Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.

A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-also directly usable by WSN developers-and then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.

Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment.

Sensor Networks are widely used in several application domains thanks to their data acquisition and data processing capabilities. They are well suited to a multitude of monitoring and surveillance applications and are often involved in mission-critical tasks, thus making security a primary concern. Many architectures and protocols have been proposed to address this issue, mainly based on cryptographic operations, but it still represents an open research area: such techniques in fact, to be effective, often require complex computations and a large amount of dedicated resources, which are not available on sensor platforms according to the existing technology. Nevertheless, if considering tiered sensor networks, where tiny motes coexist with more powerful nodes, it is possible to perform some complex and efficient security schemes by exploiting the different capabilities of such nodes. In this paper we present an secure architectural proposal of the Tenet system, a tiered re-taskable sensor network architecture. Specifically, we have integrated some security library into the Tenet architecture in order to implement a hybrid cryptosystem. The latter combines symmetric and asymmetric cryptographic schemes to benefit of the security provided by asymmetric protocols and the better performance of symmetric ones.

One of the main open issues in the development of applications for sensor network management is the definition of interoperability mechanisms among the several monitoring systems and heterogeneous data. Interesting researches related to integration techniques have taken place, they are primary based on the adoption of sharing data-mechanisms; furthermore in the last years, the Service-Oriented Architecture (SOA) approach has become predominant in many sensor network projects as it enables the cooperation and interoperability of different sensor platforms at an higher level of abstraction. In this paper we propose a novel architecture for the interoperability of sensor networks, which is based on Web services technologies and on the definition of a common data model enriched with semantic concepts and annotations. The proposed architecture allows the development of complex application by integration of heterogeneous data, accessible through services, according to standard data format and standard protocols.