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connection models for the Internet-of-Things

Frontiers of computer Science 2020年第3期14卷 65-85页

作者： Kangli HE Holger HERMANNS Hengyang WU Yixiang CHEN MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal UniversityShanghai 200062China Saarland University Saarland Informatics CampusSaarbrücken 66123Germany

The Internet-of-Things(IoT)is expected to swamp the *** order to study and understand the emergent behaviour of connected things,effective support for their modelling is *** the heart of IoT are flexible and adaptive connection patterns between things,which can naturally be modelled by channel-based coordination primitives,and characteristics of connection failure probabilities,execution and waiting times,as well as resource *** latter is especially important in light of severely limited power and computation budgets inside the *** this paper,we tackle the IoT modelling challenge,based on a conservative extension of channel-based Reo *** introduce a model called priced probabilistic timed constraint automaton,which combines models of probabilistic and timed aspects,and integrates pricing *** expressive logic called priced probabilistic timed scheduled data stream logic is presented,so as to enable the specification and verification of properties,which characterize data-flow streams and prices.A small but illustrative IoT case demonstrates the principal benefits of the proposed approach.

关键词： IoT Reo cost time probability automaton

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TSCND:Temporal Subsequence-Based convolutional Network with Difference for Time Series Forecasting

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computers, Materials & continua 2024年第3期78卷 3665-3681页

作者： Haoran Huang Weiting Chen Zheming Fan MOE Research Center of Software/Hardware Co-Design Engineering East China Normal UniversityShanghai200062China

Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

关键词： Difference data prediction time series temporal convolutional network dilated convolution

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Recognition of facial expression based on CNN-CBP features 29

Recognition of facial expression based on CNN-CBP features

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第29届中国控制与决策会议

作者： Yize Liu Yixiang Chen School of Computer Science and Software Engineering MoE Engineering Center for Software/Hardware Co-Design Technology and ApplicationEast China Normal University

ISBN: (纸本)9781509046584

Automatical recognition of facial expression is an interesting and challenging problem,which has so many applications such as expression synthesis,human-robot interaction,metal state identification,intelligent tutoring systems,operator fatigue,music for mood,and clinical *** vital step of a successful approach is deriving features from raw facial *** existed methods of features extraction are the hand-crafted features based on geometric features or appearance features,and the auto-learned *** utilize the benefit of low computation of hand-crafted features and the high-representation of auto-learned features,we firstly proposed the combined features CNN-CBP with putting together Centralized Binary Patterns(CBP) features and convolutional Neural Network(CNN) *** then,we classified the features using Support Vector Machine(SVM).With the help of the CNN-CBP features,we achieved average recognition accuracy of 97.6%on the Extended cohn-Kanade datasets and 88.7%on the Japanese Femal Facial Expression datasets based on 10-cross validation.

关键词： Facial Expression Recognition convolutional Neural Network Centralized Binary Patterns combination of Features Support Vector Machine

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research and design of Sensor Data Management System Based on Distributed Storage

Research and Design of Sensor Data Management System Based o...

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2020 International conference on computer Science and Management technology, ICCSMT 2020

作者： Yang, Jing Chi, Xiaotong Zhu, Minghua Wang, Wenjie East China Normal University MoE Engineering Research Center for Software/Hardware Co-design Technology and Application Shanghai China

ISBN: (纸本)9781728186689

Sensor data is one of the basic source of big data in the Internet of Things(IoT). It is the data that is sensed, measured, and transmitted by sensors with real-time and dynamic characteristics. With the development of the IoT and big data technologies, new industries such as smart transportation, smart cities, and smart agriculture have gradually formed, resulting in an increasing demand for large-scale data analysis and calculations. However, the storage architecture based on relational databases has been unable to meet the current needs in terms of data storage and data read and write speed. Therefore, this paper mainly studies the sensor data management system based on distributed storage, builds a Hadoop cluster, proposes a storage structure that combines the relational database MySQL with the distributed database HBase, designs a data migration module to satisfy real-time sensor data monitoring and massive data storage requirements. In order to meet the needs of enterprises in actual production, SSM framework technology is used to build a sensor data management platform, covering the functions of system management, sensor data management, data exception management, sensor data real-time monitoring, sensor node status monitoring, data statistical analysis, etc. The platform has good security and reliability, and can effectively improve the management efficiency of the enterprise. Finally, the experiment provides that the system has obvious advantages in data upload and query ability compared with the traditional storage architecture. It provides a good data environment for data analysis and data mining in the future. © 2020 IEEE.

关键词： Sensor nodes

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MACEdge: Real-Time Video Analytics Based on Multi-Access collaborative Edge computing

MACEdge: Real-Time Video Analytics Based on Multi-Access Col...

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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.

关键词：

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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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