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).

翻译：在动态且资源受限的环境中（例如多跳无线Mesh网络），传统路由协议往往因依赖预定路径而在不可预测的链路条件下失效。最短任意路径路由通过根据实时链路条件调整路由决策提供了一种解决方案。然而，此类路由的有效性根本上取决于可用链路的质量和可靠性，而准确预测这些变量颇具挑战性。本文提出一种新方法，利用多臂老虎机算法——探索与利用的确定性排序（DSEE），来满足对链路传输概率进行精确实时估计的需求。该方法增强了最短任意路径路由在链路条件波动下的可靠性和鲁棒性。通过将DSEE与任意路径路由结合，该算法持续学习并确保准确估计传输概率，同时选择最合适的路由路径高效传输数据包，并维持可证明的近似对数遗憾界。我们还在理论上证明，与先前提出的基于汤普森采样的机会路由（TSOR）相比，所提方案在网络规模维度上具有更优的遗憾缩放特性。