2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)最新文献

Wi-Eye: Tracking Urban Private Vehicles with Inter-Vehicle Communications and Sparse Video Surveillance Cameras Wi-Eye:利用车辆间通信和稀疏视频监控摄像头跟踪城市私家车

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-11 DOI: 10.1109/SAHCN.2018.8397131

Yang Wang, Zhiwei Lv, Wuji Chen, Hengchang Liu

{"title":"Wi-Eye: Tracking Urban Private Vehicles with Inter-Vehicle Communications and Sparse Video Surveillance Cameras","authors":"Yang Wang, Zhiwei Lv, Wuji Chen, Hengchang Liu","doi":"10.1109/SAHCN.2018.8397131","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397131","url":null,"abstract":"Due to the sparse distribution of video surveillance cameras and low installation rate of dash-mounted online telematics systems, tracking precise trajectories of urban private vehicles is a challenging task. Previous studies on vehicle tracking are mostly concerned with recovering trajectories with low- sampling rate GPS coordinates or captured surveillance information by identifying road traffic patterns from this information. Nevertheless, to the best of our knowledge, none of them have considered using the vehicle encounter information to enhance the sampling rate as well as the time-varying and in-group characteristics of vehicle traffic patterns, let alone to achieve vehicle tracking. With this insight, we divide all vehicles into clusters with a Canopy and K- means combined algorithm for different time periods and use an exponential distribution to approximate the time that vehicles from one cluster encounter a fixed Wi-Fi hotspot in the future during a given time period and at a specific location. Based on these preliminary results, we propose a novel approach to select the optimal data packet transmission scheme between encountered vehicles to transfer vehicle encounter information to the server as much and quickly as possible, and then calculate the trajectories of all private vehicles accurately. We evaluate our solution via real-world private vehicles and road surveillance system datasets. Experimental results demonstrate that our approach outperforms other solutions in terms of the accuracy ratio of vehicle tracking.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927279","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

Lotus: Evolutionary Blind Regression over Noisy Crowdsourced Data Lotus:嘈杂众包数据的进化盲回归

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-11 DOI: 10.1109/SAHCN.2018.8397096

C. Li, Shan Chang, Hongzi Zhu, Hang Chen, Ting Lu

{"title":"Lotus: Evolutionary Blind Regression over Noisy Crowdsourced Data","authors":"C. Li, Shan Chang, Hongzi Zhu, Hang Chen, Ting Lu","doi":"10.1109/SAHCN.2018.8397096","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397096","url":null,"abstract":"In mobile crowd sensing (MCS) applications, a public model of a system or phenomenon is expected to be derived from sensory data, i.e., observations, collected by mobile device users, through regression modeling. Unique features of MCS data bring the regression task new challenges. First, observations are error-prone and private, making it of great difficulty to derive an accurate model without acquiring raw data. Second, observations are non-stationary and opportunistically generated, calling for an evolutionary model updating mechanism. Last, mobile devices are resource-constrained, posing an urgent demand for lightweight regression schemes. In this paper, we propose an evolutionary blind regression scheme, called Lotus, in MCS settings. The core idea is first to select a 'maximum- safe-subset' of observations locally stored over all participants, which refers to finding a subset containing half of observations, such that the corresponding regression model has a minimum value of residual sum of squares. It implies the inconsistency between observations in the subset is minimized. Since such a maximum-safe- subset selection problem is NP-hard, a distributed greedy hill- climbing algorithm is proposed. Then, based on the resulted regression model, more observations are checked. Selected ones will be used to refine the model. With observations constantly coming, newly selected 'safe' observations are used to make the model evolved. To preserve data privacy, a one-time pad masking mechanism, and a blocking scheme are integrated into the process of regression estimation. Intensive theoretical analysis and extensive trace driven simulations are conducted and the results demonstrate the efficacy of the Lotus design.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116401141","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

On the Throughput Limit of Multi-Hop Wireless Networks with Reconfigurable Antennas 天线可重构多跳无线网络的吞吐量限制研究

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397127

Yanjun Pan, Ming Li, Neng Fan, Yantian Hou

{"title":"On the Throughput Limit of Multi-Hop Wireless Networks with Reconfigurable Antennas","authors":"Yanjun Pan, Ming Li, Neng Fan, Yantian Hou","doi":"10.1109/SAHCN.2018.8397127","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397127","url":null,"abstract":"Reconfigurable antenna (RA) has emerged as a disruptive antenna technology with the potential of significantly improving the capacity of wireless links, by agilely reconfiguring its antenna states. Through jointly optimizing antenna state selection, routing and scheduling, it offers another dimension of opportunity to enhance end- to-end (E2E) throughput in multi-hop wireless networks (MWNs). However, the throughput limit of MWNs with RAs has not been well understood, due to challenges in theoretical modeling and computational intractability caused by a large number of states. In this work, we endeavor to systematically study this problem. We first propose a general antenna state-link conflict graph model to capture the intricate state-link association and corresponding interference relationship in the network. Based on this model, we formulate a max-flow based optimization framework to derive the throughput bound of a given MWN. As this problem is NP-hard, we explore column generation to solve it more efficiently, and propose a heuristic algorithm which can also accelerate the optimal solution. Simulation results show that our proposed algorithms can efficiently approach or compute the optimal throughput, and validate the advantage of antenna reconfigurability in MWNs.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122832146","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

CRH: A Contactless Respiration and Heartbeat Monitoring System with COTS RFID Tags 基于COTS RFID标签的非接触式呼吸和心跳监测系统

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397132

Run Zhao, Dong Wang, Qian Zhang, Haonan Chen, Anna Huang

{"title":"CRH: A Contactless Respiration and Heartbeat Monitoring System with COTS RFID Tags","authors":"Run Zhao, Dong Wang, Qian Zhang, Haonan Chen, Anna Huang","doi":"10.1109/SAHCN.2018.8397132","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397132","url":null,"abstract":"Monitoring respiration and heartbeat contributes to disease prediction, sub-health diagnosis, exercise and sleep quality analysis, fatigue warning, and even emotion estimation. There is a compelling need for contactless, easy-to-deploy and long-term respiration and heartbeat monitoring. In this paper, we present CRH, an RFID-based contactless respiration and heartbeat monitoring system. The key insight is that the RFID signal fluctuation induced by the chest motion is synchronous with respiration and heartbeat. Therefore, CRH collects the temporal phase information from the tag array near or on body to extract respiration and heartbeat signals using a sequence of signal processing techniques. We propose a signal separation method based on multi-tag empirical mode decomposition (EMD) to obtain respiration rate and heart rate after preprocessing. Furthermore, CRH can also detect intense motions and abnormal respiration. We implement and evaluate CRH using Commercial Off-The-Shelf (COTS) RFID devices. Extensive experimental results in different scenarios show that CRH can achieve high accuracy for monitoring multi-user respiration and heart rates, validating its wide applicability and high reliability for contactless fine-grained respiration and heartbeat monitoring.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114410266","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: 33

ViType: A Cost Efficient On-Body Typing System through Vibration ViType:一种通过振动的成本效益高的身体类型系统

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397098

Wenqiang Chen, Maoning Guan, Yandao Huang, Lu Wang, Rukhsana Ruby, Wen Hu, Kaishun Wu

{"title":"ViType: A Cost Efficient On-Body Typing System through Vibration","authors":"Wenqiang Chen, Maoning Guan, Yandao Huang, Lu Wang, Rukhsana Ruby, Wen Hu, Kaishun Wu","doi":"10.1109/SAHCN.2018.8397098","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397098","url":null,"abstract":"Nowadays, smart wristbands have become one of the most prevailing wearable devices as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to extend the input. Yet by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which firstly leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rate, ViType designs a set of novel mechanisms, including an artificial neural network to process the vibration signals, and a runtime calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust to fight against various confounding factors. The average recognition accuracy is 94.8% with an initial training sample size of 20 for each key, which is 1.52 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98% on average during one month.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116847221","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: 28

COALA: Collision-Aware Link Adaptation for LTE in Unlicensed Band 非授权频带LTE的冲突感知链路自适应

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397104

Kangjin Yoon, Weiping Sun, Sunghyun Choi

引用次数: 3

IARA: An Intelligent Application-Aware VNF for Network Resource Allocation with Deep Learning 基于深度学习的智能应用感知网络资源分配VNF

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397153

Jun Xu, Jingyu Wang, Q. Qi, Haifeng Sun, Bo He

{"title":"IARA: An Intelligent Application-Aware VNF for Network Resource Allocation with Deep Learning","authors":"Jun Xu, Jingyu Wang, Q. Qi, Haifeng Sun, Bo He","doi":"10.1109/SAHCN.2018.8397153","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397153","url":null,"abstract":"Application awareness is essential for traffic engineering and Quality of Service (QoS) guarantee, especially in Internet of Things (IoT). Software Defined Network (SDN) with centralized controlling of network resources provides opportunities for fine- grained resource allocation. However, the controller cannot autonomously identify applications effectively, because sampling and recognizing traffic data consumes a lot of IO and computing resources. In this demonstration, we provide an intelligent application-aware Virtualized Network Function (VNF) with deep learning technology to identify the network traffic. The traffic type information will be mapped to specific network requirements and utilized to search appropriate route paths for different applications. The intelligent VNF is deployed on a GPU-equipped standalone server and works on the data plane of SDN. It identifies the traffic and sends the type information to the controller through OpenFlow protocol. The experiments show that by introducing the type information, SDN controller can assign more appropriate route paths for different types of traffic and highly improve the network QoS.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123621145","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

Frame-Selective Wireless Attack Using Deep-Learning-Based Length Prediction 基于深度学习长度预测的帧选择无线攻击

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397145

Gyungmin Kim, Yonggang Kim, Jaehyoung Park, Hyuk Lim

引用次数: 1

WiTrace: Centimeter-Level Passive Gesture Tracking Using WiFi Signals WiTrace:利用WiFi信号进行厘米级被动手势跟踪

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397120

Lei Wang, Ke Sun, Haipeng Dai, A. Liu, Xiaoyu Wang

{"title":"WiTrace: Centimeter-Level Passive Gesture Tracking Using WiFi Signals","authors":"Lei Wang, Ke Sun, Haipeng Dai, A. Liu, Xiaoyu Wang","doi":"10.1109/SAHCN.2018.8397120","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397120","url":null,"abstract":"Gesture tracking is a basic Human-Computer Interaction mechanism to control devices such as electronic Internet of Things and VR/AR devices. However, prior WiFi signal based systems focus on gesture recognition and provide results with insufficient accuracy, and thus cannot be applied for highprecision gesture tracking. In this paper, we propose a CSI based device-free gesture tracking system, called WiTrace, which leverages the CSI values extracted from WiFi signals to enable accurate gesture tracking. For 1D tracking, WiTrace derives the phase of the signals reflected by the hand from the composite signals, and measures the phase changes to obtain the movement distance. For 2D tracking, WiTrace proposes the first CSI based scheme to accurately estimate the initial position, and adopts the Kalman filter based on Continuous Wiener Process Acceleration model to further filter out tracking noise. Our results show that WiTrace achieves the estimated accuracy of 3.91 cm for initial position on average, and achieves cm-level accuracy, with mean tracking errors of 1.46 cm and 2.09 cm for 1D tracking and 2D tracking, respectively.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279668","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: 15

Crowdlearning: Crowded Deep Learning with Data Privacy 众学习:具有数据隐私的拥挤深度学习

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397100

Linlin Chen, Taeho Jung, Haohua Du, Jianwei Qian, Jiahui Hou, Xiangyang Li

{"title":"Crowdlearning: Crowded Deep Learning with Data Privacy","authors":"Linlin Chen, Taeho Jung, Haohua Du, Jianwei Qian, Jiahui Hou, Xiangyang Li","doi":"10.1109/SAHCN.2018.8397100","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397100","url":null,"abstract":"Deep Learning has shown promising performance in a variety of pattern recognition tasks owning to large quantities of training data and complex structures of neural networks. However conventional deep neural network (DNN) training involves centrally collecting and storing the training data, and then centrally training the neural network, which raises much privacy concerns for the data producers. In this paper, we study how to enable deep learning without disclosing individual data to the DNN trainer. We analyze the risks in conventional deep learning training, then propose a novel idea - Crowdlearning, which decentralizes the heavy- load training procedure and deploys the training into a crowd of computation-restricted mobile devices who generate the training data. Finally, we propose SliceNet, which ensures mobile devices can afford the computation cost and simultaneously minimize the total communication cost. The combination of Crowdlearning and SliceNet ensures the sensitive data generated by mobile devices never leave the devices, and the training procedure will hardly disclose any inferable contents. We numerically simulate our prototype of SliceNet which crowdlearns an accurate DNN for image classification, and demonstrate the high performance, acceptable calculation and communication cost, satisfiable privacy protection, and preferable convergence rate, on the benchmark DNN structure and dataset.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130464433","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: 5