We introduce noisy beeping networks, where nodes have limited communication capabilities, namely, they can only emit energy or sense the channel for energy. Furthermore, imperfections may cause devices to malfunction with some fixed probability when sensing the channel, which amounts to deducing a noisy received transmission. Such noisy networks have implications for ultra-lightweight sensor networks and biological systems. We show how to compute tasks in a noise-resilient manner over noisy beeping networks of arbitrary structure. In particular, we transform any R-round algorithm that assumes a noiseless beeping network (of size n) into a noise-resilient version while incurring a multiplicative overhead of only O(log n + log R) in its round complexity, with high probability. We show that our coding is optimal for some (short) tasks, such as node-coloring of a clique. We further show how to simulate a large family of algorithms designed for distributed networks in the CONGEST(B) model over a noisy beeping network. The simulation succeeds with high probability and incurs an asymptotic multiplicative overhead of O(B · Δ · min(n, Δ2)) in the round complexity, where Δ is the maximum degree of the network. The overhead is tight for certain graphs, e.g., a clique. Further, this simulation implies a constant overhead coding for constant-degree networks.
|Title of host publication
|PODC 2020 - Proceedings of the 39th Symposium on Principles of Distributed Computing
|Association for Computing Machinery
|Number of pages
|Published - 31 Jul 2020
|39th Symposium on Principles of Distributed Computing, PODC 2020 - Virtual, Online, Italy
Duration: 3 Aug 2020 → 7 Aug 2020
|Proceedings of the Annual ACM Symposium on Principles of Distributed Computing
|39th Symposium on Principles of Distributed Computing, PODC 2020
|3/08/20 → 7/08/20
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© 2020 ACM.
- error-correction in networks