Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking

TL;DR

The paper tackles the challenge of routing in dynamic, multi-hop wireless mesh networks where fixed paths fail due to fluctuating link quality. It introduces DSEE-Anypath, a method that integrates Deterministic Sequencing of Exploration and Exploitation (DSEE) with Shortest Anypath routing to learn per-link delivery probabilities online and adapt forwarding sets accordingly. The approach yields a provable near-logarithmic regret bound, with $\mathcal{R}_T = O(N^2 N_{max} \log^2 T)$, and demonstrates favorable scaling compared to TSOR. Empirical results corroborate the theory, showing near-genie performance in regret and improved robustness to changing link conditions, with potential extensions to dependent paths and decentralized deployment. The work advances reliable wireless routing by marrying online learning of link metrics to a classical anypath routing framework, enabling real-time, data-driven routing decisions in uncertain environments.

Abstract

In dynamic and resource-constrained environments, such as multi-hop wireless mesh networks, traditional routing protocols often falter by relying on predetermined paths that prove ineffective in unpredictable link conditions. Shortest Anypath routing offers a solution by adapting routing decisions based on real-time link conditions. However, the effectiveness of such routing is fundamentally dependent on the quality and reliability of the available links, and predicting these variables with certainty is challenging. This paper introduces a novel approach that leverages the Deterministic Sequencing of Exploration and Exploitation (DSEE), a multi-armed bandit algorithm, to address the need for accurate and real-time estimation of link delivery probabilities. This approach augments the reliability and resilience of the Shortest Anypath routing in the face of fluctuating link conditions. By coupling DSEE with Anypath routing, this algorithm continuously learns and ensures accurate delivery probability estimation and selects the most suitable way to efficiently route packets while maintaining a provable near-logarithmic regret bound. We also theoretically prove that our proposed scheme offers better regret scaling with respect to the network size than the previously proposed Thompson Sampling-based Opportunistic Routing (TSOR).

Figures (3)

• Figure 1: An anypath connecting nodes $1$ and $7$ is shown in double arrows. Every packet sent from $1$ traverses one of these paths to reach $7$. The numbers on links are delivery success probabilities; in this network, the links are assumed to be independent of each other.
• Figure 2: Regret Over Time
• Figure 3: Time-Averaged Regret Over Time