# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Anti-UAV in IoT Environments
#### 2.5.2 Anti-UAV with Edge Computing  
Edge computing is used to store and process data on edge devices. It has the characteristics of fast data processing and analysis speed and strong real-time performance. However, edge devices have limited computing power, so they can only perform lightweight operations. Currently, edge computing's application in UAV detection and tracking primarily revolves around sensor data fusion. This approach effectively reduces data storage and bandwidth requirements while enhancing latency and response time. An example of a commercially available multi-sensor fusion networking device is Droneshield's SmartHub Mk2. Furthermore, the literature has explored lightweight deep network models that enable quick and accurate UAV detection and tracking within the constraints of limited computational resources. These studies shed light on the potential of leveraging edge computing for UAV detection and tracking. [PERSON] et al. [34] introduces a cost-effective early warning system for UAV detection and classification. The system is composed of a BladeRF software-defined radio (SDR), a wideband antenna, and a Raspberry Pi 4, which together form an edge node. Remarkably, this setup is designed to be affordable, with a total cost of under USD 540. This produced overall accuracy for a two-class detection system at 100% and 90.9% for UAV type classification on the UAVs tested. The inference times for two-class detection in this system range from 15 to 28 s, while for the six-class UAV type classification system, the inference times range from 18 to 28 s. RF-UAVNet [83] is a lightweight convolutional neural network based on RF. Its grouped convolution layer can significantly reduce network size and computing cost; multi-level skip connections and multi-gap mechanisms can effectively improve accuracy. Notably, it achieves remarkable performance with an accuracy of approximately 99.9% for UAV detection, 98.6% for UAV classification, and 95.3% for operation recognition. What sets RF-UAVNet apart is its low complexity, boasting a mere 11,000 parameters. TIB-Net [84] introduces a cyclic pathway in the iterative backbone to keep the model size lightweight while utilizing low-level feature information, and the integrated spatial attention module further improves the performance. TIB-Net stands out not only for its compact size, but also for its efficiency. With a model size of less than 700 Kb and a remarkably low number of parameters at 0.1 million, TIB-Net demonstrates its ability to achieve notable results (approximately 89.2% for UAV detection) while maintaining a lightweight structure. In addition, there are other lightweight models available, such as the visual-based MOB-YOLO [85], which can be deployed on edge nodes. However, it is worth noting that the accuracy of MOB-YOLO is relatively lower at 49.62%. Nonetheless, it is reasonable to assume that this model can achieve higher accuracy if trained on a dedicated UAV dataset. It is essential to consider further research and training to potentially enhance the accuracy of MOF-YOLO for improved performance in UAV detection and classification tasks. According to our observation, the development of these lightweight models mainly considers taking certain measures to reduce the size and calculation amount of the model while retaining low-level information as much as possible, and using some mechanisms to improve accuracy.  
In the future, city-wide anti-UAV systems are poised to become integral to the development of smart cities. However, the vast amount of surveillance data generated in the city's airspace poses a significant challenge for a centralized computing center. To address this challenge, deploying detection models directly on edge devices and utilizing edge computing for UAV detection on these devices, can greatly reduce data transmission requirements and expedite UAV detection processes.