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The fields of wearable computing, augmented reality and ubiquitous computing are in principle highly convergent, as they all promise a Utopian future in which the devices embedded in the environment, our bodies and our clothes will have reached a level integration such that we can intuitively perceive and interact with our environment. However, the reality as practised in research labs and limited commercial deployments has been that budgetary and technical constraints have actually kept these fields separate and distinct. One manifestation of this separation is in the choice of sensors used to build systems in each domain. A truly cross-disciplinary project has to incorporate sensors of much greater heterogeneity than has occurred heretofore. The way in which sensors are deployed results in spatial seams that can act as obstacles to the provision of services across different areas. This paper takes an architectural approach to handling events from different tracking systems and maintaining a consistent spatial model of people and objects. The principal distinguishing feature is the automatic derivation of dataflow network of distributed sensors, dynamically and at run-time, based on requirements expressed by clients.