There has been great effort in developing ontologies for modeling sensor networks, describing various types of sensors and their context. However, when faced with a large scale deployment, the process of acquiring and managing semantic sensor metadata is challenging. This paper focuses on acquiring contextual metadata of sensors, such as location and surrounding environment, as opposed to technical metadata which can be derived from sensor’s firmware. More specifically, the paper proposes a framework for collecting contextual metadata information with help of the mobile devices, which allows usage on the deployment site and as such lowers the cost.
COBISS.SI-ID: 26481447
Motivated by the importance of metadata for WoT systems, we described building a metadata management system which is scalable and rich in semantics. We described two implementation approaches and discussed advantages and disadvantages of each: the embedded approach and the middleware approach. We also identified three components relevant to managing the metadata: the storage, the representation and the access. Based on our experience with implementation, we concluded that: (i) both the embedded and the middleware solutions can already be prototyped, but some critical technologies for the embedded approach were still in early development and required considerable improvements, (ii) XML like syntax is not well suited for storing and transmission of metadata due to sensor device constraints with respect to available storage and link datarate; and (iii) the middleware approach proved more convenient from the web application developer’s point of view compared to the embedded approach.
COBISS.SI-ID: 25921319
In the paper we present the generic pipeline for the analysis of sensor data that includes the concept of semantic sensor network. The pipeline is covering components from sensors (measuring diverse quantities), sensor boards (installed in smart city scenarios), communication lines delivering sensor data to the data center, data servers to enrich and store the data, real-time analytics (to aggregate and predict selected signals), and visualization (to deliver information to the user). The pipeline is installed in two smaller towns in Slovenia for testing and development purposes.
COBISS.SI-ID: 26503207