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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Energy Efficient Randomized Communication in Unknown AdHo...
Petra Berenbrink, Colin Cooper, Zengjian Hu · 2006-12-15 · via cs.DS updates on arXiv.org

This paper studies broadcasting and gossiping algorithms in random and general AdHoc networks. Our goal is not only to minimise the broadcasting and gossiping time, but also to minimise the energy consumption, which is measured in terms of the total number of messages (or transmissions) sent. We assume that the nodes of the network do not know the network, and that they can only send with a fixed power, meaning they can not adjust the areas sizes that their messages cover. We believe that under these circumstances the number of transmissions is a very good measure for the overall energy consumption. For random networks, we present a broadcasting algorithm where every node transmits at most once. We show that our algorithm broadcasts in $O(\log n)$ steps, w.h.p, where $n$ is the number of nodes. We then present a $O(d \log n)$ ($d$ is the expected degree) gossiping algorithm using $O(\log n)$ messages per node. For general networks with known diameter $D$, we present a randomised broadcasting algorithm with optimal broadcasting time $O(D \log (n/D) + \log^2 n)$ that uses an expected number of $O(\log^2 n / \log (n/D))$ transmissions per node. We also show a tradeoff result between the broadcasting time and the number of transmissions: we construct a network such that any oblivious algorithmusing a time-invariant distribution requires $Ω(\log^2 n / \log (n/D))$ messages per node in order to finish broadcasting in optimal time. This demonstrates the tightness of our upper bound. We also show that no oblivious algorithm can complete broadcasting w.h.p. using $o(\log n)$ messages per node.