Service Handover refers to the process of transferring responsibility for a particular service or task from one entity to another. This transfer can occur for various reasons, such as a change in personnel, organizational restructuring, or the need to transition services between different systems or environments. The goal of a service handover is to ensure a smooth transition, maintaining continuity and efficiency in the delivery of the service.

In the context of telecommunications or mobile networks, service handover can also specifically refer to the process of transferring an ongoing communication session from one network node or cell to another. This is crucial in maintaining seamless connectivity for mobile users as they move through different coverage areas.

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Seamless Service Handover in UAV-based Mobile Edge Computing

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Unmanned aerial vehicles (UAVs), such as drones, can carry high-performance computing devices (e.g., servers) to provide flexible and on-demand data processing services for theusers in the network edge, leading to the so-called mobile edge computing. In mobile edge computing, researchers have already explored how to optimize the computation offloading and the trajectory planning of UAVs, as well as how to perform the service handover when mobile users move from one location to another. However, there is one critical challenge that has been neglected in past research, which is the limited battery life of UAVs. On average, commercial-level drones only have a battery life of around 30 minutes to 2 hours. As a result, during operation, mobile edge computing carriers have to frequently deal with service handovers that require shifting users and their computing jobs from low-battery UAVs to new fully-charged UAVs. This is the first work that focuses on addressing this challenge with the goal of providing continuous and uninterrupted mobile edge computing service. In particular, we propose a seamless service handover system that achieves minimum service downtime when handling the duty shift between low-battery UAVs and new fullycharged UAVs. In addition, we propose a novel UAV dispatchalgorithm that provides guidelines about how to dispatch new fully-charged UAVs and where to retrieve low-battery UAVs, with the objective of maximizing UAVs’ service time. The effectiveness of the proposed service handover system and the proposed UAV dispatch algorithm is demonstrated through comprehensive simulations using a time-series event-driven simulator.

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