Wireless Sensor Networks (WSNs) are attracting active and increasing research interest in various application fields including industrial control and environmental applications. In such cases accurate event detection is of utmost importance, thus sensors' data need to be fused through a sophisticated process (i.e. data mining algorithms). In this context, semantic correlations between sensor nodes and formation of semantic clusters is critical as it enables the fusion of specific sensor data regardless of the proximity criteria. Traditional clustering schemes aim to extend sensors' network lifetime using criteria such as received signal strength, while the semantic correlation is frequently omitted. In this paper, two novel techniques for discovering semantic neighbors are proposed, Diffusion Algorithm for Discover Semantic Neighbors (DADSN) and Trace Route Algorithm for Discover Semantic Neighbors (TRA-DSN). Design and development efforts are analysed while the evaluation results offer a useful guideline on which technique fits better in different WSNs deployments.
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