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The emergence of wireless sensor networks (WSNs) in healthcare applications is gaining momentum through the increasing array of wearable vital sign sensors and location tags which can track both healthcare personnel and patient status/ location continuously in real-time mode. Despite the increased range of potential application frameworks -ranging from pre-hospital, in-hospital, ambulatory and home monitoring, to long-term database collection for longitudinal trend analysis -the security gap between existing WSN designs and the requirements of medical applications remains unresolved.Generally, WSN devices are extremely limited in terms of power, computation, and communication. They are often deployed in accessible areas, thus increasing security vulnerabilities. The dynamic ad hoc topology, multicast transmission, location awareness, critical data prioritisation, and co-ordination of diverse sensors of healthcare applications further exacerbate the security challenges. This paper presents an analysis of various WSN security mechanisms from the demanding perspective of healthcare applications, and considers the importance of security to the successful deployment of pervasive computing solutions in the healthcare industry.

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This paper proposes DD-Stream, a framework for density-based clustering stream data. The algorithm adopts a density decaying technique to capture the evolving data stream and extracts the boundary point of grid by the DCQ-means algorithm. Our method resolving the problem of evolving automatic clustering of real-time data streams, can not only find arbitrary shaped clusters with noise, but also avoid the clustering quality problems caused by discarding the boundary point of grid, our algorithm has better scalability in processing large-scale and highdimensional stream data as well.Second International Conference on Genetic and Evolutionary Computing 978-0-7695-3334-6/08 $25.00