IoT Network Dynamic Clustering and Communication for Surveillance UAV's

Abstract

The work presented in this Ph.D. thesis addresses the challenges faced by drones in the Internet of Things (IoT) environment,with a particularfocus on the development of communication protocols in a dynamic fog computing (DFC) system. As mobile devices, drones face many challenges, including loss of connectivity, data redundancy due to multiple sensors, and the complexity of collaborative missions. These challenges have the potential to reduce mission effectiveness, increase costs and lead to mission failure and equipment loss. In order to address these concerns, we would like to suggest three communication and monitoring protocols that can enhance the efficiency of drone operations. Additionally, we propose a DFC overload mechanism. The first protocol introduces a new drone recovery system that addresses the limitations of existing systems that only recover drones after a crash. In order to strengthen security, a new authentication mechanism restricts device connections to fog nodes, preventing unauthorized access. The protocol consists of two phases, during the initial phase, the system detects connection attempts and establishes a link through an intermediary. The second phase involves the deployment of additional drones to recover the lost drone using an ad-hoc network. The second protocol addresses the issue of data redundancy in drone data collection within a collaborative drone system. A novel data acquisition algorithm has been developed that divides the fog region into smaller sections using mathematical definitions. The algorithm takes into account data collection factors and employs a shot-by-shot capture approach to minimize redundancy, optimize bandwidth utilization, and reduce network congestion. Collaboration with other drones is crucial for detecting multiple suspects effectively during a mission. Our research has identified a gap in existing collaborative systems regarding the detection of multiple suspects with a single drone. However, our third protocol significantly enhances the effectiveness of drone missions by facilitating cooperation between drones and selecting appropriate assisting drones for the tracking process. This protocol has been thoroughly tested and has proven to be successful in improving the accuracy and efficiency of drone missions. In situations where a drone detects multiple suspects, pertinent information is transmitted to the base station. The base station then coordinates tracking missions with another drone to pursue additional targets, while also taking into account the potential impact on other operations. Additionally, a new (DFC)-based mechanism is proposed to intelligently manage fog node states during saturation. Through a lightweight transition protocol, DFC dynamically monitors fog node capacity and adjusts their states, enabling real-time decision-making to address challenges posed by diverse traffic scenarios. This approach showcases competence and expertise in addressing complex traffic scenarios. The i results obtained demonstrate the effectiveness of proposed methods in terms of communication, computing latency and resource utilization compared to the conventional systems.