Cost-effective resource segmentation in hierarchical mobile edge clouds

Jin, Mingshuang; Gao, Shuai; Luo, Hongbin; Zhang, Hongke

doi:10.1631/fitee.1800203

Cited by 2 publications

(1 citation statement)

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“…Mobile edge computing (MEC) is recognized as a promising technology to address the above challenge. It provides computing services to IoT devices by offloading tasks to edge servers at the edge of the network (Jin et al, 2019;Huang PQ et al, 2020a). In this way, MEC can reduce latency and energy consumption during task execution.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system

Huang

Wang

2020

Front Inform Technol Electron Eng

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We study a mobile edge computing system assisted by multiple unmanned aerial vehicles (UAVs), where the UAVs act as edge servers to provide computing services for Internet of Things devices. Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs. This problem is difficult to address because when planning the trajectories, we need to consider not only the order of stop points (SPs), but also their deployment (including the number and locations) and the association between UAVs and SPs. To tackle this problem, we present an energy-efficient trajectory planning algorithm (TPA) which comprises three phases. In the first phase, a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time. In the second phase, the k-means clustering algorithm is employed to group the given SPs into a set of clusters, where the number of clusters is equal to that of UAVs and each cluster contains all SPs visited by the same UAV. In the third phase, to quickly generate the trajectories of UAVs, we propose a low-complexity greedy method to construct the order of SPs in each cluster. Compared with other algorithms, the effectiveness of TPA is verified on a set of instances at different scales.

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Section: Introductionmentioning

confidence: 99%