Efficient Deterministic Distributed Computing in Ad-Hoc Wireless Networks

We study the problem of distributed construction of efficient de-centralized communication schedules for ad-hoc wireless networks. We consider a model which is close to real scenarios: (1) the SINR interference model, which covers most important distinctive features of contemporary wireless communic...

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Veröffentlicht in:Proceedings of the International Conference on Distributed Computing Systems S. 264 - 274
Hauptverfasser: Jurdzinski, Tomasz, Kowalski, Dariusz R.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 21.07.2025
Schlagworte:
Ad hoc networks
Ad-hoc network
Aggregation and broadcast schedules
De-centralized schedules
Deterministic algorithms
Distributed algoriths
Distributed computing
Extraterrestrial measurements
Fading channels
Interference
Lower bound
Probabilistic logic
Schedules
Signal to noise ratio
SINR interference model
Wireless network
Wireless networks
ISSN:2575-8411
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Zusammenfassung:We study the problem of distributed construction of efficient de-centralized communication schedules for ad-hoc wireless networks. We consider a model which is close to real scenarios: (1) the SINR interference model, which covers most important distinctive features of contemporary wireless communication, such as signal fading, collisions and accumulation of signal, and (2) any underlying metric of bounded growth, which includes Euclidean space with certain type of obstacles. Most of efficient solutions in the SINR model rely on probabilistic algorithms, assuming access of nodes to the sources of independent truly random bits, which in practice is hard to get by wireless devices.In this paper we show that key efficient de-centralized communication abstraction primitives, such as aggregation and broadcast schedules (which are bases of many other communication tasks), can be efficiently built by distributed algorithms in SINR networks without using any randomization. In particular, the length of the built communication schedules are asymptotically as efficient as those significantly relying on randomization, and close to the theoretic lower bounds. Importantly, the time (round) complexity of our solutions grows only logarithmically with respect to the growth of the density of a network, which makes them scalable in real-life scenarios.
ISSN:2575-8411
DOI:10.1109/ICDCS63083.2025.00034