Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data.


Computer Science


The work is supported by the National Natural Science Foundation of China under Grant no. 61004112 and the Fundamental Research Funds for the Central Universities (no. CDJZR12180006). The work of Debraj De and Sajal K. Das was partially supported by NSF projects under Awards nos. CNS-1404677, CNS-1355505, and CNS-1545037.

Keywords and Phrases

Efficiency; Flight simulators; Mobile antennas; Motion planning; Unmanned aerial vehicles (UAV); Wireless sensor networks; Data collection; Data collectors; Grid division; Large scale sensor network; Large-scale wireless sensor networks; Performance efficiency; Simulation platform; Time consumption; Data acquisition

International Standard Serial Number (ISSN)

1550-1329; 1550-1477

Document Type

Article - Journal

Document Version

Final Version

File Type





© 2015 Chengliang Wang et al., All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Nov 2015