A large number of vehicles routinely navigate through city streets; with on-board sensors, they can be transformed into a dynamic network that monitors the urban environment comprehensively and efficiently. In this paper, drive-by approaches are discussed as a form of mobile sensing, that offer a number of advantages over more traditional sensing approaches. It is shown that the physical properties of the urban environment that can be captured using drive-by sensing include Ambient Fluid, Electromagnetic, Urban Envelope, Photonic, and Acoustic properties, which comprise the FEELS classification. In addition, the spatiotemporal variations of these phenomena are discussed as well as their implications on discrete-time sampling. The mobility patterns of sensor-hosting vehicles play a major role in drive-by sensing. Vehicles with scheduled trajectories, e.g., buses, and those with less predictable mobility patterns, e.g., taxis, are investigated for sensing efficacy in terms of spatial and temporal coverage. City Scanner is a drive-by approach with a modular sensing architecture, which enables cost-effective mass data acquisition on a multitude of city features. The City Scanner framework follows a centralized IoT regime to generate a near real-time visualization of sensed data. The sensing platform was mounted on top of garbage trucks and collected drive-by data for eight months in Cambridge, MA, USA. Acquired data were streamed to the cloud for processing and subsequent analyses. Based on a real-world application, we discuss and show the potential of using drive-by approaches to collect environmental data in urban areas using a variety of non-dedicated land vehicles to optimize data collection in terms of spatiotemporal coverage.

Significance: Attaching sensors to crowd-sourced vehicles could provide a cheap and accurate way to monitor air pollution, road quality, and other aspects of a city’s health. But in order for so-called drive-by sensing to be practically useful, the sensor-equipped vehicle fleet needs to have large “sensing power”—that is, it needs to cover a large fraction of a city’s area during a given reference period. Here, we provide an analytic description of the sensing power of taxi fleets, which agrees with empirical data from nine major cities. Our results show taxis’ sensing power is unexpectedly large—in Manhattan; just 10 random taxis cover one-third of street segments daily, which certifies that drive-by sensing can be readily implemented in the real world.

Cloud computing introduces a fundamental shift in service delivery. A market registry, flexibility, exchangeability and integration of services are important issues for its success. In this work we introduce a unified Cloud and business service ontology with querying capabilities. Our framework addresses two aspects: on the one hand it gives answer to leading question how we can structure the term Cloud computing (as a question of basic research) and how we can enable a matching between offered Cloud services and demand. It closes the gap how we can summarize and describe Cloud services in a standardized way. On the other hand, it particularly addresses the demand for flexibility and exchangeability by the Cloud Computing paradigm and can serves as a repository of services.

The integration of heterogeneous data sources with even heterogeneous semantic meanings poses a challenge for data and system integrators. Ontology Alignment (OA) tries to identify similarities between heterogeneous ontologies and to automatically create suitable mappings for transformation. However, the usage of standard OA approach for safety-critical domains needs further investigation.In this paper, we describe a semi-automated ontology alignment approach (SAMOA) well-suitable for integration scenarios of safety-critical applications. The major contribution of our approach is the modeling differentiation between individual system knowledge and generic domain-specific knowledge.We evaluate our approach by providing a typical use case example from the Air Traffic Management (ATM) domain. In addition we analyze to what extent the SAMOA approach can be supported by state-of-the-art OA approaches.

Recently, portable sensors, with high accuracy and embedded communication technologies, have become available and affordable. By deploying such sensors on various urban vehicles that routinely navigate through city streets, vehicles can form a dynamic network for comprehensively and efficiently monitoring the urban environment. This drive-by sensing approach benefits also from the lower costs of sensor deployment and maintenance compared to stationary sensor networks. However, the data sampling frequency and spatial granularity of measurements are constrained by factors such as topology of the underlying street network and mobility pattern of sensor-equipped vehicles. In this paper we investigate the effect of street network topology on the quality of data captured through drive-by sensing. To this end, we first study the temporal aspects of drive-by sensing and present a quantitative method for comparing various street networks. Then, we consider the spatial aspects of drive-by sensing by defining a sensing-potential indicator for urban areas based on the geometrical properties of the street networks. This indicator is then combined with vehicle mobility patterns derived to measure the sensing potential of routes and cycles. In this context, we define the novel concept of Sensogram for describing the spatial sensing potential of network cycles using dedicated vehicles.

Drive-by sensing has the potential to provide hyper-local data to study a number of urban phenomena and deliver actionable insights for public good. Yet drive-by deployments are often characterized by small fleets, huge costs and lack of flexibility to adapt to a city's multiple sensing needs. The City Scanner project aims to accelerate the development of drive-by sensing by turning everyday vehicles into sensing nodes. In this paper we present Greta II, a solar-powered sensing platform that allows for low-cost continuous data collection and streaming of multiple phenomena. Thanks to a modular design it can be customized with off-the-shelf sensors and re-used across different deployments. Greta II can be easily built and deployed without disrupting a vehicle's normal operation. The platform has been validated during test deployments in two large American cities to collect air quality data. Drawing on our experience building and deploying Greta II we discuss challenges to be solved for enabling large-scale drive-by sensing.