With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face limitations. UAVs have emerged as a promising solution for overcoming these challenges by facilitating data collection and transmission in various environments. However, existing UAV trajectory optimization algorithms often overlook the critical factor of the battery capacity, leading to potential mission failures or safety risks. In this paper, we propose a trajectory planning approach Hyperion that incorporates charging considerations and employs a greedy strategy for decision-making to optimize the trajectory length and energy consumption. By ensuring the UAV’s ability to return to the charging station after data collection, our method enhances task reliability and UAV adaptability in complex environments.

The rapid development of Multi-access Edge Computing (MEC) technology is transforming traditional platform services and enabling realization of the mMTC vision of 5G networks. MEC allows computational tasks to be offloaded to devices at the network edge, enabling real-time crowd sensing. However, due to the limited resources in MEC environments, it is essential to propose efficient service deployment and traffic management strategies that balance Quality of Service (QoS) with costs. This paper addresses the challenge by modeling the QoS-effective joint service deployment and traffic management problem (QST) as a nonlinear integer optimization problem. We propose a customized genetic algorithm called GA4QST, which aims to minimize cost-performance ratios. In the experimental section, GA4QST is compared with baseline algorithms in terms of efficiency and effectiveness. Although GA4QST exhibits slightly increased complexity compared to the original genetic algorithm, it performs exceptionally well in balancing benefits and average time costs. GA4QST demonstrates strong capabilities in finding optimal solutions, consistently outperforming baseline algorithms. This further confirms the effectiveness and potential applicability of GA4QST in real-world scenarios. Finally, we also explore the impact on optimization outcomes of system configurations such as service diversity, data volume, processing power, and network characteristics. The results indicate that GA4QST has broad applicability and represents a feasible solution for practical MEC applications.