Uncertainty in dense heterogeneous IoT sensor networks can be decreased by applying reputation-inspired algorithms, such as the EWMA (Exponentially Weighted Moving Average) algorithm, which is widely used in social networks. Despite its popularity, the eventual convergence of this algorithm for the purpose of IoT networks has not been widely studied, and results of simulations are often taken in lieu of the more rigorous proof. Therefore the question remains, whether under stable conditions, in realistic situations found in IoT networks, this algorithm indeed converges. This paper demonstrates proof of the eventual convergence of the EWMA algorithm. The proof consists of two steps: it models the sensor network as the UOG (Uniform Opinion Graph) that enables the analytical approach to the problem, and then offers the mathematical proof of eventual convergence, using formalizations identified in the previous step. The paper demonstrates that the EWMA algorithm converges under all realistic conditions.

中文翻译：

---

物联网传感器网络中基于声誉的算法的最终收敛

通过应用受声誉启发的算法，例如广泛用于社交网络的 EWMA（指数加权移动平均）算法，可以降低密集异构物联网传感器网络中的不确定性。尽管它很受欢迎，但该算法用于物联网网络的最终收敛性尚未得到广泛研究，并且经常采用模拟结果来代替更严格的证明。因此，问题仍然存在，在稳定条件下，在物联网网络中发现的现实情况下，该算法是否确实收敛。本文演示了 EWMA 算法最终收敛的证明。证明包括两个步骤：将传感器网络建模为 UOG（统一意见图），使分析方法能够解决问题，然后使用上一步中确定的形式化提供最终收敛的数学证明。论文证明了 EWMA 算法在所有现实条件下都收敛。