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

TL;DR

This paper presents a novel DSEE-based approach to enhance Shortest Anypath routing in wireless mesh networks by providing accurate link estimation, improving reliability and resilience under dynamic link conditions.

Contribution

It introduces a DSEE-based link estimation method integrated with Anypath routing, offering provably better regret bounds than existing Thompson Sampling approaches.

Findings

Achieves near-logarithmic regret in link estimation.

Improves routing reliability in dynamic environments.

Outperforms previous TSOR methods in theoretical regret bounds.

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).