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Wireless sensor networks enable continuous and reliable data acquisition for real-time monitoring in a variety of application areas. Due to the large amount of data collected and the potential complexity of emergent patterns, scalable and distributed reasoning is preferable when compared to centralised inference as this allows network wide decisions to be reached robustly without specific reliance on particular network components. In this paper, the authors provide an overview of distributed inference for both wearable and ambient sensing with specific focus on graphical models-illustrating their ability to be mapped to the topology of a physical network.
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