ITS: Improved Tabu Search Algorithm for Path Planning in UAV-Assisted Edge Computing Systems

Mobile Edge Computing (MEC) plays a crucial role in providing diverse computation and storage services to intelligent equipment. In recent years, the utilization of Unmanned Aerial Vehicles (UAVs) equipped with edge servers has emerged as a promising approach to enable ubiquitous edge computing services. This paradigm offers various benefits, such as reduced latency and flexible service provisioning. In the context of UAV-assisted edge computing, optimizing the location and trajectory of UAVs is vital, since the communication distance has significant impact on the communication rates between edge servers and devices. To address this optimization problem, this paper establishes a UAV-assisted edge computing system and focuses on investigating the path planning issue to expedite the offloading of computational tasks. In order to achieve this objective, we propose an improved tabu search algorithm that can efficiently optimize the number of UAVs, path planning. And to ensure reliable communication, we introduce reliability guarantees. Furthermore, we consider the energy limitations of UAVs to ensure practical feasibility. Through extensive simulations, we demonstrate that the proposed algorithm outperforms alternative approaches in terms of the specified objectives.

I. Introduction

The rapid development of mobile communication and Internet of things (IoT) technologies, coupled with the advancements in industrial manufacturing capabilities, has led to the widespread use of intelligent mobile devices such as mobile phones, wearable devices and sensor devices. However, these UEs face limitations in terms of their physical resources, residual energy, etc. [1], making it challenging for them to perform computationally intensive tasks, such as virtual reality, face recognition and building scanning analysis. Mobile edge computing (MEC) can effectively solve these problems by shifting services to edge servers that are close to UEs, such as cellular base stations, and providing diverse computation and storage services. Previous research on edge computing primarily focused on static edge servers that can handle most application requests. However, the coverage of static edge servers is inherently limited. Moreover, network infrastructures become unavailable in case of some natural disasters such as earthquakes and hurricanes [2], resulting in a faulty state where the infrastructure or communication link terminals are unable to serve mobile devices. Deploying a large number of edge servers exclusively for such scenarios would require significant resources, making it impractical [3]. In contrast, Unmanned Aerial Vehicles (UAVs) offer distinct advantages in mobility and adaptability. UAVs can be deployed to specific locations and navigate through various terrains, including wilderness, oceans, and deserts. By equipping UAVs with edge servers, edge computing systems can benefit from enhanced mobility, enabling edge servers to dynamically serve users in different locations and adapt to changing environmental conditions. Moreover, this paradigm provides a viable approach to extend the coverage of services beyond the limitations of static edge servers, making it particularly valuable in situations where network infrastructure is compromised or where specific mobility requirements arise.