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Pervasive computing environments such as smart spaces require a mechanism to easily integrate, manage and use numerous, heterogeneous sensors and actuators into the system. However, available sensor network platforms are inadequate for this task. The goals are requirements for a smart space are very different from the typical sensor network application. Specifically, we found that the manual integration of devices must be replaced by a scalable, plug-and-play mechanism. The space should be assembled programmatically by software developers, not hardwired by engineers and system integrators. This allows for cost-effective development, enables extensibility, and simplifies change management. We found that in a smart space, computation and power are readily available and connectivity is stable and rarely ad-hoc. Our deployment of a smart house (an assistive environment for seniors) guided us to designing Atlas, a new, commercially available service-oriented sensor and actuator platform that enables self-integrative, programmable pervasive spaces. We present the design and implementation of the Atlas hardware and middleware components, its salient characteristics, and several case studies of projects using Atlas.

Abstract. This paper defines a new, context-driven programming model for pervasive spaces. Existing models are prone to conflict, as it is hard to predict the outcome of interleaved actions from different services, or even to detect that a particular device is receiving conflicting instructions. Nor is there an easy way to identify unsafe contexts and the emergency remedy actions, or for programmers and users to grasp the complete status of the space. The programming model proposed here resolves these problems by improving coordination by explicitly defining the behaviors via context, and providing enhanced safety guarantees as well as a real-time, at-a-glance snapshot of the space's status. We present this model by first revisiting the definitions of the three basic entities (sensors, actuators and users) and then deriving at the definition of the operational semantics of a pervasive space and its context. A scenario is provided to demonstrate both how programmers use this model as well as the advantages of the model over other approaches.

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