This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P 2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P 2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2 % of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90 % contain road anomalies in need of repair.

There is a recent trend toward rule-based authorization systems to achieve flexible security policies. Also, new sensing technologies in pervasive computing make it possible to define context-sensitive rules, such as “allow database access only to staff who are currently located in the main office. ” However, these rules, or the facts that are needed to verify authority, often involve sensitive context information. This paper presents a secure context-sensitive authorization system that protects confidential information in facts or rules. Furthermore, our system allows multiple hosts in a distributed environment to perform the evaluation of an authorization query in a collaborative way; we do not need a universally trusted central host that maintains all the context information. The core of our approach is to decompose a proof for making an authorization decision into a set of sub-proofs produced on multiple different hosts, while preserving the integrity and confidentiality policies of the mutually untrusted principals operating these hosts. We prove the correctness of our algorithm. 1

Context-aware systems represent extremely complex and heterogeneous distributed systems, composed of sensors, actuators, application components, and a variety of context processing components that manage the flow of context information between the sensors/actuators and applications. The need for middleware to seamlessly bind these components together is well recognised. Numerous attempts to build middleware or infrastructure for context-aware systems have been made, but these have provided only partial solutions; for instance, most have not adequately addressed issues such as mobility, fault tolerance or privacy.

We present WildCAT, an extensible Java framework to ease the creation of context-aware applications. WildCAT provides a simple yet powerful dynamic model to represent an application’s execution context. The context information can be accessed by application programmers through two complimentary interfaces: synchronous requests (pull mode) and asynchronous notifications (push mode). Internally, WildCAT is designed as a framework supporting different levels of extensions, from the simple configuration of the default generic implementation to completely new implementations tailored to specific needs. A given application can mix different implementations for different aspects of its context while only depending on WildCAT’s simple and unified

Abstract. As pervasive environments become more commonplace, the privacy of users is placed at increased risk. The numerous and diverse sensors in these environments can record users ’ contextual information, leading to users unwittingly leaving “digital footprints. ” Users must thus be allowed to control how their digital footprints are reported to third parties. While a significant amount of prior work has focused on location privacy, location is only one type of footprint, and we expect most users to be incapable of specifying fine-grained policies for a multitude of footprints. In this paper we present a policy language based on the metaphor of physical walls, and posit that users will find this abstraction to be an intuitive way to control access to their digital footprints. For example, users understand the privacy implications of meeting in a room enclosed by physical walls. By allowing users to deploy “virtual walls, ” they can control the privacy of their digital footprints much in the same way they control their privacy in the physical world. We present a policy framework and model for virtual walls with three levels of transparency that correspond to intuitive levels of privacy, and the results of a user study that indicates that our model is easy to understand and use. 1

The complexity of developing and deploying context-aware pervasive-computing applications calls for distributed software infrastructures that assist applications to collect, aggregate, and disseminate contextual data. In this paper, we motivate a data-centric design for such an infrastructure to support context-aware applications. Our middleware system, Solar, treats contextual data sources as stream publishers and the core of Solar is a scalable and self-organizing peer-to-peer overlay to support data-driven services. We present how different services could be systematically integrated on top of the Solar overlay. We also discuss our experience and lessons learned when using Solar to support several implemented scenarios. We conclude that a data-centric infrastructure is necessary to facilitate both development and deployment of pervasive-computing applications.

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Abstract—Context-aware services need to acquire context information from heterogeneous context sources. The diversity of service requirements posts challenges on context provisioning systems as well as their programming models. This paper proposes COPAL (COntext Provisioning for ALl) — an adaptive approach to context provisioning. COPAL is at first a runtime middleware, which provides loose-coupling between context and its processing. The component architecture of COPAL ensures that new context processing functions can be added dynamically. A set of context processing patterns are proposed to customize context attributes and compose context provisioning schemes. The COPAL components and models are reflected in a Domain Specific Language (DSL), which can further reduce the development efforts of context provisioning using automatic code generation. A motivating scenario is used throughout the paper to illustrate COPAL approach. I.

In a pervasive computing environment, the number and variety of resources (services, devices, and contextual information resources) make it necessary for applications to accurately discover the best ones quickly. Thus a resource-discovery service, which locates specific resources and establishes network connections as better resources become available, is necessary for those applications. The performance of the resource-discovery service is important when the applications are in a dynamic and mobile environment. In this thesis, however, we do not focus on the resource- discovery technology itself, but the evaluation of the scalability and mobility of the resource discovery module in Solar, a context fusion middleware. Solar has a naming service that provides resource discovery, since the resource names encode static and dynamic attributes. The results of our experiments show that Solar’s resource discovery performed generally well in a typical dynamic environment, although Solar can not be scaled as well as it should. And we identify the implementation issues related to that problem. We also discuss experience, insights, and lessons learned from our quantitative analysis of the experiment results. ii Acknowledgments

Abstract. We propose that traditional case-based recommender systems can be improved by informing them with context data describing the user’s environment. We outline existing applications with similar objectives and describe an application of our own — Ticketyboo — which uses music listening preferences and context information from users ’ calendars to recommend tickets for music concerts. This data is gathered by virtual sensors that monitor each user’s music player and calendar applications. The novelty of this approach is that context data is provided to Ticketyboo via a dedicated context infrastructure. This results in a clear separation between the providers and consumers of context data. By utilising context data in this way, minimal user input/feedback is required to guide the system since the need for explicit user feedback is negated. 1