The field of location-based services and structural health monitoring is moving towards developing more robust and resilient systems. Researchers are focusing on improving the security and integrity of location-based services, particularly in the face of low-cost attacks that can manipulate position data. Additionally, there is a growing interest in using machine learning and deep learning techniques to improve the accuracy and efficiency of structural health monitoring systems. These techniques are being used to develop more effective methods for damage detection, digital twinning, and anomaly detection. Notably, the use of transfer learning, active learning, and domain adaptation is becoming increasingly popular in this field.

Some noteworthy papers in this area include: The paper on coordinated position falsification attacks and countermeasures, which proposes a countermeasure to detect and thwart such attacks by utilizing readily available, redundant positioning information. The paper on active transfer learning for structural health monitoring, which proposes a Bayesian framework for domain adaptation that can improve unsupervised domain adaptation mappings using a limited quantity of labelled target data.