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.

Wireless sensor networks(WSNs) have been increasingly available for large-scale applications in which energy efficiency is an important performance measure. These applications include environmental monitoring and structure monitoring which demand multifarious data. Driven by the energy limitation nature of WSNs lots of research works have been done in aspects such as nodes deployment, routing protocol, topology control, data reduction, sleep scheduling, etc. However, heterogeneous, i.e. hybrid sensor nodes are combined together into semantic sensor networks to provide large-scale applications with content rich information. In this paper, we discuss the potential of energy efficiency that semantization could bring to sensor networks. First we have an overview of some related work and then address current approaches of energy conservation in WSNs as well as how semantization can contribute in each aspect of saving energy. Finally a recommendatory architecture of semantic sensor network is proposed. Semantization will be a promising solution to improve energy efficiency together with system performance.

Wireless sensor networks (WSN) generate large volumes of raw data which possess natural heterogeneity. WSNs are normally application specific with no sharing or reusability of sensor data among applications. In order for applications and services to be developed independently of particular WSNs, sensor data need to be enriched with semantic information. In this paper, we propose a semantic Web architecture for sensor networks (SWASN). This information oriented architecture allows the sensor data to be understood and processed in a meaningful way by a variety of applications with different purposes. We develop ontologies for sensor data and use the Jena API for processing which includes querying and inference over sensor data. By studying a building fire emergency scenario, we show that semantic Web technologies can provide high level information extraction and inference of sensor data.

In this paper we present a proposal that combines the benefits of autonomic and semantic sensor networks to build a semantic middleware for autonomic wireless sensor networks. The key feature of the proposed middleware is a rule-based reasoning engine based on ontology and fuzzy logic. We also propose a semantic-aware topology control based on computing semantic neighborhoods relationships. The middleware was tailored to provide support for Structural Health Monitoring applications. However, it has a flexible architecture and it can be extensible to several other application domains such as ambient intelligence, habitat monitoring and fire detection. We use the oil platform structural health monitoring domain as a case study. The paper presents the middleware architecture and the proposed ontologies.