Optimal Message-Passing with Noisy Beeps

Davies, Peter

Abstract

Beeping models are models for networks of weak devices, such as sensor networks or biological networks. In these networks, nodes are allowed to communicate only via emitting beeps: unary pulses of energy. Listening nodes only the capability of carrier sensing: they can only distinguish between the presence or absence of a beep, but receive no other information. The noisy beeping model further assumes listening nodes may be disrupted by random noise. Despite this extremely restrictive communication model, it transpires that complex distributed tasks can still be performed by such networks. In this paper we provide an optimal procedure for simulating general message passing in the beeping and noisy beeping models. We show that a round of Broadcast CONGEST can be simulated in O(Δ log n) round of the noisy (or noiseless) beeping model, and a round of CONGEST can be simulated in O(Δ2 log n) rounds (where Δ is the maximum degree of the network). We also prove lower bounds demonstrating that no simulation can use asymptotically fewer rounds. This allows a host of graph algorithms to be efficiently implemented in beeping models. As an example, we present an O(log n)-round Broadcast CONGEST algorithm for maximal matching, which, when simulated using our method, immediately implies a near-optimal O(Δ log2 n)-round maximal matching algorithm in the noisy beeping model.

Citation

Davies, P. (2023, June). Optimal Message-Passing with Noisy Beeps. Presented at PODC 2023: ACM Symposium on Principles of Distributed Computing, Orlando, Florida

• Presentation Conference Type: Conference Paper (published)
• Conference Name: PODC 2023: ACM Symposium on Principles of Distributed Computing
• Start Date: Jun 19, 2023
• End Date: Jun 23, 2023
• Acceptance Date: Mar 26, 2023
• Online Publication Date: Jun 16, 2023
• Publication Date: 2023
• Deposit Date: Apr 3, 2023
• Publicly Available Date: Jun 23, 2023
• Pages: 300-309
• DOI: https://doi.org/10.1145/3583668.3594594
• Public URL: https://durham-repository.worktribe.com/output/1134969