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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhong, Dian Zhu, Minghua Huang, Binbin East China Normal University Moe Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350337020

Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load on network bandwidth and introduces poor user experience due to latency. Edge computing offers a solution by offloading a portion of the video analysis tasks to the edge, significantly reducing the pressure on bandwidth and latency. However, the fixed video configurations (resolution, frame rate, etc.) uploaded to the edge may not be optimal for specific situations. Moreover, using a low video configuration makes it difficult for edge video analysis systems to recognize environmental changes, while using a high video configuration poses challenges to latency and bandwidth. Therefore, in this paper, we propose the MACEdge-an edge video analysis framework by leveraging the multi-access capability of the edge gateway to enhance edge perception by accessing heterogeneous sensors. We offer an algorithm based on Q Learning for dynamic adaptive adjustment of sensor threshold and video configurations. In experiments using real-world data, our collaborative solution improved accuracy and reduced high bandwidth costs compared to other video configuration adjustment benchmarks. © 2023 IEEE.

关键词： Edge computation Multi-access collaboration Video analysis

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LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System 29

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

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29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Jiali Ye, Yutong Wang, Ting Chen, Mingsong East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV's effective payload capacity. © 2023 IEEE.

关键词： Energy Efficiency Multi-Access Edge computing Reinforcement Learning Workflow Scheduling

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Joint computing Offloading and Resource Allocation in MEC-Enabled IoT: A Diffusion-Based Reinforcement Learning Approach

Joint Computing Offloading and Resource Allocation in MEC-En...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Cao, Huimin Xiao, Bo MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781665410205

The integration of the Internet of Things (IoT) with mobile edge computing (MEC) has come out to be a promising solution to address the requirements of high computing capabilities and low latency services, enabling user equipments(UE) to migrate the computation of tasks onto edge servers. This paper focuses on optimizing the performance of MEC-enabled IoT system by formulating a joint computing offloading and resource allocation problem. The objective is to minimize the total delay of the system consisting of multiple servers and multiple users. The denoising network of a diffusion model with capabilities of generation can be trained to obtain optimal solution given the changed environment conditions. Therefore, we propose the diffusion-based deep deterministic policy gradient (DiffDDPG) algorithm which utilizes a diffusion model as the policy to learn optimal decisions jointly. Simulation results exhibits the superior performance of the DiffDDPG algorithm. © 2024 IEEE.

关键词： computation offloading

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Using Multi-feature Embedding towards Accurate Knowledge Tracing 34

Using Multi-feature Embedding towards Accurate Knowledge Tra...

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34th International conference on software engineering and Knowledge engineering, SEKE 2022

作者： Yu, Yang Huang, Caidie Chen, Liangyu Chen, Mingsong MoE Engineering Research Center of Software Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)1891706543

Knowledge tracing is a crucial task in intelligent tutoring systems. Aiming at the shortcomings of traditional knowledge tracing technology such as low prediction accuracy, overfitting and low utilization of multi-features, this paper proposes a knowledge tracing model SRGCA-M using multi-feature embedding with stacked residual GRU network. compared with the traditional methods that only use the historical record of answering exercises, our approach utilizes a variety of features in the learning process of students to deep characterize students' learning. We increase the layers number of GRU network to expand the capacity of sequence learning and use residual connections to solve the problems of network degradation and vanishing gradient. We use the auto-encoder to solve the problem that the cross-feature encoding will rapidly increase the dimension of the input data. comprehensive experimental results demonstrate that compared with various advanced techniques, our approach can not only achieve better performance of tracking knowledge changes of students but also fully utilize multi-feature information of students in the learning process. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Students

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QueryEdge: Real-Time Muti-Video Query in Edge-Cloud collaborative System

QueryEdge: Real-Time Muti-Video Query in Edge-Cloud Collabor...

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhong, Jihua Niu, Yannian Zhu, Minghua East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350337020

The real-time query of surveillance video plays a significant role in many fields such as public safety, smart city, and abnormality monitoring. However, with the exponential growth of surveillance video data, traditional cloud-based intelligent video processing faces significant challenges in terms of latency and bandwidth, while the pure edge computing approach is deficient in query accuracy due to its lack of computational power. Existing edge cloud collaboration approaches, such as SurveilEdge, focus on real-time target queries within a single video stream and do not show promising results during target queries across multiple video streams. For this reason, this paper proposes Query Edge, an edge-cloud collaborative real-time query system for multiple video streams. Specifically, we design a real-time query system based on an edge-cloud collaboration framework to achieve highly accurate and low-latency target query services in multiple video streams. In addition, we introduce a prioritization mechanism and a load-balancing strategy in the query task scheduling process to further improve query efficiency. The evaluation proves that QueryEdge has a significant improvement in query latency and bandwidth consumption compared with pure cloud computing, pure edge computing, and SurveilEdge. © 2023 IEEE.

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Throughput-Efficient communication Device Driver for IoT Gateways

Throughput-Efficient Communication Device Driver for IoT Gat...

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2022 IEEE International conference on Systems, Man, and Cybernetics, SMC 2022

作者： Niu, Yannian Zhu, Minghua East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

As bridges for data exchange between end devices and cloud servers, gateways play an important role in IoT and cloud computing fields. However, with the dramatic increase of data generated by the end devices in recent years, the limited data forwarding capability of the gateway has become the bottleneck of the performance of the IoT cloud-based architectures. To efficiently improve the data throughput of links between end devices and cloud servers, this paper proposes a user-space driver for gateways that places the packet-forwarding behavior in the user space of the gateway OS and achieves high throughput by bypassing the kernel and simplifying the packet delivery path. In addition, to obtain more significant data throughput in small packet-intensive scenarios, we integrate packet assembling methods for the driver, which assembles received small packets into a large one before forwarding it. Our evaluation shows that the user-space driver has tens of times throughput compared to the kernel driver. Furthermore, our driver cooperated with the packet assembling method shows a significant advantage compared to the latest related work in small packet-intensive scenarios. © 2022 IEEE.

关键词： Cloud computing

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration 29

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

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29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Xiaoyang Chen, Wenjie Zhou, Zihao Duan, Zhuowen East China Normal University MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9798350330717

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm's execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks. © 2023 IEEE.

关键词： Deep Learning Defect Detection FPGA Parallel computing

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Hybrid Forwarding Strategy Pressure-based Routing for Underwater Wireless Sensor Network 3

Hybrid Forwarding Strategy Pressure-based Routing for Underw...

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3rd International conference on Green communication, Network, and Internet of Things, CNIoT 2023

作者： Yan, Xianyi Xiao, Bo MOE Research Center for Software/Hardware Co-Design Engineering and Application East China Normal University China

ISBN: (纸本)9781510668867

Due to the harsh underwater communication environment, the design of unique routing protocols is essential for underwater sensor networks. This paper proposes a Hybrid Forwarding Strategy Pressure-Based Routing (HFS-PBR) protocol for the Underwater Wireless Sensor Network (UWSN). The protocol categorises forwarding nodes for every sending node into three different types, each of which employs a distinct routing forwarding strategy. Nodes in HFS-PBR can gather neighbour information by exchanging information regularly, which enables the protocol to mitigate the problem of void node and make more optimal routing decisions. Through extensive simulation experiments, our proposed routing protocol demonstrates lower end-to-end delay and improves packet delivery probability compared to various routing protocols. These advantages are particularly prominent in sparse network topologies. © 2023 SPIE.

关键词： Routing protocols

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Winograd-CNN Accelerator compatible with Sparse and Non-sparse Models 23

Winograd-CNN Accelerator Compatible with Sparse and Non-spar...

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23rd IEEE International conference on High Performance computing and communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Huazhen Chen, Weiting Wang, Jiangtao East China Normal University Hangzhou Hikvision Digital Technology Company Ltd Hikvision Research Institute Moe Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University Moe Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China

ISBN: (纸本)9781665494571

The inference speed of convolutional neural networks is a key factor for their application in the industry. The two research directions of non-sparse model acceleration and sparse model acceleration are now studied independently. In this paper, we propose a hybrid granular pruning method and a fixed-length binary pruning index scheme, based on which a compatible acceleration architecture is further designed. The hybrid granular pruning method is realized by executing fine-grained pruning after coarse-grained pruning. The binary pruning index scheme ensures a simple fixed-length binary mask to index valid weight positions no matter the model is pruned or not, thus makes it possible for an accelerator compatible with both non-sparse models without pruning and sparse models with hybrid granular pruning. An acceleration architecture is further designed, using a new data layout, a buffer-pipeline mechanism for the data buffering, and converting the Winograd algorithm into matrix operations with parallelism carried out from three aspects. The experimental results suggest that, our proposed acceleration solution shows not only better acceleration performance but also excellent compatibility, and it can be directly applied for the acceleration of models at different pruning levels without recompilation. © 2021 IEEE.

关键词： Industries Smart cities Layout Optimization methods Parallel processing Data models Sparse matrices

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A verification framework for spatio-temporal consistency language with CCSL as a specification language

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Frontiers of computer Science 2020年第1期14卷 105-129页

作者： Yuanrui ZHANG Frédéric MALLET Yixiang CHEN MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal UniversityShanghai 200062China University Nice Sophia Antipolis I3SUMR 7271 CNRSINRIA06900 Sophia AntipolisFrance

The Spatio-Temporal consistency Language(STeC)is a high-level modeling language that deals natively with spatio-temporal behaviour,i.e.,behaviour relating to certain locations and *** restriction by both locations and time is of first importance for some types of real-time *** is a formal specification language based on logical *** is used to describe some crucial safety properties for real-time systems,due to its powerful expressiveness of logical and chronometric time *** consider a novel verification framework combining STeC and CCSL,with the advantages of addressing spatio-temporal consistency of system behaviour and easily expressing some crucial time *** propose a theory combining these two languages and a method verifying CCSL properties in STeC *** adopt UPPAAL as the model checking tool and give a simple example to illustrate how to carry out verification in our framework.

关键词： spatio-temporal consistency real-time systems spatio-temporal systems high-level modelling language clock constraint specification model checking verification framework

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