# Temporal Data Fusion at the Edge

## TL;DR

This paper introduces GPTDF, a Gaussian process-based method for real-time temporal data fusion at the edge, enhancing privacy, reducing latency, and saving bandwidth in IoT applications.

## Contribution

It proposes a novel Gaussian process-based approach for temporal data fusion directly at the edge, addressing privacy, latency, and bandwidth issues in IoT systems.

## Key findings

- GPTDF improves prediction accuracy over traditional methods.
- It reduces latency and bandwidth consumption in edge computing.
- Real-data experiments show timely, accurate predictions at the network edge.

## Abstract

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 before data fusion. Such an application mode of data fusion inherits disturbing concerns from the cloud computing framework, e.g., privacy-leaking, large latency between data capture and computation, excessive ingress bandwidth consumption. 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) method targeted for the problem of sequential online prediction at the edge. The GPTDF method 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 using archived traffic datasets from the Caltrans Performance Measurement System (PeMS), we demonstrate that the application of GPTDF can provide more timely and accurate real-time predictions at the network edge.