Deterministic Digital Clustering of Wireless Ad Hoc Networks

• Published in: arXiv.org 2018-01
• Main Authors: Jurdzinski, Tomasz, Kowalski, Dariusz R, Rozanski, Michal, Stachowiak, Grzegorz
• Format: Article
• Language: English
• Subjects: Ad hoc networks; Algorithms; Broadcasting; Clustering; Combinatorial analysis; Lower bounds; Polynomials; Power control; Randomization; Selectors; Wireless communications; Wireless networks
• ISSN: 2331-8422

We consider deterministic distributed communication in wireless ad hoc networks of identical weak devices under the SINR model without predefined infrastructure. Most algorithmic results in this model rely on various additional features or capabilities, e.g., randomization, access to geographic coordinates, power control, carrier sensing with various precision of measurements, and/or interference cancellation. We study a pure scenario, when no such properties are available. As a general tool, we develop a deterministic distributed clustering algorithm. Our solution relies on a new type of combinatorial structures (selectors), which might be of independent interest. Using the clustering, we develop a deterministic distributed local broadcast algorithm accomplishing this task in \(O(\Delta \log^*N \log N)\) rounds, where \(\Delta\) is the density of the network. To the best of our knowledge, this is the first solution in pure scenario which is only polylog\((n)\) away from the universal lower bound \(\Omega(\Delta)\), valid also for scenarios with randomization and other features. Therefore, none of these features substantially helps in performing the local broadcast task. Using clustering, we also build a deterministic global broadcast algorithm that terminates within \(O(D(\Delta + \log^* N) \log N)\) rounds, where \(D\) is the diameter of the network. This result is complemented by a lower bound \(\Omega(D \Delta^{1-1/\alpha})\), where \(\alpha > 2\) is the path-loss parameter of the environment. This lower bound shows that randomization or knowledge of own location substantially help (by a factor polynomial in \(\Delta\)) in the global broadcast. Therefore, unlike in the case of local broadcast, some additional model features may help in global broadcast.