We propose that a semantic service-oriented approach is one of the best techniques to cope with challenges in wireless sensor network (WSN) applications. This paper offers a framework for sensor network services that aims to improve query processing. We expect this framework will address current challenges and issues preventing the wider uptake of WSN technology. More specifically, we propose a semantic service-oriented framework with a focus on query processing to allow distributed end-users to request streams of interest easily and efficiently, based on the principle of pushing the query down to the network nodes as much as possible. As such, the lifetime and utility of the sensor network will be maximised, ultimately leading to the success of WSN deployments. The importance of semantics, which aims to support sensor capability modelling and query writing has been highlighted. On the other hand, query rewriting is emphasised followed by examples to illustrate that query rewriting can significantly contribute to the overall power efficiency of WSNs.

We describe a semantic data validation tool that is capable of observing incoming real-time sensor data and performing reasoning against a set of rules specific to the scientific domain to which the data belongs. Our software solution can produce a variety of different outcomes when a data anomaly or unexpected event is detected, ranging from simple flagging of data points, to data augmentation, to validation of proposed hypotheses that could explain the phenomenon. Hosted on the Jena Semantic Web Framework, the tool is completely domain-agnostic and is made domain-aware by reference to an ontology and Knowledge Base (KB) that together describe the key resources of the system being observed. The KB comprises ontologies for the sensor packages and for the domain; historical data from the network; concepts designed to guide discovery of internet resources unavailable in the local KB but relevant to reasoning about the anomaly; and a set of rules that represent domain expert knowledge of constraints on data from different kinds of instruments as well as rules that relate types of ecosystem events to properties of the ecosystem. We describe an instance of such a system that includes a sensor ontology, some rules describing coastal storm events and their consequences, and how we relate local data to external resources. We describe in some detail how a specific actual eventan unusually high chlorophyll readingcan be deduced by machine reasoning to be consistent with being caused by benthic diatom resuspension, consistent with being caused by an algal bloom, or both.