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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Gao, Yingmeng Zhu, Jie Huang, Haiping Chen, Chen Nanjing University of Posts & Telecommunications Nanjing210003 China Academy of Sciences State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Beijing100864 China Jiangsu High Technology Reasearch Key Laboratory of Wireless Sensor Networks Nanjing210023 China Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798350331684

When it comes to the fifth generation, collaborative edge computing is preferred for offloading computation-intensive tasks of low-latency applications in Internet of Things. In this paper, we consider the partial task offloading problem where tasks can be divided into subtasks and offloaded to nearby devices. The flow scheduling problem is integrated in the offloading process, i.e., multiple and conflicting route paths are considered. We propose the latency-aware partial task offloading framework (LaPTOF) for the considered problem. LaPTOF integrates a weighted priority ranking strategy (WPRS) which generates multiple solutions with different weights on task arrival time and the task processing time. A feasible solution generation method (FSGM) is designed where the best offloaded proportion of tasks are computed, and the appropriate offloaded devices and offload paths are determined. The proposed LaPTOF has an advantage in providing a scheduling plan that minimizes total completion time of task offloading in a shorter duration. The experimental results show that the proposal is suitable for the considered problem compared with JPOFH and its variants on both effectiveness and efficiency. © 2023 IEEE.

关键词： Internet of things

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Mean Shift Based Collaborative Localization with Dynamically Clustering for Wireless Sensor networks

Mean Shift Based Collaborative Localization with Dynamically...

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WRI International Conference on Communications and Mobile Computing, CMC

作者： Quan Zhou Xiaowei Li Yongjun Xu Institute of Computing Technology Key Laboratory of Computer System and Architecture Chinese Academy and Sciences Beijing China Institute of Computing Technology Laboratory of Sensor Networks Chinese Academy and Sciences Beijing China

Mean shift is a nonparametric method to estimate the density gradient, which iteratively computes the mean of observations and shifts to the centroid until reach a predefined end condition. This paper proposes a mean shift based scheme for localization and tracking in wireless sensor *** shift is a generalized framework, and range-free centroid and range-based W-Centrod are both the special cases of this generalized framework. A dynamically clustering protocol is proposed to support the generalized framework. Analysis and simulation results show that the communication overhead and jitter are reduced to 1/3 of traditional algorithms. The positioning accuracy of proposed algorithm with optimal weight increases 28% and 11% in comparison to Centroid and W-Centroid.

关键词： Wireless sensor networks computer networks Clustering algorithms Laboratories Protocols Equations Mobile communication Mobile computing International collaboration computer architecture

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Modeling for Web Services Composition System with Restricted Resources

Modeling for Web Services Composition System with Restricted...

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International Conference on Advances in System Simulation, SIMUL

作者： Huaizhou Yang Zengzhi Li State key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Institute of Computer Architecture & Networks Xi'an Jiaotong University Xi'an China

The Web services composition system usually has restricted resources in many practical applications. The system performance is closely related to the system resource. Firstly, a basic generalized stochastic Petri net model, constructed via service composition plan or language mapping for the purpose of performance presented. Nevertheless, the basic model can not reflect the characteristics of system resource restriction exactly. Therefore, through analyzing the various resource restrictions in Web services composition system, a series of extended modeling rules and methods are presented in order to acquire exact performance analysis results. The experiments indicate the difference of performance analysis results between basic model in ideal situation and extended model with restricted resources.

关键词： Web services Performance analysis Petri nets Stochastic processes Service oriented architecture Laboratories Telecommunication switching System performance Artificial intelligence System recovery

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Smallest Enclosing Circle Based Localization Approach for Wireless Sensor networks

Smallest Enclosing Circle Based Localization Approach for Wi...

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WRI International Conference on Communications and Mobile Computing, CMC

作者： Quan Zhou Xiaowei Li Yongjun Xu Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences Beijing China Laboratory of Sensor Networks Institute of Computing Technology Chinese Academy and Sciences Beijing China

A novel range-free localization approach, smallest enclosing Circle based Localization (SECL), has been proposed. This approach uses the center of smallest enclosing circle of neighboring anchor nodes to estimate the position of target. Comparing to centroid, SECL considers the geometrical coverage of anchors rather than the mass center of them. Consequently, SECL is robust to imperfect topology, especially when the topology of anchors is depolyed unevenly. Simulation results show that SECL outperforms Centroid by an average of 10%, and even better when the topology is not uniform.

关键词： Wireless sensor networks Network topology computer networks Laboratories Robustness Computational modeling Equations Hardware Mobile communication Mobile computing

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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A detailed experimental performance evaluation on TCP over UBR

A detailed experimental performance evaluation on TCP over U...

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IEEE International Conference on ATM

作者： L. Jaussi M. Lorang J. Nelissen Telecommunications Laboratory Swiss Federal Institute of Technology Lausanne Switzerland Inst. of Communication Networks and Computer Engineering University of Stuttgart Stuttgart Germany Network Architecture Traffic Technology Team Alcatel-Alsthom Corporate Research Center Belgium

The ATM service category UBR is intended for non-real-time applications that do not require guaranteed QoS commitments. With additional, relatively inexpensive control functions such as packet discard schemes, UBR could become a cost-effective alternative for the transmission of data traffic; offering a straightforward and flexible solution as opposed to nrt-VBR and GFR that applies stricter traffic specifications as well as ABR with its sophisticated and complex rate-control protocol. This paper presents the results obtained from a comprehensive set of experiments with TCP over UBR, comprising of measurements taken on different protocol layers. The goal is to experimentally investigate the performance of UBR to carry TCP traffic, to evaluate the performance gain achievable by packet discard schemes and TCP parameter tuning, to study the influence of the TCP implementation, and in a final step, to relate the measurements to simulation results.

关键词： Asynchronous transfer mode Traffic control Switches Protocols Size measurement Laboratories Communication networks computer networks Telecommunication computing computer architecture

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Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts - A Prosumager Smart Fog Computing Perspective

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IEEE Internet of Things Journal 2023年第21期10卷 18898-18908页

作者： Vohnout, Rudolf Bukovsky, Ivo Chou, Shuo-Yan Geyer, Jakub Budik, Ondrej Sharma, Rohit Prokysek, Milos Horvath, Tomas Wyckmans, Annemie University of South Bohemia in České Budějovice Faculty of Science Department of Computer Science České Budějovice37005 Czech Republic National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan SRM Institute of Science and Technology NCR Campus Department of Electronics and Communication Engineering Ghaziabad201204 India Brno University of Technology Department of Telecommunications Brno61600 Czech Republic CESNET a.l.e Department of Optical Networks Prague16000 Czech Republic NTNU-Norwegian University of Science and Technology Department of Architecture and Planning Trondheim7491 Norway

Living lab, one of the recent emerging smart city concepts, faces long-term sustainability challenges associated with its complexity and breadth of use. To be efficient, it must rely on comprehensive set of information distributed appropriately among all stakeholders to unleash its full innovation potential. This is especially true in the case of positive energy districts (PEDs), where timely data dissemination is essential for prosumager decisions and their greedy behavior. This article interconnects intelligent information exchange, supported by ultralow latency hybrid access network infrastructure, with the clever use of available fog computing resources to properly disseminate complex energy details to all participating entities. As the optimal task offloading for effective information distribution constitutes the convergence problem, we reintroduced higher order neural units. These units contribute to sustaining both computational and energy efficiency, as well as the balance of the entire system. We have achieved a reliable hourly energy consumption prediction with a computationally very lightweight alternative to commonly used deep neural network approaches that can be deployed on available smart appliances with ease. The application and simulation were performed on the data set provided by one of Europe's smart city pioneers, where the prosumager PED transition has already started. © 2023 IEEE.

关键词： Decision making

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Energy-Constrained Task Scheduling in Heterogeneous Distributed Systems

Energy-Constrained Task Scheduling in Heterogeneous Distribu...

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International Conference on computer Supported Cooperative Work in Design

作者： Chen Chen Jie Zhu Haiping Huang Yingmeng Gao Nanjing University of Posts & Telecommunications Nanjing China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient.

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Latency-aware Partial Task Offloading in Collaborative Edge Computing

Latency-aware Partial Task Offloading in Collaborative Edge ...

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International Conference on computer Supported Cooperative Work in Design

作者： Yingmeng Gao Jie Zhu Haiping Huang Chen Chen Nanjing University of Posts & Telecommunications Nanjing China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing China Jiangsu High Technology Reasearch Key Laboratory of Wireless Sensor Networks Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

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