Dept Comp Sci Lahore Pakistan Univ Management & Technol Dept Software Engn Lahore Pakistan

ISBN: (纸本)9781665400916

Internet of things is a rapidly growing industry, and there are more connected devices than humans. there are unlimited internet of things devices around us. the scope of these devices has increased manifold in recent years in different areas like healthcare, unmanned vehicles, automatic monitoring systems, and smart homes, etc. these connected devices produce lots of data that need to be processed to achieve the goal of automation in daily life. the cloud provides many centralized solutions to process data using powerful resources in real-time, but there could be latency issues due to increased network traffic. So a distributed solution is much needed to meet this growing demand of processing data in parallel. In this paper, we propose a distributed shared memory [18] abstraction for the internet of things that uses a graph-aware partitioning algorithm and uses smart message passing techniques to process data in distributed and parallel fashion [19].

关键词： Internet of things

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Deep learning-based slip-trip falls and near-falls prediction model using a single inertial measurement unit sensor for construction workplace 4

Deep learning-based slip-trip falls and near-falls predictio...

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4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Yuhai, Oleksandr Kim, Hyunggun Choi, Ahnryul Mun, Joung Hwan Sungkyunkwan University Department of Bio-Mechatronic Engineering Suwon Korea Republic of Catholic Kwandong University Department of Biomedical Engineering Gangneung Korea Republic of

ISBN: (纸本)9798350300192

Slip-trip falls are one of the leading causes of injury in dynamic constructive workplace environments. To solve the problem of slips-trip in the construction industry, it is important not only to predict falls in order to be able to apply various fall prevention systems, but also to detect and monitor slip-trip near-falls in order to eliminate various dangerous anomalies in a timely manner. In this study, we propose a deep learning-based method for predicting and classifying activities of daily living (ADLs), trip falls, slip falls, trip near-falls and slip near-falls using a waist-attached wearable device with an embedded single inertial measurement unit (IMU) sensor containing a 3-axis accelerometer and a gyroscope. A total of 34 young and healthy participants took part in experiment to collect accelerometer and gyroscope data while performing 8 types of ADLs, slip-trip falls, and slip-trip near-falls. the data was processed and then 30 features were extracted from them. the resulting feature data was then transformed using bicubic interpolation to fit the input data size of the modified interpretation of LeNet-5 Convolutional Neural Network (CNN) deep learning algorithm. After completing 5-folds cross validation with 5 evaluation criteria, the proposed prediction and classification method showed high performance with an accuracy of about 0.9039, a specificity of about 0.9753 and F1-score of about 0.9049. therefore, it is believed that the method proposed in this study can be used not only to increase accuracy by reducing false alarms of systems for early detection and prevention of slip-trip falls, but also for various systems for detecting and timely elimination of dangerous anomalies in the construction workplace. © 2023 ieee.

关键词： Forecasting

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Coupled sensor Configuration and Path-Planning in a Multimodal threat Field 4th

Coupled Sensor Configuration and Path-Planning in a Multi...

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4th international conference on Dynamic Data Driven Applications systems, DDDAS 2022

作者： Laurent, Chase L. St. Cowlagi, Raghvendra V. AMETEK Inc. PeabodyMA01960 United States Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (纸本)9783031526695

A coupled path-planning and sensor configuration method is proposed. the path-planning objective is to minimize exposure to an unknown, spatially-varying, and temporally static scalar field called the threat field. the threat field is modeled as a weighted sum of several scalar fields, each representing a mode of threat. A heterogeneous sensor network takes noisy measurements of the threat field. Each sensor in the network observes one or more threat modes within a circular field of view (FoV). the sensors are configurable, i.e., parameters such as location and size of field of view can be changed. the measurement noise is assumed to be normally distributed with zero mean and a variance that monotonically increases with the size of the FoV, emulating the FoV v/s resolution trade-off in most sensors. Gaussian Process regression is used to estimate the threat field from these measurements. the main innovation of this work is that sensor configuration is performed by maximizing a so-called task-driven information gain (TDIG) metric, which quantifies uncertainty reduction in the cost of the planned path. Because the TDIG does not have any convenient structural properties, a surrogate function called the self-adaptive mutual information (SAMI) is considered. sensor configuration based on the TDIG or SAMI introduces coupling with path-planning in accordance with the dynamic data-driven application systems paradigm. the benefit of this approach is that near-optimal plans are found with a relatively small number of measurements. In comparison to decoupled path-planning and sensor configuration based on traditional information-driven metrics, the proposed CSCP method results in near-optimal plans with fewer measurements. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motion planning

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FakeSens: A Social Sensing Approach to COVID-19 Misinformation Detection on Social Media 17

FakeSens: A Social Sensing Approach to COVID-19 Misinformati...

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17th Annual ieee international conference on distributed computing in sensor systems (ieee DCOSS)

作者： Kou, Ziyi Shang, Lanyu Zhang, Yang Youn, Christina Wang, Dong Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781665439299

Social sensing is emerging as an effective and pervasive sensing paradigm to collect timely data and observations from human sensors. this paper focuses on the problem of COVID-19 misinformation detection on social media. Our work is motivated by the lack of COVID-specific knowledge in current misinformation detection solutions, which is critical to assess the truthfulness of social media claims about the emerging COVID19 disease. In this paper, we leverage human intelligence on a crowdsourcing platform to obtain essential knowledge facts for detecting the COVID-19 misinformation on social media. Two critical challenges exist in solving our problem: i) how to efficiently acquire accurate and timely knowledge that is both inclusive and specific to COVID-19? ii) How to effectively coordinate the efforts from both expert and non-expert workers to detect COVID-19 misinformation? To address these challenges, we develop FakeSens, a social sensing based crowd knowledge graph approach that explicitly explores the knowledge facts specific to COVID-19 and models the reliability of different types of crowd workers to capture the misleading COVID-19 claims Evaluation results on a real-world dataset show that FakeSens significantly outperforms state-of-the-art baselines in accurately detecting misleading claims of COVID-19 on social media.

关键词： COVID-19 Knowledge engineering Social networking (online) Human intelligence Performance gain sensor systems Graph neural networks

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Energy Optimization to Extend Network Lifetime for IoT based Wireless sensor Networks 4

Energy Optimization to Extend Network Lifetime for IoT based...

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4th international conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Shreyas, J. Deepa, H. Udayaprasad, P.K. Chouhon, Dharmendra Srinidhi, N.N. Dilip Kumar, S.M. Creencia Technologies Private Limited Indiranagar Bangalore560038 India School of Computer Science and Engineering Reva University Bangalore India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9781665401180

the issue of extending the lifetime of Wireless sensor Networks (WSN) designed for Internet of things (IoT) has been rigorously explored by researchers over the last two decades. IoT applications for real time are in demand for monitoring and complex analysis. In IoT, sensor nodes are powered by battery backup which is considered as energy constrain while performing various task. Maximizing the network lifetime of network nodes has become a challenging issue. In past researchers have focused on extending network lifetime of nodes through guarantee lifetime protocols, however some issues such as sensing coverage, scheduling and network connectivity are not been considered. therefore, in this work energy efficient routing scheme (EERS) is proposed for duty cycled nodes by considering sleep/awake mode and increased data rates to achieve higher packet delivery ratio and to extend network lifetime and optimize the energy consumption of the network. Sleep/awake modes for nodes are periodically assigned by sink node based on nodes residual energy, coverage area and total active time. © 2022 ieee

关键词： Energy efficiency

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Secure parallel Outsourcing Scheme for Large-scale Matrix Multiplication on distributed Cloud Servers 29

Secure parallel Outsourcing Scheme for Large-scale Matrix Mu...

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29th ieee international conference on Parallel and distributed systems, ICPADS 2023

作者： Wang, Yinlong Tao, Yunting Kong, Fanyu Gu, Zhaoquan Yu, Jia Zhang, Hanlin Binzhou Polytechnic College of Information Engineering Shandong Binzhou256603 China Shandong University School of Software Shandong Jinan250101 China Shandong Sansec Information Technology Co. Ltd Jinan250101 China School of Computer Science and Technology Shenzhen China Qingdao University College of Computer Science and Technology Shandong Qingdao266071 China

ISBN: (纸本)9798350330717

Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. this paper presents a parallel matrix multiplication outsourcing scheme based on Cannon's algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme's correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data. © 2023 ieee.

关键词： Cloud computing Data privacy Matrix multiplication Parallel outsourcing

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Research on the Characteristics of Local Discharge Ground Wave sensors in New Energy Stations 3

Research on the Characteristics of Local Discharge Ground Wa...

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3rd international conference on Smart Grids and Energy systems, SGES 2024

作者： Xie, Peng Ma, Jinxiong Tang, Zhanjun Li, Jielong Chen, Tianle Li, Yujiang Yunnan Longyuan New Energy Co. Ltd. 4th Floor Building 2 Huaxia Plaza No. 1037 Dianchi Road Xishan District Yunnan Province Kunming City China Faculty of Information Engineering and Automation Kunming University of Science and Technology No. 727 Jingming South Road Chenggong District Yunnan Province Kunming City China Yunnan Power Grid Co. Ltd. Dali Power Supply Bureau Block B No. 42 Longxi Road Dali Bai Autonomous Prefecture Yunnan Province Dali City China

ISBN: (纸本)9798331539634

there are many 35kV cable lines in new energy stations, and the frequency of faults is also high. Cable faults are an important factor affecting the safe and stable operation of new energy. Partial discharge, as an early indicator of insulation degradation in power equipment, has been proven to be an effective fault warning information. Conducting partial discharge detection in distribution network lines can timely detect potential insulation defects or faults and provide new solutions to improve the level of distribution network operation and maintenance. therefore, it is necessary to conduct research on the performance of partial discharge sensor materials, turns, and other characteristics of power equipment in new energy stations, and design a partial discharge detection device suitable for the characteristics of new energy stations. this device can effectively provide early warnings for partial discharge in new energy stations and is of great significance for the safe and stable operation of new energy stations. TEV sensor is a sensor used to detect the transient ground voltage generated during partial discharge of high-voltage equipment. When partial discharge occurs, pulsating voltage is induced on the surface of the ferromagnetic shell of high-voltage equipment. TEV sensors, as transient ground voltage couplers, can detect the waveform and amplitude of this pulsating voltage, providing a basis for judging and locating partial discharge in high-voltage equipment. therefore, optimizing the performance design of TEV sensors can effectively improve the detection level of partial discharge. © 2024 ieee.

关键词： Partial discharges

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Object-level 3D Semantic Mapping using a Network of Smart Edge sensors 6

Object-level 3D Semantic Mapping using a Network of Smart Ed...

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6th ieee international conference on Robotic computing, (IRC)

作者： Hau, Julian Bultmann, Simon Behnke, Sven Univ Bonn Autonomous Intelligent Syst Bonn Germany

ISBN: (纸本)9781665472609

Autonomous robots that interact with their environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. the scene understanding can further be improved by including object-level information in the map. In this work, we extend a multi-view 3D semantic mapping system consisting of a network of distributed smart edge sensors with object-level information, to enable downstream tasks that need object-level input. Objects are represented in the map via their 3D mesh model or as an object-centric volumetric sub-map that can model arbitrary object geometry when no detailed 3D model is available. We propose a keypoint-based approach to estimate object poses via PnP and refinement via ICP alignment of the 3D object model with the observed point cloud segments. Object instances are tracked to integrate observations over time and to be robust against temporary occlusions. Our method is evaluated on the public Behave dataset where it shows pose estimation accuracy within a few centimeters and in real-world experiments with the sensor network in a challenging lab environment where multiple chairs and a table are tracked through the scene online, in real time even under high occlusions.

关键词： Object-level mapping semantic scene understanding intelligent sensors and systems distributed perception

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Multi-UAV Formation Control Based on distributed Model Predictive Control

Multi-UAV Formation Control Based on Distributed Model Predi...

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4th ieee international conference on Cyborg and Bionic systems (CBS)

作者： Liu, Si-Sheng Ge, Ming-Feng Liu, Zhi-Wei China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Iluazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9781665490283

this paper presents a distributed model predictive control (DMPC) algorithm for multiple unmanned aerial vehicle (Multi-UAV) formation tracking. this algorithm takes into account the rapidly formation and maintenance of multi-UAV formation, and each UAV only needs to accept the information of neighbor nodes (UAVs) for real-time online rolling optimization. the DMPC scheme enables all followers to form the desired formation while tracking the leader. the Lyapunov function is constructed by the sum of local cost functions, and it is proved that the asymptotic stability can be guaranteed under the conditions of satisfying the proposed terminal constraints and weights. Finally, the effectiveness of the proposed algorithm is illustrated by simulation.

关键词： multiple unmanned aerial vehicles formation control model predictive control rolling optimization

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