2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)最新文献

Energy-Efficient Partial Offloading in Mobile Edge Computing Under a Deadline Constraint 最后期限约束下移动边缘计算的节能部分卸载

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637065

Jinxin Zhao, Longxin Deng, Yong Liu, J. Sun

{"title":"Energy-Efficient Partial Offloading in Mobile Edge Computing Under a Deadline Constraint","authors":"Jinxin Zhao, Longxin Deng, Yong Liu, J. Sun","doi":"10.1109/ICITES53477.2021.9637065","DOIUrl":"https://doi.org/10.1109/ICITES53477.2021.9637065","url":null,"abstract":"Task offloading and scheduling is an important issue in mobile edge computing (MEC). A good offloading decision can fully utilize the computing capabilities of edge servers to deliver high-quality computing services. This paper takes into account the concerns about task processing latency and mobile device's energy consumption, and formulates the task offloading problem as a deadline-constrained energy-minimization integer program. We propose a partial offloading and scheduling method based on the whale optimization algorithm to solve the formulated optimization problem. This method employs a probability model-based mapping operator to convert an individual whale into a valid offloading solution represented by a task sequence. This mapping scheme is advantageous over sorting-based rules in producing high-quality task sequence. We develop an efficient heuristic strategy to decide each task should be processed locally on the mobile device or offloaded to the edge server for execution. With all tasks in the sequence having been scheduled, the mobile device's energy consumption for task execution and data transmission can be calculated. Accordingly, we can identify the best individual whale in the population leading to the solution with lowest energy consumption. We iteratively apply the population updating rule to explore better task sequence and offloading solution. We perform extensive experiments to verify that the proposed algorithm achieves more energy-efficient offloading solutions as compared to baseline algorithms while satisfying the deadline constraint.","PeriodicalId":370828,"journal":{"name":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115699065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Backfill Quality Monitoring Method of Complex Geological Substation Based on Deep Learning and Edge Computing 基于深度学习和边缘计算的复杂地质变电站回填质量监测方法

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637110

Jiyuan Zhang, Yuan Huang, Xinyuan Fan, Jingyao Wang

{"title":"Backfill Quality Monitoring Method of Complex Geological Substation Based on Deep Learning and Edge Computing","authors":"Jiyuan Zhang, Yuan Huang, Xinyuan Fan, Jingyao Wang","doi":"10.1109/ICITES53477.2021.9637110","DOIUrl":"https://doi.org/10.1109/ICITES53477.2021.9637110","url":null,"abstract":"Earthwork backfilling is an important link in substation construction. In order to improve the backfill quality during construction, this paper introduces the edge calculation framework into the earthwork backfilling project of substation, and realizes automatic monitoring and analysis of backfill quality at the construction site. In view of the shortcomings of time-consuming and labor-intensive manual monitoring, this paper adopts deep learning method to identify the driving track of engineering vehicles and automatically monitor the compaction times of engineering vehicles during backfilling. Combining the convolutional neural networks (CNN) with the region proposal network (RPN), the region where the engineering vehicle is located in the video is extracted, and then the gradient amplitude image for identifying the engineering vehicle is calculated and generated by using the HOG feature. By analyzing the video frame sequence one by one, the driving track of the engineering vehicle can be obtained, and the automatic monitoring of backfill quality can be realized.","PeriodicalId":370828,"journal":{"name":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129427322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electricity Decentralized Transaction Framework of Community Energy Internet Cluster Based on Blockchain 基于区块链的社区能源互联网集群电力去中心化交易框架

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637077

Haiyan Wang, Wei Wang, Liyang Liu, Chuan Long, Jun Wei, Ting Zhu

{"title":"Electricity Decentralized Transaction Framework of Community Energy Internet Cluster Based on Blockchain","authors":"Haiyan Wang, Wei Wang, Liyang Liu, Chuan Long, Jun Wei, Ting Zhu","doi":"10.1109/ICITES53477.2021.9637077","DOIUrl":"https://doi.org/10.1109/ICITES53477.2021.9637077","url":null,"abstract":"The frequent electricity transactions of multienergy complementary Energy Internet Cluster result in higher operating costs and increased risks of information security by traditional transaction mode. Therefore, based on the blockchain technology, this paper proposes an electricity trading architecture suitable for Community Energy Internet Cluster. Firstly, the article elaborates the basic structure of Energy Internet and blockchain, and analyzes the adaptability of blockchain applied to Energy Internet Cluster electricity transaction. Secondly, the process of establishing electricity trading platform and deploying smart contract based on Ethereum network is described in detail. Thirdly, the power transaction framework of Community Energy Internet Cluster is constructed, and the double auction mechanism is applied to complete matchmaking tradeoff, and the smart contract is designed. Finally, a practical energy trading platform is built through the Ganache client of Ethereum network. Case studies demonstrate the feasibility and effectiveness of the trading operation framework.","PeriodicalId":370828,"journal":{"name":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128900631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robust Stochastic Optimization for Locally Ideal Unit Commitment Considering Renewable Energy Sources 考虑可再生能源的局部理想机组承诺鲁棒随机优化

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637111

Ao Li, Yang Liu, Jiayu Wu

{"title":"A Robust Stochastic Optimization for Locally Ideal Unit Commitment Considering Renewable Energy Sources","authors":"Ao Li, Yang Liu, Jiayu Wu","doi":"10.1109/ICITES53477.2021.9637111","DOIUrl":"https://doi.org/10.1109/ICITES53477.2021.9637111","url":null,"abstract":"The unit commitment (UC) is a fundamental task in day-ahead electricity market. However, the proliferation of renewable energy sources (RES), especially wind power and solar power, significantly influences the economics of UC, due to their inherent uncertainties. Therefore, this paper presents a general robust stochastic optimization (RSO) framework for performing UC with involving the uncertainties of RES. Firstly, a typical UC model is presented, which determines the on/off state, base-point generation, and reserve level for units. And then, the intractable quadratic objective is linearized using a locally ideal piecewise formulation, so that a tractable mixed integer linear programming (MILP) based UC model is obtained. Furthermore, an event-wise RSO framework is employed to deal with the uncertainties, in which the uncertain RESs are captured by an event-wise ambiguity set. Moreover, a practical way is designed to apply the RSO framework on the presented UC model. Finally, using realworld RES data from Belgian Transmission System Operators, experimental results on IEEE RTS 24-bus system demonstrate the effectiveness of the presented method.","PeriodicalId":370828,"journal":{"name":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133003855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Recognition-based Deep Neural Network for Packed Malware Detection 基于图像识别的深度神经网络包装恶意软件检测

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637103

Xuchenming Sun, Yunchun Zhang, Chengjie Li, Xin Zhang, Yuting Zhong

引用次数: 1

[Title page] (标题页)

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/icites53477.2021.9637083

引用次数: 0

Multi-microgrids Transaction Model Based on Cyber Physical System 基于网络物理系统的多微网交易模型

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637068

Zhichao Ren, Liyang Liu, Chaofeng Cheng, Wei Wang, Jun Wei, Jiayu Wu

引用次数: 0

Design of TSN-based Ethernet Driver Working Model for Time-aware Scheduling 基于tsn的时间感知调度的以太网驱动工作模型设计

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637084

Bowen Wang, Feng Luo, Yutao Jin, Zihao Fang, Qiujian Li

{"title":"Design of TSN-based Ethernet Driver Working Model for Time-aware Scheduling","authors":"Bowen Wang, Feng Luo, Yutao Jin, Zihao Fang, Qiujian Li","doi":"10.1109/ICITES53477.2021.9637084","DOIUrl":"https://doi.org/10.1109/ICITES53477.2021.9637084","url":null,"abstract":"With the rapid increase in the proportion of electronic products used in automobiles, the scale and complexity of in-car systems are increasing day by day. The requirements for automobile safety, energy conservation, emission reduction, and comfort are also growing. Under such a development trend, the bandwidth demand of the onboard network is also increasing. It demands that the onboard network can carry various high-speed data transmissions. To overcome this problem, automotive Ethernet becomes one of the best solutions in such a trend by establishing communication links between related ECUs, allowing ECUs to use more advanced functions and share data. Meanwhile, Time Sensitive Network (TSN) is dedicated to developing more powerful functions to realize an ultra-low delay control network. Time synchronization and time-aware scheduling are the core of TSN protocol. In this paper, we propose a new architecture of the TSN-based Ethernet driver working model for time-aware scheduling to improve the performance of the deterministic delays of the time-sensitive stream. TSN only guarantees the communication delay but does not make requirements for the controllers. To support the real-time scheduling mechanism of TSN, the Ethernet controller's working model must change. Due to the more considerable amount of data to be processed, the driver's occupation management of the controller's running resources becomes more critical. At the same time, due to the need for real-time scheduling, the message sending behavior is constrained, and it cannot run in the event-based triggering mode like traditional Ethernet drivers. We also implement our architecture on an embedded system and evaluate the performance of the time-aware scheduling based on our architecture.","PeriodicalId":370828,"journal":{"name":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114821131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Light-Weight Compressed Video Processing Method on Embedded Platforms for IIoT 基于IIoT嵌入式平台的轻量级压缩视频处理方法

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/ICITES53477.2021.9637075

Lvcheng Chen, Pingyang Liu, Li Zhang

{"title":"A Light-Weight Compressed Video Processing Method on Embedded Platforms for IIoT","authors":"Lvcheng Chen, Pingyang Liu, Li Zhang","doi":"10.1109/ICITES53477.2021.9637075","DOIUrl":"https://doi.org/10.1109/ICITES53477.2021.9637075","url":null,"abstract":"Recently, video has become an important medium for knowledge sharing for both industrial and consumer scenarios. For industrial applications, especially Industrial IoT (IIoT), it is highly desired to transfer the video content with limited bandwidth and process the video using constrained resources, which makes compressed video processing a very challenging problem. Recently, there have been extensive works focusing on compressed video quality enhancement (VQE) tasks, many of which deploy dedicated and complex CNNs to reach amazing performances. Such advancements have enabled various applications in video-based tasks. On the other hand, since deep neural networks often require high computational resources, such complex CNNs can hardly be deployed on the embedded devices. Thus, model pruning technique and inference optimization have been appealing options for efficient deployment of VQE under resource-constrained environments. In this paper, we incorporate a novel deformable convolution method into our network architecture and propose a light-weight method for compressed video quality enhancement on an embedded platform for IIoT. The proposed system has outperformed several SOTA light-weight quality enhancement models and can achieve 15.230 FPS and 0.773 FPS/W on MFQEv2 dataset [1].","PeriodicalId":370828,"journal":{"name":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127818546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Copyright notice] (版权)

2021 International Conference on Intelligent Technology and Embedded Systems (ICITES) Pub Date : 2021-10-31 DOI: 10.1109/icites53477.2021.9637102

引用次数: 0