Today’s wireless cyber-physical systems focus on achieving reliable data transfer over a lossy medium at the expense of latency. However, sensor data are often noisy and thus only lossily characterise real-word phenomena, rendering their exact transfer wasteful. Furthermore, many next-generation privacy-sensitive applications, such as smart grid control, real-time distributed object tracking, and inter-vehicle federated learning face latency and traffic bottlenecks due to the sheer amount of data collection required to overcome noise. We tackle this problem by introducing Twofer, a communication approach which reduces latency and traffic in high-noise or high-privacy settings by abandoning the focus on reliable networking. Twofer empowers developers to tune networks for latency-bound rather than reliability-bound performance; the system coordinates ambiguous transmissions, which are used to estimate the network-wide distribution of data, rather than to reliably communicate exact data from individual nodes. Twofer’s full-stack design maintains black-box compatibility with existing application code, but advocates for, and shows the value of, uncommon physical-layer features such as symbol-synchronous transmission. The system is therefore implemented and evaluated on a prototype low-latency wireless mesh network called Zero-Wire. Experiments using state-of-the-art local differential privacy protocols show 25–75% latency reductions relative to conventional approaches. The results are also future-proof, with performance advantages increasing with the strength of the privacy guarantees that are offered.

 

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