Towards Persistent Surveillance and Reconnaissance Using a Connected Swarm of Multiple UAVs
- 주제(키워드) Surveillance , Data collection , Sensors , Reconnaissance , Unmanned aerial vehicles , Navigation , Mobile agents , Persistent surveillance , reconnaissance , connected UAVs , mobile sensor networks , swarm exploration
- 주제(기타) Computer Science, Information Systems
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Telecommunications
- 설명문(일반) [Cho, Jeihee; Sung, Jaeyi; Yoon, Jinyi; Lee, Hyungjune] Ewha Womans Univ, Dept Comp Sci & Engn, Seoul 03760, South Korea
- 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/000000174600
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/ACCESS.2020.3019963
초록/요약
In situations where surveillance or communication infrastructure has collapsed, it is important to keep monitoring affected areas. We leverage unmanned aerial vehicles (UAVs) to collect and provide up-to-date on-site information to a data consumer in an efficient way, for later complete yet agile analysis. We propose a distributed dynamic data collection scheme for persistent surveillance and reconnaissance, using a swarm of connected UAVs with two phases of operation: 1) network formation; and 2) UAV traversal of a region of interest. The main task of a UAV is to continuously collect data within its sensing range, while the UAV swarm travels along the calculated paths. When UAVs are newly connected to form a swarm, or disconnected from an already-formed swarm, a formation phase begins. In the formation phase, UAVs become a single group and produce a compact, dynamically alternating formation called DiagonalX to cover broad areas, including boundary parts, in a fair and effective manner. During the traversal phase, each UAV swarm finds a simple yet efficient navigation path based on data freshness to cover sub-areas and continuously obtain up-to-date information evenly throughout the whole region of interest. Simulation experiments confirm that both formation and traversal procedures perform essential tasks in a distributed manner, while maintaining better data freshness than other counterpart algorithms, with a freshness factor of up to 5.77, and reasonable overheads. An additional feature, a dynamically aperiodic formation change, achieves a more stable performance.
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