Blockchain and UAV-Enabled Signal Source Identification with Edge Computing and Wireless Signal-Aerial Image Fusion

This paper proposes a novel signal source identification system composed of unmanned aerial vehicles (UAVs) and a blockchain, in which the identification method makes full use of binocular camera data and received signal strength. The UAV tasks are organized by using blockchain technology and smart contracts. To tackle the challenge that the transmit power of the object and the channel path loss coefficient are unknown to the UAV, a maximum likelihood estimation method is developed to estimate the parameters in the path loss log-normal shadowing model. Then, the mean squared error is used as the metric to distinguish the signalling object. The simulation results show that the proposed method can effectively complete the task. Also, a mobile edge computing- (MEC-) enabled UAV testbed system is designed and implemented in real environment. The system works accurately where the number of candidate objects is 3.