Biblio

Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named “FengHuoLun”. Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.

In this work, a bi-directional transceiver with a maximum throughput of 24 kbps is presented. The spatio-temporal shallow water channel characteristics between a projector and a hydrophone array are analyzed in a seawater tank, and a methodology to maintain a 10−4 probability of bit error with prior knowledge of the channel statistics is proposed. Also, it is found that flow generated in the sea water provides a realistic representation of time-varying propagation conditions, particularly for the reverse link communication link at 22.5 kHz.