2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)最新文献_第3页

The telepathic phone: Frictionless activity recognition from WiFi-RSSI 心灵感应手机:WiFi-RSSI无摩擦活动识别

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) Pub Date : 2014-03-24 DOI: 10.1109/PerCom.2014.6813955

S. Sigg, Ulf Blanke, G. Tröster

引用次数: 129

Soft Authentication with Low-Cost Signatures 低成本签名软认证

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) Pub Date : 2014-03-24 DOI: 10.1109/PerCom.2014.6813958

S. Buthpitiya, A. Dey, M. Griss

{"title":"Soft Authentication with Low-Cost Signatures","authors":"S. Buthpitiya, A. Dey, M. Griss","doi":"10.1109/PerCom.2014.6813958","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813958","url":null,"abstract":"As mobile context-aware services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities for applications such as user authentication, adaptive assist technologies and remote elder-care monitoring. Existing approaches have limited applicability as they regularly poll power-hungry sensors (e.g., accelerometer, GPS) reducing the availability of devices to perform anomaly detection. This paper present SALCS (Soft Authentication with Low-Cost Signatures), an approach for anomaly detection on a user's routine comprised of a collection of anomaly detection techniques utilizing soft-sensor data (e.g., call-logs, messages) and radio channel information (e.g., GSM cell IDs), all of which are available as part of a phone's routine usage. Using these information sources we model aspects of a person's routine, such as movement, messaging and conversation patterns. We present extensive evaluations of the individual anomaly detection techniques, compare the collection SALCS to an existing power-hungry approach showing SALCS has a 7.6% higher detection rate and gives 5x better coverage throughout the day.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126078598","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: 14

Fixed-lag particle filter for continuous context discovery using Indian Buffet Process 使用印度自助餐过程的连续上下文发现的固定滞后粒子滤波器

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) Pub Date : 2014-03-24 DOI: 10.1109/PERCOM.2014.6813939

Nguyen Cong Thuong, S. Gupta, S. Venkatesh, Dinh Q. Phung

{"title":"Fixed-lag particle filter for continuous context discovery using Indian Buffet Process","authors":"Nguyen Cong Thuong, S. Gupta, S. Venkatesh, Dinh Q. Phung","doi":"10.1109/PERCOM.2014.6813939","DOIUrl":"https://doi.org/10.1109/PERCOM.2014.6813939","url":null,"abstract":"Exploiting context from stream data in pervasive environments remains a challenge. We aim to extract proximal context from Bluetooth stream data, using an incremental, Bayesian nonparametric framework that estimates the number of contexts automatically. Unlike current approaches that can only provide final proximal grouping, our method provides proximal grouping and membership of users over time. Additionally, it provides an efficient online inference. We construct co-location matrix over time using Bluetooth data. A Poisson-exponential model is used to factorize this matrix into a factor matrix, interpreted as proximal groups, and a coefficient matrix that indicates factor usage. The coefficient matrix follows the Indian Buffet Process prior, which estimates the number of factors automatically. The non-negativity and sparsity of factors are enforced by using the exponential distribution to generate the factors. We propose a fixed-lag particle filter algorithm to process data incrementally. We compare the incremental inference (particle filter) with full batch inference (Gibbs sampling) in terms of normalized factorization error and execution time. The normalized error obtained through our incremental inference is comparable to that of full batch inference, whilst the execution time is more than 100 times faster. The discovered factors have similar meaning to the results of the popular Louvain method for community detection.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124203729","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

Pushing the spatio-temporal resolution limit of urban air pollution maps 突破城市空气污染地图的时空分辨率极限

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) Pub Date : 2014-03-24 DOI: 10.1109/PerCom.2014.6813946

David Hasenfratz, O. Saukh, C. Walser, C. Hueglin, M. Fierz, L. Thiele

{"title":"Pushing the spatio-temporal resolution limit of urban air pollution maps","authors":"David Hasenfratz, O. Saukh, C. Walser, C. Hueglin, M. Fierz, L. Thiele","doi":"10.1109/PerCom.2014.6813946","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813946","url":null,"abstract":"Up-to-date information on urban air pollution is of great importance for health protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 25 million measurements. We collected the measurements throughout more than a year using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with subweekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115841697","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: 97

Kintense: A robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data kintensity:一个强大、准确、实时和不断发展的系统，用于从流3D骨骼数据中检测攻击行为

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) Pub Date : 2013-11-11 DOI: 10.1145/2517351.2517396

S. Nirjon, C. Greenwood, Carlos Torres, S. Zhou, J. Stankovic, Hee-Jung Yoon, Ho-Kyeong Ra, Can Basaran, Taejoon Park, S. Son

{"title":"Kintense: A robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data","authors":"S. Nirjon, C. Greenwood, Carlos Torres, S. Zhou, J. Stankovic, Hee-Jung Yoon, Ho-Kyeong Ra, Can Basaran, Taejoon Park, S. Son","doi":"10.1145/2517351.2517396","DOIUrl":"https://doi.org/10.1145/2517351.2517396","url":null,"abstract":"Kintense is a robust, accurate, real-time, and evolving system for detecting aggressive actions such as hitting, kicking, pushing, and throwing from streaming 3D skeleton joint coordinates obtained from Kinect sensors. Kintense uses a combination of: (1) an array of supervised learners to recognize a predefined set of aggressive actions, (2) an unsupervised learner to discover new aggressive actions or refine existing actions, and (3) human feedback to reduce false alarms and to label potential aggressive actions. This paper describes the design and implementation of Kintense and provides empirical evidence that the system is 11% - 16% more accurate and 10% - 54% more robust to changes in distance, body orientation, speed, and person when compared to standard techniques such as dynamic time warping (DTW) and posture based gesture recognizers. We deploy Kintense in two multi-person households and demonstrate how it evolves to discover and learn unseen actions, achieves up to 90% accuracy, runs in real-time, and reduces false alarms with up to 13 times fewer user interactions than a typical system.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114790425","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

Keynote: WInternet: From Net of Things to Internet of Things 主题演讲:WInternet:从物联网到物联网

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) Pub Date : 2013-06-15 DOI: 10.1109/PerCom.2014.6813936

Wei Zhao

引用次数: 8