A unified framework for joint mobility prediction and object profiling of drones in UAV networks

Abstract

In recent years, using a network of autonomous and cooperative unmanned aerial vehicles (UAVs) without command and communication from the ground station has become more imperative, in particular in search-and-rescue operations, disaster management, and other applications where human intervention is limited. In such scenarios, UAVs can make more efficient decisions if they acquire more information about the mobility, sensing and actuation capabilities of their neighbor nodes. In this paper, we develop an unsupervised online learning algorithm for joint mobility prediction and object profiling of UAVs to facilitate control and communication protocols. The proposed method not only predicts the future locations of the surrounding flying objects, but also classifies them into different groups with similar levels of maneuverability (e.g. rotatory, and fixed-wing UAVs) without prior knowledge about these classes. This method is flexible in admitting new object types with unknown mobility profiles, thereby applicable to emerging flying Ad-hoc networks with heterogeneous nodes.

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

“…Further alternatives for UAV path planning have been proposed in the open literature. In [31] an unsupervised solution has been proposed to enable motion prediction for a group of heterogeneous flying UAVs. In addition to predicting the future locations of the UAVs, the algorithm is designed to classify the network nodes based on their motion properties.…”

A Survey on Machine-Learning Techniques for UAV-Based Communications

“…In Reference [115], an unsupervised online self-tuning learning algorithm for joint mobility prediction and object profiling of the individual UAVs was proposed. Apart from predicting the flying objects’ future locations without requiring prior knowledge of the mobility profiles or trajectories of the UAVs, the proposed method also enables the classification of the UAVs into particular groups based on their motion properties, e.g., rotatory and fixed-wing UAVs, via an hierarchical generative model.…”

UAVouch: A Secure Identity and Location Validation Scheme for UAV-Networks

“…This could, for example, be achieved through a combination of complex trajectories and specific physical dynamics of the drones. The complexity of the movement pattern is arbitrary, but the more complex it is, the harder it will be for an attacker to predict, even one employing advanced learning mechanisms, such as [39].…”