An Optimal Approach for Deployment Sensors in WSN

Grari, Hamida; Hajjej, Faten; Ejbali, Ridha; Zaied, Mourad

doi:10.12720/jcm.13.3.124-128

Cited by 8 publications

(1 citation statement)

References 5 publications

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“…Literature [9] 9 proposes a UAV relay selection strategy based on SA-SARSA reinforcement learning algorithm, which improves the throughput of relay nodes. Literature [10] 10 proposes a new approach to WSN deployment that minimizes energy consumption by reducing the distance that sensors have to travel to reach the optimal location. Although the above methods can solve the relay UAV deployment problem to a certain extent, they still have deficiencies.…”

Section: Unmanned Aerial Vehicle (Uav) Relaymentioning

confidence: 99%

Optimization of artificial bee colony deployment based on rapid depth-first search

Zhou,

Zhang,

Chen

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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Post-disaster networking is the foundation and the primary work of emergency rescue. In this paper, we adopt the rapid deployment method of artificial bee colony (ABC) based on rapid depth-first search (RDFS) to keep the UAV cluster in the relay state, and use rapid depth-first search in the period of employment bees, observation bees and scout bees, respectively, to quickly find out the connectivity links between the ground control centre and the ground nodes, and by optimizing the depth-first search algorithm, we can stop searching as soon as any link is found, which greatly improves the relaying efficiency and shortens the relaying deployment time, and identifies an optimal relaying deployment strategy to improve the throughput. Simulation experiments show that compared with the deployment method before optimization, the rapid deployment method of artificial bee colony based on rapid depth-first search after optimization improves the throughput of the network by up to 12 times.

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Section: Unmanned Aerial Vehicle (Uav) Relaymentioning

confidence: 99%