In this paper we introduce mechanisms for automated mapping of urban areas that provide a virtual sensor abstraction to the applications. We envision a participatory system that exploits widely available devices as mobile phones to cooperatively read environmental conditions as air quality or noise pollution, and map these measurements to stationary virtual sensors. We propose spatial and temporal coverage metrics for measuring the quality of acquired sensor data that reflect the conditions of urban areas and the uncontrolled movement of nodes. To achieve quality requirements and efficiency in terms of energy consumption, this paper presents two algorithms for coordinating sensing. The first is based on a central control instance, which assigns sensing tasks to mobile nodes based on movement predictions. The second algorithm is based on coordination of mobile nodes in an ad-hoc network. By extensive simulations, we show that these algorithms achieve a high quality of readings, which is about 95% of the maximum possible. Moreover, the algorithms achieve a very high energy efficiency allowing for drastic savings compared to uncoordinated sensing.

This paper introduces mechanisms for the automated detection of mobile objects in urban areas. Widely available devices such as mobile phones with integrated proximity sensors such as RFID readers or Bluetooth cooperatively perform sensing operations to discover mobile objects. In this paper, we propose a coverage metric for assessing the completeness of sensing that considers spatial and temporal aspects. To maximize coverage while minimizing energy consumption of mobile nodes, we propose both a centralized and a distributed coordination algorithm for selecting nodes that need to sense. Moreover, we present strategies that allow selected nodes to perform efficient sense operations. By extensive simulations, we show that distributed coordination achieves drastic energy savings of up to 63%, while limiting the coverage loss to 13%. Moreover, we show that the centralized algorithm loses less than 1% coverage compared to the maximum possible coverage.

With the increasing proliferation of small and cheap GPS receivers, a new way of generating road maps could be witnessed over the last few years. Participatory mapping approaches like OpenStreetMap introduced a way to generate road maps collaboratively from scratch. Moreover, automatic mapping algorithms were proposed, which automatically infer road maps from a set of given GPS traces. Nevertheless, one of the main problems of these maps is their unknown quality in terms of accuracy, which makes them unreliable and, therefore, not applicable for the use in critical scenarios.To address this issue, we propose MapCorrect: An automatic map correction and validation system. MapCorrect automatically collects GPS traces from people's mobile devices to correct a given road map and validate it by identifying those parts of the map that are accurately mapped with respect to some user provided quality requirements. Since fixing a GPS position is a battery draining operation, the collection of GPS data raises concerns about the energy consumption of the participating mobile devices. We tackle this issue by introducing an optimized sensing mechanism that gives the mobile devices notifications indicating those parts of the map that are considered as sufficiently mapped and, therefore, require no further GPS data for their validation. Furthermore, we show by simulation that using this approach up to 50% of the mobile phones' energy can be saved while not impairing the effectiveness of the map correction and validation process at all.

In this paper we introduce a novel scenario for environmental sensing based on the combination of simple and cheap RFID-based sensors and mobile devices like mobile phones with integrated RFID readers. We envision a system that exploits the availability of these devices to cooperatively read sensors installed in the environment, and transmit the data to a server infrastructure. To achieve quality requirements and efficiency in terms of communication cost and energy consumption, this paper presents several algorithms for coordinating update operations. First, mobile nodes form an ad-hoc network for the cooperative management of requested update times to meet the desired update interval and to avoid redundant sensor reading and collisions during read operations. Second, besides this decentralized coordination algorithm, we also show a complementary algorithm that exploits infrastructure based coordination. By extensive simulations we show that our algorithms allow for autonomous operation and achieve a high quality of sensor updates where nearly 100% of the possible updates are performed. Moreover, the algorithms achieve a very high energy efficiency allowing for several hundred hours of operation assuming a typical battery of a mobile phone.

Context-aware applications rely on models of the physical world. Within the Nexus project, we envision a World Wide Space which provides the conceptual and technological framework for integrating and sharing such context models in an open, global platform of context providers. In our ongoing research we tackle important challenges in such a platform including distributed processing of streamed context data, situation recognition by distributed reasoning, efficient management of context data histories, and quality of context information. In this paper we discuss our approach to cope with these challenges and present an extended Nexus architecture.