Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration parameters affect network key performance indicators (KPIs). Traditional performance evaluation methods can evaluate KPIs based on configuration parameters, but they are unable to explain how configuration parameters affect KPIs. Moreover, static causal discovery and inference methods are not directly applicable to dynamic networks. To address these challenges, we propose a self-iterative performance optimization method based on causal inference and interpretable network evaluation (CiiNet). CiiNet constructs causal graphs through change-point detection and hierarchical incremental causal discovery. Then, CiiNet introduces causal inference for critical parameter analysis (CPA). Using intervention analysis and regression-based parameter learning, CiiNet infers and evaluates the impact of critical parameters on KPIs. Based on the interpretable evaluation, CiiNet can further self-iteratively optimize the critical parameters for optimal network performance and dynamically obtain the optimal configurations. Our extensive experiments show that CiiNet outperforms other baseline methods regarding causal discovery, network performance evaluation, and CPA.

A lot of the devices in the Internet of Things are sensors responsible for capturing environmental information and relaying it to a system or network. Oftentimes, it is important to know the location of these sensors to better contextualize the information received from them. However, the sensors are purposely simple and cheap, meaning that conventional localization techniques, such as global navigation satellite systems are not feasible. Research has been done on using unmanned aerial vehicles to estimate the location of the sensors, but issues with signal strength fluctuation and location approximation because of it are still prevalent. In this work, we propose a new method for estimating the location of sensors by exploiting the characteristics of radio wave signal propagation and creating a new flight path design, a solution to minimize the impact of variation in measurements, a novel candidate point generation through signal strength analysis, and a method to find the location based on the candidates. Through a thought-out experiment, we show that the proposed algorithm is overall significantly better than the existing solution when it comes to identifying the location of outdoor sensors.