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

共 30906 条记录，以下是1731-1740 订阅

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Enhanced Accuracy in Underwater Communication Prediction Utilizing LSTM Over RNN for Improved Efficiency 2

Enhanced Accuracy in Underwater Communication Prediction Uti...

引用

2nd international conference on Automation, computing and Renewable systems, ICACRS 2023

作者： Sandhiya, V. Sandhiya, J. Hema Malini, A. Archana, T. Department of ECE Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

Underwater communication systems experience obstacles like confined bandwidth, elevated energy consumption, propagation lags, end-to-end delay (E-ED), complex media accessibility control, resource allocation, and power constraints. These challenges are resolved using energy-efficient protocols, which fall into two categories: constraint-basedand constraint-free. The study conducts a comprehensive assessment of constraint-free protocols in our project, considering environmental impact, data transmission rates, efficiency, energy consumption, E-ED, and propagation delay. This analysis provides insights into the pros and cons of these protocols, guiding future research in Underwater Wireless sensor Networks (UWSNs). The study also presents routing strategies and their strengths and weaknesses, identifying areas for further investigation. In this research work, algorithms such as Long-short Term memory algorithm (LSTM), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), and Artificial Neural Network (ANN), are employed and analyzed in terms of accuracy. The results demonstrate that Long-short Term memory algorithm (LSTM) outperforms better than other existing algorithms in terms of accuracy. © 2023 ieee.

关键词： Data Transmission Rate End to End delay (E-ED) Long-short term memory algorithm (LSTM) Recurrent Neural Network (RNN) Underwater wireless sensor network (UWSN)

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Symmetric and Asymmetric Algorithms for Susceptible Harmful in Wireless sensor Network on FPGA Platform 9

Symmetric and Asymmetric Algorithms for Susceptible Harmful ...

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9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Saranya, L. Arvind, C. Zhu Tang, Seng Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India University of Huaguoshan Department of Intelligent Robotics Huaguoshan Jileshijie Province China

ISBN: (纸本)9798350397376

WSNs are made up of a network of sensor nodes that are used to track environmental characteristics like pressure, temperature, etc. and communicate the data to the intended location. Networks must overcome obstacles including security, routing, problems with energy conservation, scalability, dependability, secrecy, integrity, authentication, and others when transmitting information. Different routing systems, including data-centric, location- and mobility-based, Quality of Service (QoS)-based, and multipath routing protocols, have been employed to address these issues. Even while these routing methods guarantee a successful transaction, the network still has to manage intermediary assaults that can completely alter the content of messages being carried. Therefore, a number of cryptographic approaches have been used to minimise the network. The newly developed symmetric and asymmetric algorithms are susceptible to problems with route latency, area overhead, and on-chip power consumption. Thus, to reduce these harmful actions in WSNs, an efficient reduced key size and high security based cryptographic method must be used. Utilizing the limited network resources, the main goal of the study is to increase network security while reducing network overhead and path delay-related problems. © 2023 ieee.

关键词： Network security

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Managing Smart Urban Transportation with the integration of Big Data Analytic Platform 6

Managing Smart Urban Transportation with the integration of ...

引用

6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Kunjir, Sanika Nitin Patil, Sakshi Sanjay Hingane, Bhakti Sunil Pagariya, Janvi Anand Rashid, Mamoon Vishwakarma University Pune India

ISBN: (纸本)9798350304480

Urban traffic management is a major challenge for cities worldwide, but with the help of technology, such as big data analytics and intelligent transport systems, cities are working to improve their situations. An integrated big data analytics platform has the potential to change transportation networks for the better, providing real-time insights that can be used for decisions. By collecting and analyzing real-time data from various sources, such as traffic sensors and GPS, we can predict traffic flow and create plans more efficiently. This paper will explore how an integrated big data analytics platform can be used to improve urban transportation management. It discusses some of the challenges that urban transportation systems face and provides insights from government officials and industry experts. The importance of this paper reviews fundamental concepts such as big data analytics and smart cities so that you have a better understanding of what this technology can do. Afterwards, it identifies opportunities for improving urban transportation networks using big data analytics. © 2023 ieee.

关键词： Big data data analytics data quality distributed environment public transport management smart city

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Learning-based Co-Design of distributed Edge Sensing and Transmission for Industrial Cyber-Physical systems 19

Learning-based Co-Design of Distributed Edge Sensing and Tra...

引用

ieee 19th international conference on Industrial Informatics (INDIN)

作者： Jin, Tiankai Ji, Zhiduo Zhu, Shanying Chen, Cailian Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781728143958

Industrial cyber-physical systems (ICPS) refer to an emerging generation of intelligent systems, where distributed data acquisition is of great importance and is influenced by data transmission. In the improvement of the overall performance of sensing accuracy and energy efficiency, sensing and transmission are tightly coupled. Due to the unknown transmission channel states in the harsh industrial field environment, intelligently performing sensor scheduling for distributed sensing is challenging. In this paper, edge computing technology is utilized to enhance the level of intelligence at the edge side and deploy advanced scheduling algorithms. We propose a learning-based distributed edge sensing-transmission co-design (LEST) algorithm under the coordination of the sensors and the edge computing unit (ECU). Deep reinforcement learning is applied to perform real-time sensor scheduling under unknown channel states. The conditions for the existence of feasible scheduling policies are analyzed. The proposed algorithm is applied to estimate the slab temperature in the hot rolling process, which is a typical ICPS. The simulation results demonstrate that the overall performance of LEST is better than other suboptimal algorithms.

关键词： Industrial cyber-physical systems (ICPS) distributed edge sensing sensing-transmission co-design deep reinforcement learning

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Gaming on the Edge: Performance Issues of distributed Online Gaming 23

Gaming on the Edge: Performance Issues of Distributed Online...

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23rd international-Federation-for-Information-Processing (IFIP) Networking conference (IFIP Networking)

作者： Olliaro, D. Mancuso, V. Castagno, P. Sereno, M. Marsan, M. Ajmone Ca Foscari Univ Venice Venice Italy IMDEA Networks Inst Leganes Spain Univ Torino Turin Italy Consorzio Nazl Interuniv Telecomunicaz CNIT Parma Italy

ISBN: (纸本)9798350390605;9783903176638

We study the performance of online games played over a platform that implements gaming as a service (GaaS) in a mobile network slice that hosts concatenated virtual network functions (VNFs) at the edge. The distributed gaming architecture is based on edge computing facilities, whose utilization must be carefully planned and managed, so as to satisfy the stringent performance requirements of game applications. The game manager must consider the latency between players and edge server VNFs, the capacity and load of edge servers, and the latency between edge servers used by interacting players. This calls for a careful choice about the allocation of players to edge server VNFs, aiming at extremely low latency in interactions resulting from player's commands. We develop an analytical model, which we validate with experiments in the wild, and show that, under several combinations of system parameters, deploying gaming VNFs at the edge can deliver better performance with respect to cloud gaming, in spite of the complexities arising from the distribution of gaming VNFs over edge servers. Our analytical model provides a useful tool for edge gaming systems performance prediction, thus supporting the management of GaaS applications.

关键词： Online gaming GaaS Performance model

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DSP Architectures of Covariance Intersection Fusion Algorithm for Automotive sensor Fusion 14

DSP Architectures of Covariance Intersection Fusion Algorith...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Praharshita, D.S.L. Achala, G. Srihari, Pathipati Acharya, U. Shripathi Pardhasaradhi, Bethi India National Institute of Technology Karnataka ECE Dept. India Continental Autonomous Mobility India Pvt Ltd Adas Dept. India

ISBN: (纸本)9798350335095

The data fusion from sensors within the automotive vehicle is vital for improved accuracy and safety. The centralized and information matrix fusion (IMF) algorithms are famous for providing an optimal fusion estimate. However, the IMF is not viable in automotive sensor fusion applications due to the limited bandwidth and low hardware resources. Hence, distributed fusion technology is widely adopted in the automotive sensor applications to achieve high-speed and low-area realizations. This paper proposes three digital signal processing (DSP) architectures for covariance intersection (CI) fusion algorithm: Pipelined-traditional CI, adder-ladder CI, and pipelined adder-ladder CI. The proposed DSP architectures are evaluated with hardware resource consumption (multipliers, adders, and delays), maximum achievable frequency, and latency of the architecture. In addition, proposed CI algorithms for Digital Signal Processing (DSP) architectures are compared with IMF DSP architectures. The hardware resources and optimal pipeline stages required for CI with respect to N number of sensors are provided. The traditional pipeline algorithm requires N number of stages where as the proposed pipelined version of adder-ladder CI requires a N-1 pipeline stage with additional 7N-1 and 7N-3 delay elements for even and odd number of sensors to achieve the overall system operating frequency to an operation of multiplier. The proposed DSP architectures are suitable for automotive sensor fusion due to their high operating frequency and low hardware resources. © 2023 ieee.

关键词： Digital signal processing

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distributed Compression for Partially Cooperating sensors and Gaussian Relevant Signals 26

Distributed Compression for Partially Cooperating Sensors an...

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26th international ITG Workshop on Smart Antennas, WSA 2023 and 13th conference on systems, Communications, and Coding, SCC 2023

作者： Steiner, Steffen Kuehn, Volker Institute of Communications Engineering University of Rostock Rostock18119 Germany

ISBN: (纸本)9783800760510

This paper considers the well known Chief Executive Officer (CEO) scenario where each sensor in a network observes noisy versions of the same signal of interest. These measurements have to be locally compressed in order to forward them over capacity limited links to a common receiver. To improve the performance in an information theoretic measure the non-cooperative CEO scenario is extended allowing partial cooperation among sensors. Therefore, each sensor not only forwards information to the common receiver, but also to a neighboring sensor via capacity limited links. This extension of the non-cooperative CEO scenario changes the statistical dependencies of the involved random variables. A heuristic approach based on the algorithmic solution of the non-cooperative CEO problem is considered, which optimizes the quantizer of all sensors in a greedy manner. Simulations demonstrate that exchanging instantaneous side-information during runtime can increase the overall performance of the CEO problem in terms of information theoretic measures. However, there still remains a gap to a fully cooperative CEO scenario, where each sensor has access to the measurements of all sensors. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Heuristic methods

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Proceedings of the 2023 ieee/ACM international conference on Advances in Social Networks Analysis and Mining, ASONAM 2023

Proceedings of the 2023 IEEE/ACM International Conference on...

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15th ieee/ACM international conference on Advances in Social Networks Analysis and Mining, ASONAM 2023

ISBN: (纸本)9798400704093

The proceedings contain 122 papers. The topics discuss include: centralization problem for opinion convergence in decentralized networks;annotators’ perspectives: exploring the influence of identity on interpreting misogynoir;maximizing influence with graph neural networks;structure and dynamics of a charitable donor co-attendance network;how popularity shapes user interactions in tech-related online communities;together apart: decoding support dynamics in online COVID-19 communities;classifying severe weather events by utilizing social sensor data and social network analysis;the art of active listening;analyzing bias in recommender systems: a comprehensive evaluation of YouTube’s recommendation algorithm;exploring inequity in park usage amidst the COVID-19 pandemic;and understanding online attitudes with pre-trained language models.

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Online Resource Allocation in Edge computing Using distributed Bidding Approaches 18

Online Resource Allocation in Edge Computing Using Distribut...

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18th ieee international conference on Mobile Ad hoc and Smart systems (ieee MASS)

作者： Rublein, Caroline Mehmeti, Fidan Towers, Mark Stein, Sebastian La Porta, Thomas F. Penn State Univ University Pk PA 16802 USA Univ Southampton Southampton Hants England

ISBN: (纸本)9781665449359

Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients (tasks) to make allocation decisions. This provides robustness and does not require service providers to share information about their configurations or workloads. We propose a two-round bidding approach of assigning tasks to edge cloud servers, while taking into account various processing requirements and server constraints. We consider cases in which all jobs have equal utility, cases where jobs have different utilities but users do not disclose these utilities to servers, and cases where users disclose the utility of their jobs to servers. We evaluate the performance using extensive realistic simulations. Results show that our approach is very close to an optimal assignment, with discrepancy not exceeding 5%.

关键词： Edge cloud computing Optimization Bidding

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Multi-Positive Sample Quantum Contrastive Learning for Human Activity Recognition

Multi-Positive Sample Quantum Contrastive Learning for Human...

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2024 ieee Global Communications conference, GLOBECOM 2024

作者： Ren, Yanhui Wang, Di An, Lingling Mao, Shiwen Wang, Xuyu Xidian University Guangzhou Institute of Technology Guangzhou China Xidian University School of Computer Science and Technology Xi'an China Auburn University Department of Electrical and Computer Engineering AuburnAL United States Florida International University Knight Foundation School of Computing & Information Sciences MiamiFL United States

ISBN: (纸本)9798350351255

Human activity recognition (HAR) based on wearable devices has become an active research direction in the field of ubiquitous computing, and has a wide range of Internet of Things (IoT) applications. Unfortunately, it is challenging to obtain large amounts of labeled sensing data, and manual annotation is time-consuming and labor-intensive, making it impossible for the extensive deployment of HAR systems. Consequently, self-supervised learning has emerged to address this challenge by training on unlabeled data. However, traditional contrastive learning fails to simulate more sample diversity problems caused by environmental heterogeneity and sensor heterogeneity. In this paper, we propose a multi-positive sample quantum contrastive learning (MPSQCL) framework. By increasing the positive samples for contrastive learning and leveraging the advantages of quantum machine learning (QML) techniques, the richer features of input samples are extracted to improve the robustness and generalization of the model. Moreover, we design a new contrastive loss function to adapt to multiple positive sample contrastive learning scenarios. Finally, we validate the effectiveness of the proposed framework on several publicly available HAR datasets. © 2024 ieee.

关键词： Contrastive Learning

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