2022 18th International Conference on Mobility, Sensing and Networking (MSN)最新文献_第7页

EdgeMan: Ensuring Real-Time Service for Containerized Edge Systems EdgeMan:确保集装箱边缘系统的实时服务

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00049

Wenzhao Zhang, Wei Dong, Geng Ren, Yi Gao

引用次数: 0

Scene Classification Through Knowledge Distillation Enabled Parameter-Free Attention Model for Remote Sensing Images 基于知识蒸馏的遥感图像无参数关注模型场景分类

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00077

Yubing Han, Zongyin Liu, Jiguo Yu, Anming Dong, Huihui Zhang

{"title":"Scene Classification Through Knowledge Distillation Enabled Parameter-Free Attention Model for Remote Sensing Images","authors":"Yubing Han, Zongyin Liu, Jiguo Yu, Anming Dong, Huihui Zhang","doi":"10.1109/MSN57253.2022.00077","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00077","url":null,"abstract":"Remote sensing image scene classification is to label remote sensing images as a specific scene category by understanding the semantic information of the images. It is an essential link in remote sensing image analysis and interpretation and has important research value. Convolutional neural networks (CNNs) have been dominant in remote sensing image scene classification due to their powerful feature extraction capabilities. The general trend has been to make deeper and wider CNN architectures to achieve higher classification accuracy. However, these advances to improve accuracy enlarge the network, creating too many parameters and high computational costs. Large models are difficult to deploy on resource-constrained edge devices for practical applications. Furthermore, CNNs can effectively capture local information but are weak in extracting global features. To overcome these drawbacks, we propose a novel knowledge distillation (KD) based method by employing Swin Transformer as a teacher network for guiding MobileNetV2 with Parameter-Free Attention (MobileNetV2-PFA). First, we modify MobileNetV2 by introducing PFA into the inverted bottleneck block; this improvement helps the model learn more latent and robust features without extra parameters. Second, Swin Transformer is an excellent architecture for capturing long-range dependencies via shifted window-based attention. So, we utilize the long-range dependency information from the Swin Transformer to assist MobileNetV2-PFA training through KD. Experimental results on the challenging NWPU-RESISC45 dataset show that the proposed method outperforms the original MobileNetV2 in classification accuracy with low computational consumption.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115801925","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

HCA Operator: A Hybrid Cloud Auto-scaling Tooling for Microservice Workloads HCA操作员:用于微服务工作负载的混合云自动扩展工具

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00143

Yuyang Wang, Fan Zhang, S. Khan

{"title":"HCA Operator: A Hybrid Cloud Auto-scaling Tooling for Microservice Workloads","authors":"Yuyang Wang, Fan Zhang, S. Khan","doi":"10.1109/MSN57253.2022.00143","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00143","url":null,"abstract":"Elastic cloud platform, e.g. Kubernetes, enables dy-namically scale in or out computing resources in accordance with the workloads fluctuation. As the cloud evolves to hybrid, where public and private clouds co-exist as the underline substrate, autoscaling applications within a hybrid cloud is no longer straightforward. The difficulty lies in all aspects, e.g. global load balancing, hybrid-cloud monitoring and alerting, storage sharing and replication, security and privacy, etc. However, it will significantly pay off if hybrid-cloud autoscaling is supported and boundless computing resources can be utilized per request. In this paper, we design Hybrid Cloud Autoscaler Operator (HCA Operator), a customized Kubernetes Controller that leverages the Kubernetes Custom Resource to auto-scale microservice applications across hybrid clouds. HCA Operator load balances across hybrid clouds, monitors metrics, and autoscales to des-tination clusters that exist in other clouds. We discuss the implementation details and perform experiments in a hybrid cloud environment. The experimental results demonstrate that if the workload changes quickly, our Operator can properly auto-scale the microservice applications across hybrid cloud in order to meet the Service Level Agreement (SLA) requirements.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"41 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131687751","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

Interval Matching Algorithm for Task Scheduling with Time Varying Resource Constraints 时变资源约束下任务调度的区间匹配算法

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00152

Weiguan Li, Jialun Li, Yujie Long, Weigang Wu

引用次数: 0

Real-time Simulation and Testing of a Neural Network-based Autonomous Vehicle Trajectory Prediction Model 基于神经网络的自动驾驶车辆轨迹预测模型的实时仿真与测试

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00106

Cheng Wei, F. Hui, Xiangmo Zhao, Shan Fang

{"title":"Real-time Simulation and Testing of a Neural Network-based Autonomous Vehicle Trajectory Prediction Model","authors":"Cheng Wei, F. Hui, Xiangmo Zhao, Shan Fang","doi":"10.1109/MSN57253.2022.00106","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00106","url":null,"abstract":"Autonomous vehicle trajectory prediction is an important component of autonomous driving assistance algorithms (ADAAs), which can help autonomous driving systems (ADSs) better understand the traffic environment, assess critical tasks in advance thus improve traffic safety and traffic efficiency. However, some existing neural network-based trajectory prediction models focus on theoretical numerical analysis and are not tested in real time, leading to doubts about the practical usability of these trajectory prediction models. To address the above limitations, this study first proposes a collaborative simulation environment integrating traffic scenario construction, driving environment perception, and neural network modeling, afterwards used the co-simulation environment for trajectory data and driving environment data collection. In addition, based on the characteristics of the collected data, a trajectory prediction model based on Bi-Encoder-Decoder and deep neural network (DNN) is proposed and pre-trained. Finally, the pre-trained completed model is embedded in the co-simulation environment and tested in real-time with different batches of data. The simulation results show that the proposed trajectory prediction model can predict trajectories well under specific training data batches, and the best performing trajectory prediction model has a prospective time of 4.9 s and a prediction accuracy of 91.55%.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114178134","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

Multiuser Collaborative Localization based on Inter-user Distance Estimation using Wi-Fi RSS Fingerprints 基于Wi-Fi RSS指纹的用户间距离估计的多用户协同定位

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00111

Tinghao Qi, Chanxin Zhou, Guanglie Ouyang, Bang Wang

{"title":"Multiuser Collaborative Localization based on Inter-user Distance Estimation using Wi-Fi RSS Fingerprints","authors":"Tinghao Qi, Chanxin Zhou, Guanglie Ouyang, Bang Wang","doi":"10.1109/MSN57253.2022.00111","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00111","url":null,"abstract":"Indoor localization based on Wi-Fi received signal strength (RSS) fingerprints has been widely studied in recent years, mainly focusing on how to improve localization accuracy in an independent way. Some studies propose to use additional hardware devices to measure the distance in between users to improve localization accuracy, but these methods suffer from high cost and low practicality. In order to solve this problem, an inter-user distance estimation algorithm iDE is proposed in this paper. We first construct user features based on Wi-Fi fingerprints, then train the random forest and nearest neighbor regressors to obtain inter-user distance estimates, and design a multi-layer perceptron to fuse them. We propose a multiuser collaborative localization MCLoc based on inter-user distance estimation. It takes the distance estimation from iDE as a soft constraint to optimize the user's location using gradient descent search. Experiments in real scenarios show that in terms of inter-user distance estimation, the iDE algorithm can reduce the error by 24.2% compared with the single-model algorithm; in terms of positioning performance, the MCLoc algorithm can reduce the localization error by 11.4% compared with the non-collaborative method.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124170732","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

Analysing and Evaluating Complementarity of Multi-Modal Data Fusion in AD Diagnosis AD诊断中多模态数据融合的互补性分析与评价

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00135

Zhaodong Chen, Fengtao Nan, Yun Yang, Jiayu Wang, Po Yang

{"title":"Analysing and Evaluating Complementarity of Multi-Modal Data Fusion in AD Diagnosis","authors":"Zhaodong Chen, Fengtao Nan, Yun Yang, Jiayu Wang, Po Yang","doi":"10.1109/MSN57253.2022.00135","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00135","url":null,"abstract":"The clinical progression of Alzheimer's disease( AD ) can't be accurately evaluated by single modality data alone. Multi-modal data have a good effect on the diagnosis of AD. Clarifying the complementarity between modalities is crucial for the assessment of each stage of AD. Few studies have specifically explored the complementarity between different modalities due to the lack of completely aligned and paired multi-modal data and the limitation of sample size. However, collecting the full set of aligned and paired data is expensive or even impractical. In addition, the limited number of samples poses a great challenge to the robustness of the model. In this paper, different machine learning( ML ) methods were used to explore data complementarity between T1-weighted magnetic resonance imaging ( MRI ), cerebrospinal fluid ( CSF ), and fluorodeoxyglucose-positron emission tomography ( FDG-PET ) modalities. The different modal data of Alzheimer's Neuroimaging Initiative ( ADNI ) and the self-extracted neuroimaging data were experimentally explored. Experiments show that there is obvious complementarity between MRI and CSF. By fusing MRI and CSF data, three binary classification tasks using multi-modal fusion data have achieved varying degrees of improvement. At the same time, we also explored the important features of multi-modal fusion data through SHapley Additive exPlanations ( SHAP ), and found that most important features are supported by relevant literature.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117298842","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

Multi-timescale History Modeling for Temporal Knowledge Graph Completion 时间知识图补全的多时间尺度历史建模

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00082

Chen Chen Peng, Xiaochuan Shi, Rongwei Yu, Chao Ma, Libing Wu, Dian Zhang

{"title":"Multi-timescale History Modeling for Temporal Knowledge Graph Completion","authors":"Chen Chen Peng, Xiaochuan Shi, Rongwei Yu, Chao Ma, Libing Wu, Dian Zhang","doi":"10.1109/MSN57253.2022.00082","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00082","url":null,"abstract":"Temporal knowledge graph (TKG) has received great attention in recent years. However, the TKG is not always complete due to the missing of important facts, which has seriously hindered its wide application. Inferring missing facts in TKG is a critical and challenging task due to its highly dynamic nature. Most of the existing methods mainly focus on modeling the structural features and temporal dependencies of TKG to solve the temporal knowledge graph completion problem (TKGC). However, those methods only operate at a single timescale without considering the latent time variability of TKG and thus limit the performance of TKGC solutions. Therefore, we propose a novel method named MtGCN (Multi-timescale history modeling framework based on Graph Convolutional Networks) for completing TKG by self-adaptively modeling the multi-timescale history of the incomplete TKG. Firstly, MtGCN uses a structural encoder with a graph convolutional network to mine the latent semantic information and structural features of the TKG. Secondly, MtGCN uses GRU-based temporal encoder to learn the historical information at various timescales of the TKG. Finally, it generates effective entity and relation representations to infer the missing facts for the originally incomplete TKG. By conducting comprehensive experiments on 5 public datasets, the experimental results show that our proposed method MtGCN significantly outperforms the baselines by achieving the highest MRR and HITS@1,3,10.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129007834","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

Analytic Correlation Penalty with Variable Window in Multi-task Learning Disease Progression Model 多任务学习疾病进展模型的变量窗相关惩罚分析

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00112

Xiangchao Chang, Menghui Zhou, Fengtao Nan, Yun Yang, Po-Sung Yang

{"title":"Analytic Correlation Penalty with Variable Window in Multi-task Learning Disease Progression Model","authors":"Xiangchao Chang, Menghui Zhou, Fengtao Nan, Yun Yang, Po-Sung Yang","doi":"10.1109/MSN57253.2022.00112","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00112","url":null,"abstract":"Alzheimer's Disease (AD) is the most common reason of dementia that causes serious problems in patients' congnitive functions. Multi-task learning (MTL) has performed well in studies of longitudinal processes in Alzheimer's disease for revealing the progression of AD. Combined with prior knowl-edges in disease progression or medical science, regularization MTL framework could introduce empirical constraints more flexibly. Meanwhile, it brings higher cost during optimization. While it shown that most of formulations could not define the disease progression precisely. Existing regression methods with temporal smoothness method eliminated abnormal fluctuation of cognitive scores, and neglected the sophisticated progression in disease. In this article, we proposed an analytic method to define the progression of AD, and a flexible bandwidth method to encourage the points of disease time sequence temporal smoothness in an appropriate way. To solve three non-smooth penalties in our method, we proposed an optimization method combined accelerated gradient descent (AGD) and alternating direction method of multipliers (ADMM).","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124975816","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 Novel Data Aggregation Scheme for Wireless Sensor Networks Based on Robust Chinese Remainder Theorem 一种基于鲁棒中国剩余定理的无线传感器网络数据聚合方案

2022 18th International Conference on Mobility, Sensing and Networking (MSN) Pub Date : 2022-12-01 DOI: 10.1109/MSN57253.2022.00032

Jinxin Zhang, Fuyou Miao

{"title":"A Novel Data Aggregation Scheme for Wireless Sensor Networks Based on Robust Chinese Remainder Theorem","authors":"Jinxin Zhang, Fuyou Miao","doi":"10.1109/MSN57253.2022.00032","DOIUrl":"https://doi.org/10.1109/MSN57253.2022.00032","url":null,"abstract":"In wireless sensor networks (WSNs), to improve sensing accuracy and coverage, a large number of sensor nodes are usually deployed in the monitoring area. The high density makes the data sensed by adjacent sensor nodes the same or similar, causing a lot of data redundancy and energy waste. In addition, reliability and non-plaintext transmission of the sensed data are also major concerns in WSNs. In this paper, we propose a novel data aggregation scheme to satisfy the requirements of energy efficiency, reliability, and non-plaintext transmission simultaneously, which obtains the approximate measurement result when small measurement errors are allowed. The scheme employs robust Chinese Remainder Theorem (RCRT) to compress the data when it is sensed and no other assumptions are required. We further derive some analytical results and give the simulation results of our scheme. Finally, we compare the performance of the typical data aggregation schemes with our RCRT-based data aggregation scheme in experimental simulation. The results demonstrate that the proposed RCRT-based data aggregation scheme has a better performance in energy saving.","PeriodicalId":114459,"journal":{"name":"2022 18th International Conference on Mobility, Sensing and Networking (MSN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121256958","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