PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into “trips”; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

Abstract. Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm- participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus.

Abstract—Mobile phones ’ increasing ubiquity has created many opportunities for personal context sensing. Personal activity is an important part of a user’s context, and automatically recognizing it is vital for health and fitness monitoring applications. Recording a stream of activity data enables monitoring patients with chronic conditions affecting ambulation and motion, as well as those undergoing rehabilitation treatments. Modern mobile phones are powerful enough to perform activity classification in real time, but they typically use a static classifier that is trained in advance or require the user to manually add training data after the application is on his/her device. This paper investigates ways of automatically augmenting activity classifiers after they are deployed in an application. It compares active learning and three different semi-supervised learning methods, self-learning, En-Co-Training, and democratic co-learning, to determine which show promise for this purpose. The results show that active learning, En-Co-Training, and democratic co-learning perform well when the initial classifier’s accuracy is low (75-80%). When the initial accuracy is already high (90%), these methods are no longer effective, but they do not hurt the accuracy either. Overall, active learning gave the highest improvement, but democratic colearning was almost as good and does not require user interaction. Thus, democratic co-learning would be the best choice for most applications, since it would significantly increase the accuracy for initial classifiers that performed poorly. I.

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users ’ real lives. Two levels of clustering are used to obtain places of interest. First, user location points are grouped using a time-based clustering technique which discovers stay points while dealing with missing location data. The second level performs clustering on the stay points to obtain stay regions. A grid-based clustering algorithm has been used for this purpose. To obtain more user location points, a client-server system has been installed on the mobile phones, which is able to obtain location information by integrating GPS, Wifi, GSM and accelerometer sensors, among others. An extensive set of experiments have been performed to show the benefits of using the proposed framework, using data from the real life of 8 users over 5 continuous months of natural phone usage.

Much work has been done in the area of monitoring on traditional systems, such as servers, workstations and laptops. User and application behavior has also been studied on a wide range of platforms. Recently, smartphones have seen a dramatic increase in availability and adoption. New monitoring tools are needed to handle the unique demands of these mobile devices, such as minimal energy usage and cellular network activity, and the unique opportunities they provide, such as incorporating contextual information. Smartphones include a wide range of sensors which can be used to provide insights about the context of the activities being monitored. Individuals also use their mobile phones in a much different manner than traditional systems, and these differences have not been fully explored. With a better understanding of how these devices are used, and how common usage patterns impact system performance, we can improve upon system and application design. In this paper, we introduce an integrated monitoring framework for mobile phones which incorporates sensor, system and user activity. Our expectation is that integrated monitoring solutions will provide the foundation for various new solutions. 1.

Abstract. The widespread use of smartphones and body-worn sensors has made continuous and unobtrusive collection of personal data feasible. This has led to the emergence of useful applications in diverse areas such as medical behavioral studies, personal health-care and participatory sensing. However, the nature of highly personal information shared with these applications, together with the additional inferences that could be possibly drawn using the same data leads to a variety of privacy concerns. This paper proposes SensorSafe, an architecture for managing personal sensory information in a privacy-preserving way. Our architecture consists of multiple remote data stores and a broker so users can retain the ownership of their data and management of multiple users can be well supported. SensorSafe also provides a context-aware finegrained access control mechanism by which users can define their own sharing rules based on various conditions including context and behavioral status. We discuss our design of the SensorSafe architecture and provide application examples to show how our system can support user privacy.