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. 2018 Nov 30;18(12):4202.
doi: 10.3390/s18124202.

A Collaborative UAV-WSN Network for Monitoring Large Areas

Dan Popescu  1 , Cristian Dragana  2 , Florin Stoican  3 , Loretta Ichim  4 , Grigore Stamatescu  5
Affiliations

A Collaborative UAV-WSN Network for Monitoring Large Areas

Dan Popescu et al. Sensors (Basel). .

Abstract

Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value.

Keywords: clustering; large area monitoring; optimal trajectory design; unmanned aerial vehicle; wireless sensor network.

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Conflict of interest statement

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Figures

Figure 1
Figure 1
Hybrid UAV-WSN large scale monitoring system concept.
Figure 2
Figure 2
Hybrid UAV-WSN large scale monitoring functional diagram.
Figure 3
Figure 3
Example of aerial vehicle flight path planning in a multi-obstacles environment.
Figure 4
Figure 4
Photogrammetry trajectories for sensor location estimation.
Figure 5
Figure 5
Sensor position estimation through a photogrammetry procedure (with highlight for the uncertainty radius). (a) estimation detail; (b) uncertainty range.
Figure 6
Figure 6
UAV-WSN survey dialog sequence.
Figure 7
Figure 7
UAV Probe Flight for ground WSN nodes revealing.
Figure 8
Figure 8
Clustering sequence.
Figure 9
Figure 9
Estimation of the sensor location in both exact and over-approximated (by circles).
Figure 10
Figure 10
Computed trajectories passing through or near the cluster heads: gray-filled regions—obstacles, semi-transparent red regions—communication areas.
Figure 11
Figure 11
Clustered WSN—Simulation result (normal and head sensors with cluster highlighting).
Figure 12
Figure 12
Equipment used in the experimental run. (a) Ground sensor node; (b) UAV.
Figure 13
Figure 13
Flying path for the experimental run.
Figure 14
Figure 14
Sensor nodes response positions.
Figure 15
Figure 15
Sensors’ estimated location as the result of succesive UAV mesurements.
Figure 16
Figure 16
RSSI for each unicast along the experimental run.
See this image and copyright information in PMC

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