عليرضا نجف زاده

The use of UAVs has been one of the recent solutions of interest due to their inherent characteristics. The need for communication networks to have a stable connection in different conditions is an incentive for research in this field. On the other hand, UAVs, as a growing technology, can be deployed in various environments at the lowest cost and provide appropriate communication. The use of UAVs in environmental monitoring and management operations in natural or unnatural disaster conditions is one of the valuable applications of UAVs. Due to the inherent characteristics of the UAV, they can be easily deployed and used. Managing the mobility of UAVs for optimal energy efficiency of UAVs is one of the main objectives of this research. Managing the mobility and deployment of UAVs for monitoring and serving users is one of the most fundamental issues of the UAV network that is directly related to the energy consumption of UAVs. The need for a UAV operation, the lowest possible energy consumption of UAVs, and mobility management are fundamental issues. Therefore, mobility management can be considered the key to using UAVs in UAV operations. In this paper, an efficient energy efficiency model for positioning and mobility management of UAVs based on real-time demand and network conditions is presented adaptively. Traditional optimization methods often face challenges in handling the complex and dynamic nature of UAV deployment. To overcome these challenges, a novel algorithm, named JAYA algorithm, which combines a population-based optimization algorithm inspired by social behavior to solve mathematical optimization problems, and the K-means clustering technique, is proposed. The performance of the proposed model and the JAYA-based algorithm is also eva‎luated, demonstrating the effectiveness of mobility management in achieving the objectives of maximizing network coverage in an environment and minimizing the energy consumption of UAVs. The results show that this approach performs relatively better than other methods in terms of positioning and mobility accuracy, less power (energy) consumed by UAVs, packet loss rate, and latency. On the other hand, in reducing the energy consumption of UAVs, we have relatively witnessed improved consumption of UAVs in serving an increased number of users in different scenarios. This model also considers adaptation to different network conditions and scenarios, making it a valuable tool for improving and managing the mobility of UAVs in different environments.

شبكه پهپادي , شبكه سلولي , مديريت تحرك , جايابي پهپاد , بهره وري انرژي

UAV Network , Celluar Network , Mobility managment , Placement , Energy efficiency

Alireza Najafzadeh

Abolfazl Diyanat