Time-Dependent Ad-Hoc Routing Structure for Delivering Delay-Sensitive Data Using UAVs
- 주제(키워드) Time-dependent routing , delay constraints , load-carry-and-delivery , vehicular ad-hoc networks , unmanned aerial vehicles , ad-hoc data delivery
- 주제(기타) Computer Science, Information Systems
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Telecommunications
- 설명문(일반) [Yoon, Jinyi; Doh, Sungju; Lee, Hyungjune] Ewha Womans Univ, Dept Comp Sci & Engn, Seoul 03760, South Korea; [Gnawali, Omprakash] Univ Houston, Dept Comp Sci, Houston, TX 77204 USA
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 gold
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000169459
- 본문언어 영어
- Published As https://dx.doi.org/10.1109/ACCESS.2020.2974553
초록/요약
In disaster scenarios where communication networks have broken down, it is important to ensure a reliable data delivery from an emergency operations center to the local target nodes within its effective time limit. We propose a hybrid data delivery mechanism that exploits the load-carry-and-delivery by UAVs with a mixture of localized ad-hoc routing over partially connected terrestrial networks. We aim to achieve reliable on-time data delivery to the target nodes, while preserving the lowrouting cost. Our proposed routing methodology consists of three steps: 1) localized network construction, 2) network probing by UAVs, and 3) localized ad-hoc routing based on a dynamic depth routing tree depending on the data urgency. Here, we present an innovative cost-effective local data sharing structure called localized minimal routing tree that balances with the direct data delivery by UAVs. After the initial network setup and probing procedure, each UAV makes a series of near-optimal decisions of which grid points to visit considering its localized network topology and data urgency. Our time-dependent routing mechanism dynamically decides data recipient nodes to serve more urgent data delivery with a higher priority at a time. The simulation experiments validated our combined path planning and routing approach, achieving 71% higher reliability than the best possible performance by using only the network nodes and consuming 20% lower energy than theUAV-only approach, while maintaining high reliability. Thus, our work makes a strong case for systematically combining the two approaches.
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