Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

Park, Soo-Hyun; Shin, Won-Yong; Choi, Minseok; Kim, Joongheon

doi:10.1109/access.2021.3093431

Cited by 20 publications

(8 citation statements)

References 42 publications

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“…al. proposed a two-stage charging matching approach, i.e., using ground-based charging towers to charge the charging UAVs, and the charging UAVs to charge the UAVs [24]. Moadab et.…”

Section: Mobile-unit Charging Strategiesmentioning

confidence: 99%

MobiPower: Scheduling Mobile Charging Stations for UAV-Mounted Edge Servers in Internet of Vehicles

Huang,

Li,

Chen

et al. 2024

Preprint

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Recently, Unmanned Aerial Vehicles (UAVs) have been proposed as the carriers of mobile edge servers. Such UAV-mounted edge servers can offer computation offloading service to users more efficiently due to their fast movement. However, the limited battery endurance of the UAVs places a real challenge. In this paper, we propose MobiPower: a two-layer UAV-based edge computing and charging scheme for Internet of Vehicles (IoVs). The UAV-mounted edge servers fly in the air to serve the IoV nodes, while the Mobile Charging Stations (MCSs) move on ground to charge these UAVs. As the mobile charging stations are low-speed trucks which are difficult to respond to the realtime traffic change, we predict the future traffic based on the historical data to facilitate obtaining the offline scheduling strategies in advance. Moreover, we use hierarchical clustering to analyze the traffic patterns and classify the regions with same pattern into the same cluster. Mobile charging stations will only be scheduled between different clusters to reduce the complexity. Finally, we design an auction-based mobile charging station scheduling algorithm to maximize the amount of offloaded tasks and reduce the moving distance of the mobile charging stations. We perform the analysis and experiments on a popular trace, i.e., the TaxiBJ trace, and the experimental results show that our heuristic strategies effectively schedule the mobile charging stations over the entire map and demonstrate superior performance compared with existing popular methods, e.g., up to 36.46% improvement in terms of task offloading ratio and 39.06% less scheduling distance of the mobile charging stations under typical scenarios.

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“…al. proposed a two-stage charging matching approach, i.e., using ground-based charging towers to charge the charging UAVs, and the charging UAVs to charge the UAVs [24]. Moadab et.…”

Section: Mobile-unit Charging Strategiesmentioning

confidence: 99%

MobiPower: Scheduling Mobile Charging Stations for UAV-Mounted Edge Servers in Internet of Vehicles

Huang,

Li,

Chen

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…For the fresh content providing in connected vehicle network, we set 4 values which have to be considered. In (5), each value means (i) AoI of contents that exist in all RSUs, (ii) communication cost used by the MBS for RSU cache management (content upload from CVs and content update to RSUs), (iii)service waiting delay of UVs, and (iv) communication cost that RSU uses while providing UV service. They are divided as two content transmission stages as mentioned in Sec.…”

Section: B Problem Formulationmentioning

confidence: 99%

“…for content service (5) The overall object which has to be achieved to ensure the latest status of content and prevent indiscriminate communication in the proposed network is as shown in following (6).…”

Section: B Problem Formulationmentioning

confidence: 99%

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Age-of-Information Aware Contents Caching and Distribution for Connected Vehicles

Park¹,

Park²,

Jung³

et al. 2022

Preprint

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To support rapid and accurate autonomous driving services, road environment information, which is difficult to obtain through vehicle sensors themselves, is collected and utilized through communication with surrounding infrastructure in connected vehicle networks. For this reason, we consider a scenario that utilizes infrastructure such as road side units (RSUs) and macro base station (MBS) in situations where caching of road environment information is required. Due to the rapidly changed road environment, a concept which represents a freshness of the road content, age of information (AoI), is important. Based on the AoI value, in the connected vehicle system, it is essential to keep appropriate content in the RSUs in advance, update it before the content is expired, and send the content to the vehicles which want to use it. However, too frequent content transmission for the minimum AoI leads to indiscriminate use of network resources. Furthermore, a transmission control, that content AoI and service delay are not properly considered adversely, affects user service. Therefore, it is important to find an appropriate compromise. For these reasons, the objective of this paper is about to reduce the system cost used for content delivery through the proposed system while minimizing the content AoI presented in MBS, RSUs and UVs. The transmission process, which is able to be divided into two states, i.e., content caching and service, is approached using Markov decision process (MDP) and Lyapunov optimization framework, respectively, which guarantee optimal solutions, as verified via data-intensive performance evaluation.

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“…This paper proposes a data delivery service by the drone network in a surveillance system. With advances to AI, modeling, and simulation technologies, it has become essential to analyze data to extract potential values and create additional services [8], [9]. In this situation, a large amount of data accumulation is fundamentally required, including various range of information.…”

Section: Introductionmentioning

confidence: 99%

Neural Myerson auction for truthful and energy-efficient autonomous aerial data delivery

et al. 2022

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A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account. In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone. Our proposed Myerson auction based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations. On top of this SPA-based operations, a deep learning based framework is additionally designed for delivery performance improvements.

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Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

Cited by 20 publications

References 42 publications

MobiPower: Scheduling Mobile Charging Stations for UAV-Mounted Edge Servers in Internet of Vehicles

MobiPower: Scheduling Mobile Charging Stations for UAV-Mounted Edge Servers in Internet of Vehicles

Age-of-Information Aware Contents Caching and Distribution for Connected Vehicles

Neural Myerson auction for truthful and energy-efficient autonomous aerial data delivery

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