Enhanced Multi-Target Surveillance and Tracking Algorithm for Collaborative Drones in IoT Environment

In the realm of IoT-enabled surveillance systems, drones play a pivotal role in detecting and tracking suspects within defined areas. However, a significant challenge arises when a single drone identifies multiple suspects concurrently, leading to the inability to effectively track all targets simultaneously. To address this limitation, this paper introduces a collaborative algorithm designed to enhance multi-target surveillance and tracking in such environments. The algorithm fosters cooperation among drones by dynamically selecting suitable assisting drones to engage in the tracking process. When a drone identifies multiple suspects, it transmits relevant information, including positional data and imagery, to the base station. Subsequently, the algorithm orchestrates the assignment of new tracking missions to assisting drones, enabling them to effectively follow the additional targets. This paper elucidates the algorithm’s intricate workings, elucidates its integration within the IoT-based surveillance system architecture, and presents the empirical results of its implementation. Through experimental evaluation, we demonstrate the algorithm’s efficacy in improving tracking accuracy, mission success rate, and resource utilization. The proposed solution holds the potential to revolutionize multi-target tracking capabilities in IoT-driven surveillance scenarios, offering a tangible advancement in collaborative drone operations.