With the widespread use of Internet of Things (IoT) technology, an enormous number of end devices that request various kinds of cloud services have been connected to the Internet. Multi-access edge computing (MEC) can reduce the service response time by selecting the required edge computing resources closer to the end device. However, MEC-based smart systems require heterogeneous and diverse services support. Taking unmanned aerial vehicle (UAV) last-mile delivery system as an example, there are two types of services required: delivery and computational services. The edge services in MEC environments are distributed and limited. Inefficient service selection plans will affect the quality of services of such smart systems. Therefore, how to design a suitable service selection strategy is a crucial issue for MEC-based smart systems. To address this issue, we propose a service selection framework and a holistic and hybrid service selection ($H^{2}S^{2}$) strategy for MEC-based UAV last-mile delivery systems in real-world UAV last-mile delivery scenarios. This framework considers three important characteristics of UAV delivery systems: diverse service requirements, service availability, and service mobility. The$H^{2}S^{2}$strategy focuses on selecting the optimal delivery and computational services and provides an integrated approach with a static service selection algorithm and a dynamic service re-selection algorithm. The$H^{2}S^{2}$strategy determines the optimal delivery and computational service selection plans with the lowest UAV energy consumption and shortest service response time. We assess the effectiveness and efficiency of the$H^{2}S^{2}$strategy through ablation studies and comparative analyses with diverse representative strategies. The experimental results show that the$H^{2}S^{2}$strategy improves the effectiveness and efficiency of the UAV delivery system by significantly reducing UAV's energy consumption and service response time.

With the rapid growth of Internet of Things (IoT), Mobile Edge Computing (MEC) is becoming the major platform for many smart systems such as smart logistics, smart healthcare, and smart transportation, given its lower latency and higher reliability compared with centralized cloud computing. There is a growing interest in Drone-as-a-Service in recent years which enables the MEC-based smart UAV delivery system. However, most existing works on Drone-as-a-Service focus on the static service composition or the dynamic service provisioning, rather than a holistic service provisioning strategy for the entire UAV delivery process. In this paper, we propose a holistic service provisioning strategy for Drone-as-a-Service in MEC-based UAV delivery to address such an issue. Specifically, a MEC-based UAV relay delivery system framework (RDS) is designed, which considers both the static stage for provisioning delivery services and the dynamic stage for provisioning computing services. Based on the service models for both static and dynamic stages, an energy-efficient service provision strategy (ESP-GA) for MEC-based UAV last-mile delivery is proposed, which aims to minimize the overall energy consumption under deadline constraints. Through the simulation experiments based on a prototype UAV delivery system, the experimental results have successfully demonstrated the superior performance of the proposed holistic strategy in comparison with several representative service provisioning strategies.