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2021 IEEE 7th international conference on Collaboration and Internet computing CIC 2021

2021 IEEE 7th International Conference on Collaboration and ...

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international conference on collaborative computing: networking, applications and worksharing (CollaborateCom)

2021 IEEE 7th international conference on Collaboration and Internet computing CIC 2021.

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Distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning 17

Distributed Task Offloading based on Multi-Agent Deep Reinfo...

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17th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Hu, Shucheng Ren, Tao Niu, Jianwei Hu, Zheyuan Xing, Guoliang Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou 450001 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781665406680

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the 'curse of dimensionality' in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

关键词： task offloading mobile edge computing multi-agent deep reinforcement learning

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A collaborative Optimization-Guided Entity Extraction Scheme 17th

A Collaborative Optimization-Guided Entity Extraction Scheme

引用

17th EAI international conference on collaborative computing: networking, applications, and worksharing, CollaborateCom 2021

作者： Peng, Qiaojuan Luo, Xiong Shen, Hailun Huang, Ziyang Chen, Maojian School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Shunde Graduate School University of Science and Technology Beijing Foshan528399 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Ouyeel Co. Ltd. Shanghai201999 China

ISBN: (纸本)9783030926373

Entity extraction as one of the most basic tasks in achieving information extraction and retrieval, has always been an important research area in natural language processing. Considering that most of the traditional entity extraction methods need to manually adjust their hyperparameters, it takes a lot of time and is easy to fall into local optimality. To avoid such limitations, this paper proposes a novel scheme to extract named entities, where the model hyperparameters are automatically adjusted to improve the performance of entity extraction. Here, the proposed scheme is composed of bi-directional encoder representation from transformers (BERT) and conditional random field (CRF). Specifically, through the fusion of collaborative computing paradigm, particle swarm optimization (PSO) algorithm is utilized in this paper to search for the best value of hyperparameters automatically in a cooperative way. the experimental results on two public datasets and a steel inquiry dataset verify that our proposed scheme can effectively improve the performance of entity extraction. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Particle swarm optimization (PSO)

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Identification of Sequential Feature for Volcanic Ash Cloud Using FNN-LSTM collaborative computing 16th

Identification of Sequential Feature for Volcanic Ash Cloud ...

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16th EAI international conference on collaborative computing: networking, applications, and worksharing, CollaborateCom 2020

作者： Liu, Lan Li, Cheng-fan Sun, Xian-kun Shi, Jiangang School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute of Advanced Communication and Data Science Shanghai University Shanghai200444 China Shanghai Shangda Hairun Information System Co Ltd. Shanghai200444 China

ISBN: (纸本)9783030675363

collaborative computing performs quickly and accurately the task via combining the multimedia, multi-methods, and multi-clients. Analyzing of traditional feedforward neural network (FNN), long short-term memory (LSTM) neural networks and remote sensing data, this paper proposes a new identification method of sequential feature based on FNN-LSTM collaborative calculation in the volcanic ash cloud monitoring. In this method, combining remote sensing data, the FNN network is used firstly to construct the identification model of volcanic ash cloud. Next, the LSTM network is used to identify the sequential feature of dynamic changes in volcanic ash cloud based on the text data of the volcanic ash report. And then the simulation and true volcanic ash cloud case is performed and analyzed. the experimental results show that: 1) the proposed method is high in training accuracy with 76.54% and testing accuracy with 77%, respectively, and has obvious advantages for small-scale data volumes;2) the total accuracy and RMS of the simulation analysis reached 79.05% and 0.0149, respectively, it verified the feasibility and effectiveness in the prediction of spatiotemporal evolution;3) the anti-noise property and the image segmentation effect is good, the obtained sequential feature of the volcanic ash cloud are closer to the actual diffusion. It can provide a reference for sequential feature extraction and dynamic monitoring of volcanic ash cloud in complex environments. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Long short-term memory

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ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments

ArcaDB: A Disaggregated Query Engine for Heterogenous Comput...

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IEEE international conference on Cloud computing, CLOUD

作者： Kristalys Ruiz-Rohena Manuel Rodriguez-Martínez Department of Electrical and Computer Engineering University of Puerto Rico Mayagüez Department of Computer Science and Engineering University of Puerto Rico Mayaguez

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

Modern enterprises rely on data management systems to collect, store, and analyze vast amounts of data related to their operations. Nowadays, clusters and hardware accelerators (e.g., GPUs, TPUs) have become a necessity to scale with the data processing demands in many applications related to social media, bioinformatics, surveillance systems, remote sensing, and medical informatics. Given this new scenario, the architecture of data analytics engines must evolve to take advantage of these new technological trends. In this paper, we present ArcaDB: a disaggregated query engine that leverages container technology to place operators at compute nodes that fit their performance profile. In ArcaDB, a query plan is dispatched to worker nodes that have different computing characteristics. Each operator is annotated with the preferred type of compute node for execution, and ArcaDB ensures that the operator gets picked up by the appropriate workers. We have implemented a prototype version of ArcaDB using Java, Python, and Docker containers. We have also completed a preliminary performance study of this prototype, using images and scientific data. this study shows that ArcaDB can speed up query performance by a factor of 3.5x in comparison with a shared-nothing, symmetric arrangement.

关键词： Cloud computing Java Social networking (online) Surveillance Prototypes Computer architecture Containers

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Leveraging Synergies Between AI and networking to Build Next Generation Edge Networks

Leveraging Synergies Between AI and Networking to Build Next...

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international conference on collaborative computing: networking, applications and worksharing (CollaborateCom)

作者： Sen Lin Ming Shi Anish Arora Raef Bassily Elisa Bertino Constantine Caramanis Kaushik Chowdhury Eylem Ekici Atilla Eryilmaz Stratis Ioannidis Nan Jiang Gauri Joshi Jim Kurose Yingbin Liang Zhiqiang Lin Jia Liu Mingyan Liu Tommaso Melodia Aryan Mokhtari Rob Nowak Sewoong Oh Srini Parthasarathy Chunyi Peng Hulya Seferoglu Ness Shroff Sanjay Shakkottai Kannan Srinivasan Ameet Talwalkar Aylin Yener Lei Ying The Ohio State University Purdue University University of Texas Austin Northeastern University University of Illinois Urbana Champaign Carnegie Mellon University University of Massachusetts Amherst University of Michigan University of Wisconsin Madison University of Washington University of Illinois Chicago

networking and Artificial Intelligence (AI) are two of the most transformative information technologies over the last few decades. Building upon the synergies of these two powerful technologies, we envision designing next generation of edge networks to be highly efficient, reliable, robust and secure. To this end, in this paper, we delve into interesting and fundamental research challenges and opportunities that span two major broad and symbiotic areas: AI for Networks and Networks for AI. the former deals with the development of new AI tools and techniques that can enable the next generation AI-assisted networks; while the latter focuses on developing networking techniques and tools that will facilitate the vision of distributed intelligence, resulting in a virtuous research cycle where advances in one will help accelerate advances in the other. A wide range of applications will be further discussed to illustrate the importance of the foundational advances developed in these two areas.

关键词： Wireless communication Symbiosis Privacy Uncertainty Program processors Reliability engineering Security

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A DQN-Based Approach for Online Service Placement in Mobile Edge computing 16th

A DQN-Based Approach for Online Service Placement in Mobile ...

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16th EAI international conference on collaborative computing: networking, applications, and worksharing, CollaborateCom 2020

作者： Jie, Xiaogan Liu, Tong Gao, Honghao Cao, Chenhong Wang, Peng Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9783030675394

Due to the development of 5G networks, computation intensive applications on mobile devices have emerged, such as augmented reality and video stream analysis. Mobile edge computing is put forward as a new computing paradigm, to meet the low-latency requirements of applications, by moving services from the cloud to the network edge like base stations. Due to the limited storage space and computing capacity of an edge server, service placement is an important issue, determining which services are deployed at edge to serve corresponding tasks. the problem becomes particularly complicated, with considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the time spent for waiting in queues for processing at edge. Benefiting from reinforcement learning, we propose a deep Q network based approach, by formulating service placement as a Markov decision process. Real-time service placement strategies are output, to minimize the total latency of arrived tasks in a long term. Extensive simulation results demonstrate that our approach works effectively. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reinforcement learning

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AI Revolutionizing 5G and Next-Generation Networks

AI Revolutionizing 5G and Next-Generation Networks

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IEEE international Scientific conference on Informatics

作者： Nora PireciSejdiu Nikola Rendevski Blagoj Ristevski Faculty of Information and Communication Technologies University "St. Kliment Ohridski" – Bitola Bitola Republic of Macedonia

ISBN: (数字)9798350387681

ISBN: (纸本)9798350387698

the introduction of 5G and beyond (B5G) networks has transformed massively telecommunications resulting in enhanced capacity, speed, and connectivity of mobile communications. the requirements and the complexity of these networks need to be addressed by innovative approaches to effective network management and optimization. this paper analyzes the critical role of Artificial Intelligence (AI) and Machine Learning (ML) in upgrading next-generation mobile networks and provides a comprehensive analysis of how AI and ML are integrated into 5G systems, highlighting the ways these technologies improve security, edge computing, and network management. It also details the evolution of AI, from the onset in symbolic logic to modern neural networks, and its application in network optimization, including computational offloading, beamforming design, and resource management. AI-driven Self-Organizing Networks (SON) provide adaptive network management and predictive maintenance, while AI-powered edge computing deals with the real-time processing demands of applications such as autonomous vehicles and smart cities. Moreover, the paper also explores how AI improves network security by enhancing threat detection and response capabilities. It concludes by highlighting key future directions, including the integration of AI with emerging technologies such as blockchain and advancements in edge computing. the AI and 5G fusion offer a new era of connected intelligence, revolutionizing the way we interact with technology and one another. this convergence is about transforming communication and connectivity, laying the foundations for more innovative applications and more resilient network infrastructure.

关键词： 5G mobile communication Neural networks Self-organizing networks Real-time systems Telecommunications Resource management Artificial intelligence Next generation networking Optimization Edge computing

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Deep Transfer Learning for Cross-Device Channel Classification in mmWave Wireless 17

Deep Transfer Learning for Cross-Device Channel Classificati...

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17th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Almutairi, Ahmed Srinivasan, Suresh Keshavarz-Haddad, Alireza Aryafar, Ehsan Portland State Univ Comp Sci Dept Portland OR 97207 USA Shiraz Univ Elect & Comp Engn Dept Shiraz Iran

ISBN: (纸本)9781665406680

Identifying whether the wireless channel between two devices (e.g., a base station and a client device) is Line-of-Sight (LoS) or non-Line-of-Sight (nLoS) has many applications, e.g., it can be used in device localization. Prior works have addressed this problem, but they are primarily limited to sub-6 GHz systems, assume sophisticated radios on the devices, incur additional communication overhead, and/or are specific to a single class of devices (e.g., a specific smartphone). In this paper, we address this channel classification problem for wireless devices with mmWave radios. Specifically, we show that existing beamforming training messages that are exchanged periodically between mmWave wireless devices can also be used in a deep learning model to solve the channel classification problem with no additional overhead. We then extend our work by developing a transfer learning model (t-LNCC) that is trained on simulated data, and can successfully solve the channel classification problem on any commercial-off-the-shelf (COTS) mmWave device with/without any real-world labeled data. the accuracy of t-LNCC is more than 95% across three different COTS wireless devices, when there is a small sample of labeled data for each device. We finally show the application of our classification problem in estimating the distance between two wireless devices, which can be used in localization.

关键词： Artificial Intelligence Beamforming mmWave Wireless Mobile computing Transfer Learning

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Micromovement Analysis in Human-Robot Interaction for Direct Robot Manipulation

Micromovement Analysis in Human-Robot Interaction for Direct...

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17th international conference on Social computing and Social Media, SCSM 2025, held as part of the 27th HCI international conference, HCII 2025

作者： Zidaric, Matija Stipancic, Tomislav Koren, Leon Orsag, Luka Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia

ISBN: (纸本)9783031935350

Human-Robot Interaction (HRI) increasingly integrates direct robot control, requiring precision and stability. In this study, we investigate the impact of natural human tremors - micromovements on real-time HRI and their interaction with robotic system vibrations. Tremors, involuntary rhythmic muscle movements, can affect fine motor tasks, particularly in high-precision applications. We analyze resting and action tremors, excluding pathological tremors. To examine these effects, we employ two synchronized accelerometers (MPU6050) to capture tremor-induced micromovements: one on the human hand or controller and another on the robot’s end effector. Experiments involve the Fanuc LR Mate 200ic and UR5 robots, controlled via the Mediapipe Hands module and HTC Vive VR controller. We analyze accelerometer data using Fast Fourier Transform (FFT). Our findings contribute to mitigating unwanted oscillations in HRI, paving the way for adaptive filters and machine learning approaches to enhance real-time robot control. By minimizing tremor-induced micromovements, we aim to achieve seamless, embodied interaction, optimizing robotic precision while preserving human fine motor skills and dexterity in collaborative settings. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： collaborative robots

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