My Professional Journey

The MavLink protocol is a lightweight communication protocol used for communication between unmanned aerial vehicles (UAVs) and the ground control station (GCS). The contents of the MavLink payload might include sensitive information, including mission details and the geographical coordinates of the drone. Nonetheless, due to the lack of encryption support in the MavLink protocol, the payload can be readily obtained and modified by an attacker. This study introduces an enhanced protocol called MavLink Secure (MavSec) that provides built-in support for payload encryption. Furthermore, we have also incorporated the secure key exchange process. Then, our proposed protocol was tested with various encryption algorithms (AES, RC4, ChaCha20, PRESENT, RECTANGLE) implemented in C++. Next, we proceed to evaluate the performance metrics with peak memory usage and average delay time on two separate machines. The results of experiments indicate that ChaCha20 has better overall performance in comparison to other encryption algorithms. The integration of ChaCha20 with MavSec has resulted in enhanced levels of confidentiality, integrity, and authenticity compared to the unprotected MavLink protocol.

Placing a number of home surveillance cameras around the property can enhance home security. However, camera coverage and their true effectiveness can be limited due to the limited number of cameras that can be installed, camera's field of view, camera's fixed position, and associated privacy issues. Unmanned aerial vehicles (UAVs), commonly known as drones, are able to fly independently without any human intervention. There are already a few commercially available options for outdoor drone surveillance, but none for indoor applications. We believe the drones can be effectively deployed for home monitoring purposes in a cost-effective and privacy-preserving manner. In this paper, we developed a novel autonomous drone prototype that can offer economically viable effective smart home monitoring capabilities than currently available home monitoring solutions in today's smart home industry. While in flight, our developed drone navigation system can fly on any predefined paths, dynamically change the paths based on user requirements to inspect any place within its range and adapt to unanticipated situations, such as obstacle avoidance and low battery. In addition, the system can utilize machine learning to evaluate the camera stream from the onboard camera and perform object detection tasks and notify users accordingly. In our testing, we demonstrated that our developed prototype successfully performed all the functions mentioned above. Also, our novel findings from this paper shed light on some of the important parameters of indoor drone-based monitoring systems, which will contribute to the further advancement in drone-based home monitoring technology,