One-Take: Gathering Distributed Sensor Data Through Dominant Symbols for Fast Classification
Jonathan Oostvogels, Sam Michiels, Danny Hughes · 2022
Sensor networks gather data at one or more gateway devices by sequentially receiving data frames from individual sensor nodes. In cases where applications rely on data that is distributed across sev-eral nodes, the latency of such node-by-node data collection scales with the number of nodes and the frame sizes involved, impeding fast decision-making. This paper seeks to overcome that limitation for latency-sensitive classification problems by introducing One-Take, a cross-layer aggregation protocol that aims to decouple the process of data gathering from network size and frame length. The protocol leverages dominance properties between concurrently transmitted symbols to convey socalled collages, messages that de-scribe relationships between data held by several nodes, rather than local information from any single node. Collages thus transmit a hash of distributed data that is computed within the physical layer, thereby communicating more informative features than regular frames of the same length. Experiments on a recent wireless mesh network platform, called Zero-wire, demonstrate that two-byte collages are delivered correctly 99% of the time and, using public sensor network data sets, show that One-take achieves a latency reduction of circa 42% relative to node-by-node transmissions.