Power Efficient Long Range Drone Networking System for UAV Detection

In recent years, technological advancements in Unmanned Aerial Vehicles (UAV) have removed barriers to entry on drone usage for the public. While this has led to the growth of the drone industry it has also provided terrorists with easier access to drones. Rising threats of malicious drones have raised the importance of a Counter Drone System (CDS). Traditional CDSs use a variety of physical sensors and technologies to detect hostile drones and react efficiently, however, these methods have their own set of constraints, such as high power consumption and short operational range. This paper proposes a power-efficient long range CDS composed of a LoRa mesh network and on-device Machine Learning (ML). LoRa provides low-power and long range communication and the multi-hop feature of the mesh network expands the coverage of the network. Applying Tiny Machine Learning (TinyML) for detecting drone on the Micro-Controller unit is reasonable to design long range communication. On device Machine Learning has no round trip between server and device. The implemented system achieves a satisfactory communication range, a successful operation of the UAV detection and a power consumption which is similar to that of typical MCU.