Temporal Data Fusion at the Edge
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As an enabler technique, data fusion has gained great attention in the context of internet of things (IoT). In traditional settings, data fusion is done at the cloud server. So the data to be fused should be transferred from the sensor nodes to the cloud server prior to data fusion. Such an application mode of data fusion inherits disturbing concerns, e.g., privacy-leaking, large latency between data capture and computation, excessive ingress bandwidth consumption, from the cloud computing framework. We take into account how to do temporal data fusion at the edge to bypass the above issues. We present a Gaussian process based temporal data fusion (GPTDF) algorithm targeted for a problem of sequential online prediction at the edge. Our algorithm fits the edge computing framework and thus inherits desirable properties from edge computing, such as privacy-preserving, low latency between data capture and computation, and tiny bandwidth consumption. Through a real-data experiment, we also demonstrate that using GPTDF at the edge can lead to more timely and accurate predictions.
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