Federated learning for enhanced sensor reliability of automated wireless networks

• Main Author: Basnayake Mudiyanselage, V. (Vishaka)
• Format: Dissertation
• Language: English
• Published: University of Oulu 2019
• Online Access: http://jultika.oulu.fi/Record/nbnfioulu-201908142761

Abstract. Autonomous mobile robots working in-proximity humans and objects are becoming frequent and thus, avoiding collisions becomes important to increase the safety of the working environment. This thesis develops a mechanism to improve the reliability of sensor measurements in a mobile robot network taking into the account of inter-robot communication and costs of faulty sensor replacements. In this view, first, we develop a sensor fault prediction method utilizing sensor characteristics. Then, network-wide cost capturing sensor replacements and wireless communication is minimized subject to a sensor measurement reliability constraint. Tools from convex optimization are used to develop an algorithm that yields the optimal sensor selection and wireless information communication policy for aforementioned problem. Under the absence of prior knowledge on sensor characteristics, we utilize observations of sensor failures to estimate their characteristics in a distributed manner using federated learning. Finally, extensive simulations are carried out to highlight the performance of the proposed mechanism compared to several state-of-the-art methods.