Oct 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 17845-17862· 1 citation· 37 references
Abstract
In large areas, mobile edge computing (MEC) systems enabled by drones, also known as unmanned aerial vehicles (UAVs), can provide flexible edge computing services and facilitate low-altitude inspection. Such systems are primarily limited by the computing resources and energy of the drone, as well as their reliance on cellular network infrastructure. To overcome these limitations, this paper investigates a cooperative drone-vehicle MEC system in which a ground vehicle (GV) carries an accompanying drone (AD) and a detached drone (DD) to visit multiple data collection nodes for low-altitude inspection. We develop a joint drone-vehicle model with path planning, data collection, and processing. The AD is carried by the GV to multiple nodes and collects data when the GV is stationed at a node. The DD can detach from the GV to visit other nodes and perform data collection independently. The GV and drones cooperate in data processing and energy replenishment. We propose a heuristic to minimize the low-altitude inspection mission completion time by jointly optimizing the route and the DD speeds. We analyze the relationship between the DD speed and the size of DD-processed data and design a method that includes flight power approximation to optimize the DD speed. Numerical results indicate that solutions optimized by the heuristic fully utilize the DD, thereby reducing the completion time of low-altitude inspection missions.
Drone-vehicle collaborative inspection system, which combines the long-range strength of a vehicle and the speed, agility, and direct-flight capability of drones, has been regarded as a promising scheme to facilitate the tasks of smart city inspection. Specifically, the vehicle carries multiple drones to successively v...
Xu-Min Huang, Xin Chen, Wei-Feng Zhong et al.· IEEE Transactions on Cogniti...· 0 citations
A network-adaptive edge-assisted multi-view localization framework that combines scalable orthogonality-regularized variational information bottleneck (O-VIB) encoding, value-of-information (VOI)-guided request control, and value-aware edge scheduling is proposed.
Zheng-Ru Fang, Huanhuan Lou, Sen-Kang Hu et al.· 0 citations
A structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers is presented, indicating that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster...
Rakan Armoush, Shidrokh Goudarzi, Muhammad Nadeem Khan et al.· Italian National Conference...· 0 citations
In this article, we investigate the problem of computational task offloading and unmanned aerial vehicle (UAV) path planning within an edge computing-enabled space–ground integrated metaverse system. Specifically, we conceptualize the UAV as a “Communication – Perception – Computing” integrated device that provides rea...
Jian-Bo Du, Hui-Fang Fang, Zheng-Qin Pei et al.· IEEE Journal of Selected Are...· 0 citations
This paper investigates energy-aware joint trajectory planning and service scheduling in a heterogeneous unmanned aerial vehicle base-station (UAV-BS) network comprising conventional hovering UAV-BSs, referred to as Hovers, and graspingenabled UAV-BSs, referred to as Anchors. The service region is discretized into grou...
Shi-Jie Zhu, V. Friderikos· IEEE International Symposium...· 0 citations
Unmanned Aerial Vehicle (UAV)-aided Integrated Sensing, Communication, and Computation (ISCC) networks have emerged as a core component of the low-altitude network, providing an efficient mobile platform for complex tasks. In UAV coverage missions, jointly optimizing the trajectory and ISCC resources under global energ...
Peng Hou, Ya-Peng Zhao, Hong-Bin Zhu et al.· IEEE Transactions on Communi...· 0 citations
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