2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)最新文献

{"title":"Differentially-private release of check-in data for venue recommendation","authors":"Daniele Riboni, C. Bettini","doi":"10.1109/PerCom.2014.6813960","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813960","url":null,"abstract":"Recommender systems suggesting venues offer very useful services to people on the move and a great business opportunity for advertisers. These systems suggest venues by matching the current context of the user with the venue features, and consider the popularity of venues, based on the number of visits (“check-ins”) that they received. Check-ins may be explicitly communicated by users to geo-social networks, or implicitly derived by analysing location data collected by mobile services. In general, the visibility of explicit check-ins is limited to friends in the social network, while the visibility of implicit check-ins limited to the service provider. Exposing check-ins to unauthorized users is a privacy threat since recurring presence in given locations may reveal political opinions, religious beliefs, or sexual orientation, as well as absence from other locations where the user is supposed to be. Hence, on one side mobile app providers host valuable information that recommender system providers would like to buy and use to improve their systems, and on the other we recognize serious privacy issues in releasing that information. In this paper, we solve this dilemma by providing formal privacy guarantees to users and trusted mobile providers while preserving the utility of check-in information for recommendation purposes. Our technique is based on the use of differential privacy methods integrated with a pre-filtering process, and protects against both an untrusted recommender system and its users, willing to infer the venues and sensitive locations visited by other users. Extensive experiments with a large dataset of real users' check-ins show the effectiveness of our methods.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"62 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":"129626326","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}

{"title":"Nonparametric discovery of human routines from sensor data","authors":"Feng-Tso Sun, Yi-Ting Yeh, Heng-Tze Cheng, Cynthia Kuo, M. Griss","doi":"10.1109/PerCom.2014.6813938","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813938","url":null,"abstract":"People engage in routine behaviors. Automatic routine discovery goes beyond low-level activity recognition such as sitting or standing and analyzes human behaviors at a higher level (e.g., commuting to work). With recent developments in ubiquitous sensor technologies, it becomes easier to acquire a massive amount of sensor data. One main line of research is to mine human routines from sensor data using parametric topic models such as latent Dirichlet allocation. The main shortcoming of parametric models is that it assumes a fixed, pre-specified parameter regardless of the data. Choosing an appropriate parameter usually requires an inefficient trial-and-error model selection process. Furthermore, it is even more difficult to find optimal parameter values in advance for personalized applications. In this paper, we present a novel nonparametric framework for human routine discovery that can infer high-level routines without knowing the number of latent topics beforehand. Our approach is evaluated on public datasets in two routine domains: a 34-daily-activity dataset and a transportation mode dataset. Experimental results show that our nonparametric framework can automatically learn the appropriate model parameters from sensor data without any form of model selection procedure and can outperform traditional parametric approaches for human routine discovery tasks.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"19 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":"126555970","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}

{"title":"A refined limit on the predictability of human mobility","authors":"Gavin Smith, R. Wieser, James Goulding, Duncan Barrack","doi":"10.1109/PerCom.2014.6813948","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813948","url":null,"abstract":"It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiotemporal granularity down to the level of GPS data, and under a similar methodology results once again suggested a high predictability upper bound (i.e. 90% when movement was quantized down to a spatial resolution approximately the size of a large building). In this work we reconsider the derivation of the upper bound to movement predictability. By considering real-world topological constraints we are able to achieve a tighter upper bound, representing a more refined limit to the predictability of human movement. Our results show that this upper bound is between 11-24% less than previously claimed at a spatial resolution of approx. 100m×100m, with a greater improvement for finer spatial resolutions. This indicates that human mobility is potentially less predictable than previously thought. We provide an in-depth examination of how varying the spatial and temporal quantization affects predictability, and consider the impact of corresponding limits using a large set of real-world GPS traces. Particularly at fine-grained spatial quantizations, where a significant number of practical applications lie, these new (lower) upper limits raise serious questions about the use of location information alone for prediction, contributing more evidence that such prediction must integrate external variables.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"120 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":"133145845","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}

{"title":"A regression-based radar-mote system for people counting","authors":"Jin He, A. Arora","doi":"10.1109/PerCom.2014.6813949","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813949","url":null,"abstract":"People counting is key to a diverse set of sensing applications. In this paper, we design a mote-scale event-driven solution that uses a low-power pulsed radar to estimate the number of people within the ~10m radial range of the radar. In contrast to extant solutions, most of which use computer vision, our solution is light-weight and private. It also better tolerates the presence of obstacles that partially or fully impair line of sight; this is achieved by accounting for “small” indirect radio reflections via joint time-frequency domain features. The counter itself is realized using Support Vector Regression; the regression map is learned from a medium sized dataset of 0-~40 people in various indoor room settings. 10-fold cross validation of our counter yields a mean absolute error of 2.17 between the estimated count and the ground truth and a correlation coefficient of 0.97.We compare the performance of our solution with baseline counters.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"61 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":"131073132","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}

{"title":"Commitment-based device pairing with synchronized drawing","authors":"Mohit Sethi, M. Antikainen, T. Aura","doi":"10.1109/PerCom.2014.6813959","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813959","url":null,"abstract":"Secure device pairing is a widely studied problem. Local wireless connections such as Bluetooth and WiFi typically rely on user-entered secret keys or manually verified authentication codes. Several recent proposals replace these with contextual or location-dependent sensor inputs, which are assumed to be secret from anyone not present at the location where the pairing takes place. These protocols have to cope with a fuzzy secret, i.e. noisy secret input that differs between the devices. In this paper, we overview such protocols and propose a new variation using time-based opening of commitments. Our protocol has the advantage of treating the fuzzy secret as one piece of data rather than requiring it to be partitioned into time intervals, and being more robust against variations in input entropy than those based on error correction codes. The protocol development is motivated by the discovery of a novel human source for the fuzzy secret: synchronized drawing with two fingers of the same hand on two touch screens or surfaces. Metrics for measuring the distance between the drawings are described and evaluated. We implement a prototype of this surprisingly simple and natural pairing mechanism and show that it accurately differentiates between true positives and man-in-the-middle attackers.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"28 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":"129964938","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}

{"title":"SocketWatch: An autonomous appliance monitoring system","authors":"T. Ganu, Dwi A. P. Rahayu, D. Seetharam, R. Kunnath, Ashok Pon Kumar, V. Arya, S. A. Husain, S. Kalyanaraman","doi":"10.1109/PerCom.2014.6813941","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813941","url":null,"abstract":"A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns. SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"20 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":"132649311","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}

{"title":"Keynote: IoT on the move: The ultimate driving machine as the ultimate mobile thing","authors":"C. Grote","doi":"10.1109/PerCom.2014.6813943","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813943","url":null,"abstract":"Summary form only given. Automotive customers' expectations have significantly changed in the past decade. They are influenced by new technologies such as the high-bandwith mobile Internet and increasingly capable mobile consumer electronics devices. At the same time, urbanization and the necessity for ecological sustainability provoke new demands. Internet based communities are drivers for a developing Shareconomy. It is evident that that the conventional car, whether it is the all mechanical Auto 1.0 or the electronics and driving assistance systems loaded Auto 2.0, will not be able to meet these expectations and demands. The upcoming Auto 3.0 is no longer mobility hardware, but instead integral part of a seamless intermodal mobility chain for individuals. It is connected with the surrounding environment and other modes of transport. It is capable of understanding the mobility requirements of the individual, and it delivers the right information at the right time in order to make the right decisions. For this to work, the Auto 3.0 will need access to all sorts of data that is always current, comprehensive, filtered, and geographically referenced. Important enabling technologies are cross-OEM crowd sourcing of data as well as processing and real-time data analytics in the cloud. The vehicle finally becomes part of the Internet of Things. Benefits that can already today be achieved are for example traffic jam avoidance by enrichment of the navigation map with relevant real-time traffic flow information, reduction of CO2 emissions by anticipatory adaption of engines, power train and recuperation, and extension of the mileage of electric vehicles. The next steps are to open up the systems and to establish market places for the interconnection of different services. Traditionally, the Internet of Things, services realized by combining sensors, computing backends, and apps are part of a single offering and cannot be combined with other services. Platforms such as IFTTT, on the other hand, connect different ecosystems and enable a new class of services. Especially the Auto 3.0 will benefit from such an open platform which allows the implementation of multiplicity of connected and location-based services including data from numerous systems, operators, and stakeholders. Additional potentials in efficiency for future smart cities can be realized by this interconnection of different systems and participants such as vehicles, buildings, urban administration, and utilities. Establishing such a platform is an important objective.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"51 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114020572","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}

{"title":"Missing sensor value estimation method for participatory sensing environment","authors":"H. Kurasawa, Hiroshi Sato, Atsushi Yamamoto, Hitoshi Kawasaki, Motonori Nakamura, Yohei Fujii, Hajime Matsumura","doi":"10.1109/PerCom.2014.6813950","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813950","url":null,"abstract":"Participatory sensing produces incomplete sensor data. Thus, we have to fill in the gaps of any missing values in the sensor data in order to provide sensor-based services. We propose a method to estimate a missing value of incomplete sensor data. It accurately estimates a missing value by repeating two processes: selecting sensors locally correlated with the sensor that includes the missing value and then updating the training sensor dataset that consist of data from the selected sensors available for multiple regression. This procedure effectively helps to find more suitable neighbor records of a query record from the training sensor dataset and to refine the regression model using the records. It overcomes three problems that other estimation methods have: a decrease in the amount of available training sensor dataset due to missing values, the difficulty in finding similar records of a query due to the “curse of dimensionality,” and the complexity in formalizing the estimation model due to “overfitting.” The main feature of our method is the way it repeatedly prunes inessential sensors while exploiting the anti-monotone property in which the training sensor dataset R' that consist of the sensors V' ⊂ V is larger than the data R that consist of V. Empirical evaluations done using public datasets in which we appended missing values show that our method increases the training sensor dataset for estimation and improves estimation accuracy through repeated sensor selections. Furthermore, we confirmed through a field trial and a life-log enrichment trial, that our method was effective for estimating missing sensor values in a participatory sensing environment.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"24 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":"125114751","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}

{"title":"Inferring occupancy from opportunistically available sensor data","authors":"L. Yang, Kevin Ting, M. Srivastava","doi":"10.1109/PerCom.2014.6813945","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813945","url":null,"abstract":"Commercial and residential buildings are usually instrumented with meters and sensors that are deployed as part of a utility infrastructure installed by companies that provide services such as electricity, water, gas, security, phone, etc. As part of their normal operation, these service providers have direct access to information from the sensors and meters. A concern arises that the sensory information collected by the providers, although coarse-grained, can be subject to analysis that reveals private information about the users of the building. Oftentimes, multiple services are provided by the same company, in which case the potential for leakage of private information increases. Our research seeks to investigate the extent to which easily available sensory information may be used by external service providers to make occupancy-related inferences. Particularly, we focus on inferences from two different sources: motion sensors, which are installed and monitored by security companies, and smart electric meters, which are deployed by electric companies for billing and demand-response management. We explore the motion sensor scenario in a three-person single-family home and the electric meter scenario in a twelve-person university lab. Our exploration with various inference methods shows that sensory information available to service providers can enable them to make undesired occupancy related inferences, such as levels of occupancy or even the identities of current occupants, significantly better than naive prediction strategies that do not make use of sensor information.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"39 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":"127516162","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}

{"title":"Using rule mining to understand appliance energy consumption patterns","authors":"Sami Rollins, Nilanjan Banerjee","doi":"10.1109/PerCom.2014.6813940","DOIUrl":"https://doi.org/10.1109/PerCom.2014.6813940","url":null,"abstract":"Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study of factors that influence energy consumption of individual devices in the home. Our first contribution is collection of a context-rich data set from six homes across the United States. The second contribution of this work is a set of insights into key factors influencing energy usage derived by the novel application of a rule mining algorithm to identify significant associations between energy usage and four key features: hour of the day, day of the week, use of other appliances in the home, and user-supplied annotations of activities such as working or cooking. Our analysis confirms our hypothesis that, though most devices show a regular pattern of daily or weekly use, this is not true for all devices. Associations that relate use of two different devices in the same home are often stronger, and are observed for nearly 25% of device uses. Overall, we observe that the associations derived from the first five weeks of data in our data set are sufficient to explain nearly 70% of the device uses in the subsequent five weeks of data, and over 90% of the associations identified during the first five weeks recur in the latter portion of the data set. The associations identified by our approach may be used to to aid in end-user applications that heighten awareness and encourage energy savings, improve energy disaggregation algorithms, or even detect anomalous uses that may signal problems in aging-in-place homes.","PeriodicalId":263520,"journal":{"name":"2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)","volume":"12 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":"128524647","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}