2019 IEEE International Conference on Smart Internet of Things (SmartIoT)最新文献

Application of Edge Intelligent Computing in Satellite Internet of Things 边缘智能计算在卫星物联网中的应用

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00022

Junyong Wei, Suzhi Cao

引用次数: 28

Publisher's Information 出版商的信息

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/smartiot.2019.00095

引用次数: 0

An Effective Network Intrusion Detection Framework Based on Learning to Hash 基于哈希学习的有效网络入侵检测框架

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00089

Wenrui Zhou, Yuan Cao, Heng Qi, Junxiao Wang

引用次数: 0

Enhanced and Lock-Free Tendermint Blockchain Protocol 增强和无锁的Tendermint区块链协议

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00041

Basem Assiri, W. Z. Khan

{"title":"Enhanced and Lock-Free Tendermint Blockchain Protocol","authors":"Basem Assiri, W. Z. Khan","doi":"10.1109/SmartIoT.2019.00041","DOIUrl":"https://doi.org/10.1109/SmartIoT.2019.00041","url":null,"abstract":"Blockchain (BC), the backbone technology of cryptocurrency systems and smart contracts, is considered to be an alluring concept in recent years due to its ability to ensure enhanced security and privacy for multifarious applications in many domains. The blockchain is exclusively used for facilitating secure online transactions by maintaining a distributed and decentralized ledger of records across multiple computers. In this paper, we have analyzed and modified the PBFT (Practical Byzantine Fault Tolerant) consensus-based Tendermint blockchain algorithm. The major contributions of this paper are as follows; first we have analyzed and enhanced the correctness of Tendermint blockchain algorithm by proposing a lock free algorithm, employing wait-freedom property by using a timeout on the voting phase. Our second contribution relates to the fairness of the Tendermint algorithm. We have considered the block sensitivity and node's trustworthiness for determining the size of voter's (validator's) subset and employed the random walk algorithm for the fair selection of sub set of the voter nodes. Our third contribution is to investigate the reason for having voting conflicts and the weakness of consensuses as a correctness property. Finally, we have shown how to detect byzantine and failure nodes in the blockchain.","PeriodicalId":240441,"journal":{"name":"2019 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126774757","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: 6

Title Page i 第1页

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/smartiot.2019.00001

引用次数: 0

Attention Based Speech Model for Japanese Recognization 基于注意的日语语音识别模型

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00071

Deguo Mu, Tao Zhu, Guoliang Xu, Han Li, Dongbin Guo, Yongquan Liu

引用次数: 0

Hybrid Network Assisted Dynamic Worker Recruitment Algorithm 混合网络辅助动态工人招聘算法

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00046

A. Lu, Jinghua Zhu

{"title":"Hybrid Network Assisted Dynamic Worker Recruitment Algorithm","authors":"A. Lu, Jinghua Zhu","doi":"10.1109/SmartIoT.2019.00046","DOIUrl":"https://doi.org/10.1109/SmartIoT.2019.00046","url":null,"abstract":"With the wide application of mobile electronic devices such as smartphones, the emerging mobile crowd sensing (MCS) has gradually become an effective way of real-time sensing and information collection. In the MCS system, worker recruitment is a core and common research issue. The cold start problem limits the application of traditional MCS worker recruitment methods. Introducing social relationships can solve the cold start problem to some extent. Therefore, this paper borrows the idea of influence propagation on social networks and proposes a worker recruitment algorithm based on hybrid network mixing social network and communication network. The core idea is that first select seed workers according to the recruitment probability by using the communication network, then initiate the task spread on social networks and communication networks at the same time in a greedy way. The goal of worker recruitment is to maximize task spatial coverage. When calculating the probability of recruitment, this paper considers various factors such as worker's ability, sojourn time and worker's movement to improve the accuracy of recruitment probability. The experimental results on real datasets show that compared with the existing algorithms, the algorithm in this paper can guarantee the time constraint of the task and have better performance in terms of spatial coverage and running time.","PeriodicalId":240441,"journal":{"name":"2019 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128380849","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

More Competitive Feature Extraction Network for Instance Segmentation 更具竞争力的实例分割特征提取网络

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00016

Ying Xu, Huixiang Qiao, Yongping Zhang, Lei Lei, Tuozhong Yao

{"title":"More Competitive Feature Extraction Network for Instance Segmentation","authors":"Ying Xu, Huixiang Qiao, Yongping Zhang, Lei Lei, Tuozhong Yao","doi":"10.1109/SmartIoT.2019.00016","DOIUrl":"https://doi.org/10.1109/SmartIoT.2019.00016","url":null,"abstract":"Instance segmentation has a wide range of applications in autonomous driving, video security and so on. Mask R-CNN, which introduces Feature Pyramid Networks (FPN), is a simple and effective instance segmentation framework. However, it still has some problems such as false detection, missed detection, and low instance segmentation accuracy. To address such problems, we make corresponding improvements on Mask R-CNN. A more competitive feature extraction module, which we call Squeeze-and-Excitation feature model, is thereupon proposed. Squeeze-and-Excitation feature model performs a bottom-up feature fusion on the FPN output layer, making the underlying features easier to propagate. In particular, a concurrent spatial and channel Squeeze-and-Excitation module (scSE) is employed in it. The application of scSE can improve the adaptability of the feature channel and aggregate relevant spatial information, which greatly reduce false detection and missed detection. In addition, during the final segmentation phase, a spatial Squeeze-and-Excitation module (sSE) is used to refine the segmentation. The experimental result reveals that, both detection and instance segmentation have achieved significant improvements in the MS COCO dataset.","PeriodicalId":240441,"journal":{"name":"2019 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127008924","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

Budget-Aware Equilibrium Offloading for Mobile Edge Computing 基于预算感知的移动边缘计算均衡卸载

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00067

Xiuyuan Yang, Ran Bi

引用次数: 2

Non-Subsampled Shearlet Transform Based Seismic Data Denoising via Proximal Classifier with Consistency 基于非下采样Shearlet变换的地震数据一致性近端分类去噪

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00080

Yu Sang, Jinguang Sun, Xiangfu Meng, Haibo Jin, Yanfei Peng, Xinjun Zhang

{"title":"Non-Subsampled Shearlet Transform Based Seismic Data Denoising via Proximal Classifier with Consistency","authors":"Yu Sang, Jinguang Sun, Xiangfu Meng, Haibo Jin, Yanfei Peng, Xinjun Zhang","doi":"10.1109/SmartIoT.2019.00080","DOIUrl":"https://doi.org/10.1109/SmartIoT.2019.00080","url":null,"abstract":"The sparse transform based seismic denoising is one of the most effective and widely used approaches in seismic data processing. In this paper, we present a novel and unconventional seismic data denoising method based on the non-subsampled shearlet transform (NSST) and proximal classifier with consistency (PCC). NSST is an emerging and excellent multi-scale, multi-direction and optimal sparsity analysis method, which can provide nearly optimal approximation of the decomposed seismic effective signals. Unlike traditional sparse transform based methods that often use a thresholding operator and corresponding inverse transform to denoise seismic data, our proposed method employs a superior performance PCC to classify the NSST coefficients of seismic data before thresholding operator. The added step can effectively divide the NSST coefficients into reflected signal information-related coefficients and noise-related coefficients, which can preserve the edge of reflected signals and keep the information of events intact as much as possible. In addition, we also introduce an adaptive threshold computing method and a soft-thresholding method to achieve seismic data denoising better. A typical synthetic example is used to demonstrate the superior performance of the proposed method over two well-known sparse transform based denoising methods. Besides, we also apply the proposed method to real seismic data, achieving the satisfactory denoising results.","PeriodicalId":240441,"journal":{"name":"2019 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134015162","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