TY - JOUR
T1 - 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
AU - Eledlebi, Khouloud
AU - Hildmann, Hanno
AU - Ruta, Dymitr
AU - Isakovic, A. F.
N1 - Funding Information:
Funding: We acknowledge the support from UAE ICT Fund grant on “Biologically Inspired Self-organizing Network Services.”
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Drone swarms
KW - Drones
KW - Energy aware
KW - Genetic algorithm
KW - GPS denied
KW - Noise coherence
KW - Particle swarm optimization
KW - Position-navigation-timing
KW - Self-optimization
KW - Self-organization
KW - Swarm intelligence
KW - Swarming
KW - Voronoi centroids
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85090616185&partnerID=8YFLogxK
U2 - 10.3390/drones4030033
DO - 10.3390/drones4030033
M3 - Article
AN - SCOPUS:85090616185
SN - 2504-446X
VL - 4
SP - 1
EP - 30
JO - Drones
JF - Drones
IS - 3
M1 - 33
ER -