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A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

1
Department of Electrical Engineering, Khalifa University, Abu Dhabi 127788, UAE
2
Intelligent Autonomous Systems Group, Netherlands Organization for Applied Scientific Research (TNO), Oude Waalsdorperweg 63, 2597 AK The Hague, The Netherlands
3
Data Science Group, Emirates ICT Innovation Centre (EBTIC), Abu Dhabi 127788, UAE
4
Physics and Astronomy Department, Colgate University, Oak Dr. 13, Hamilton, NY 13346, USA
5
CNF, Cornell University, Ithaca, NY 14853, USA
*
Author to whom correspondence should be addressed.
Drones 2020, 4(3), 33; https://doi.org/10.3390/drones4030033
Received: 3 June 2020 / Revised: 30 June 2020 / Accepted: 2 July 2020 / Published: 14 July 2020
Using autonomously operating mobile sensor nodes to form adaptive wireless sensor networks has great potential for monitoring applications in the real world. Especially in, e.g., disaster response scenarios—that is, when the environment is potentially unsafe and unknown—drones can offer fast access and provide crucial intelligence to rescue forces due the fact that they—unlike humans—are expendable and can operate in 3D space, often allowing them to ignore rubble and blocked passages. Among the practical issues faced are the optimizing of device–device communication, the deployment process and the limited power supply for the devices and the hardware they carry. To address these challenges a host of literature is available, proposing, e.g., the use of nature-inspired approaches. In this field, our own work (bio-inspired self-organizing network, BISON, which uses Voronoi tessellations) achieved promising results. In our previous approach the wireless sensors network (WSN) nodes were using knowledge about their coverage areas center of gravity, something which a drone would not automatically know. To address this, we augment BISON with a genetic algorithm (GA), which has the benefit of further improving network deployment time and overall coverage. Our evaluations show, unsurprisingly, an increase in energy cost. Two variations of our proposed GA-BISON deployment strategies are presented and compared, along with the impact of the GA. Counter-intuitively, performance and robustness increase in the presence of noise.
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Keywords: Voronoi centroids; genetic algorithm; particle swarm optimization; drones; drone swarms; swarming; swarm intelligence; wireless sensor networks; self-organization; self-optimization; energy aware; noise coherence; position-navigation-timing; GPS denied Voronoi centroids; genetic algorithm; particle swarm optimization; drones; drone swarms; swarming; swarm intelligence; wireless sensor networks; self-organization; self-optimization; energy aware; noise coherence; position-navigation-timing; GPS denied
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MDPI and ACS Style

Eledlebi, K.; Hildmann, H.; Ruta, D.; Isakovic, A.F. A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments. Drones 2020, 4, 33. https://doi.org/10.3390/drones4030033

AMA Style

Eledlebi K, Hildmann H, Ruta D, Isakovic AF. A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments. Drones. 2020; 4(3):33. https://doi.org/10.3390/drones4030033

Chicago/Turabian Style

Eledlebi, Khouloud; Hildmann, Hanno; Ruta, Dymitr; Isakovic, A. F. 2020. "A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments" Drones 4, no. 3: 33. https://doi.org/10.3390/drones4030033

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