ABSTRACT
Unmanned drone vehicle (UDV) communication has emerged as a quick fix for inaccessible regions such as hazardous operation, rescue, and search attributed to its adaptability and accessibility. Prior research in this area concentrated on UDV positioning and route scheduling; however, these strategies have been limited by adverse weather conditions in the environment. Fifth-generation (5G) networks are being developed with the goal of offering promising services and applications with higher data rates as well as lower latency. Thus, in the 5G era, it is critical to effectively manage and schedule physical resources. The significance of climate-based positioning and route scheduling of UDVs (ICROS-UDV) on reliability, QoS, and energy efficiency in UDV communications is investigated in this chapter. To facilitate effective communication, climate conditions are predicted by employing Analytical Short Long-Term Memory (A-SLTM) based on historical data. The route scheduling to conduct efficient data gathering is regarded as a multi-faceted optimal control concern and is carried out employing the Intelligent Mayfly-Based Optimization (IMO) method. As an outcome, the proposed approach can improve QoS, efficacy, and reliability and safety in UDV-based communication. The mentioned ICROS-UDV method is examined and evaluated using performance metrics like coverage ratio, latency, route obtained, number of gathered packets, UDV transmission rate, and consumption of energy. The obtained findings are summarized in a way that demonstrates the effectiveness of the proposed method.
