Optimal path planning for UAV using NSGA-II based metaheuristic for sensor data gathering application in Wireless Sensor Networks
| Autor: | Govind P. Gupta, Seema Dewangan, Vrajesh Kumar Chawra |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Data collection Fitness function Computer science Real-time computing 020206 networking & telecommunications 02 engineering and technology Python (programming language) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Motion planning Distributed File System Metaheuristic Wireless sensor network computer computer.programming_language |
| Zdroj: | ANTS |
| Popis: | Data Collection from the remote or very callous environments is very challenging issue in wireless sensor network. In a remote or very callous environment, when sensor nodes are randomly deployed, it is observed that all sensor nodes are not fully connected. Therefore, to facilitate an efficient data collection from remote sensing region, application of Unmanned Arial Vehicles (UAVs) has been mulled over with the wireless sensor networks. This paper considers optimal path planning problem for UAV using NSGA-II-based meta-heuristic algorithm. In the proposed scheme, first Depth-First-Search (DFS) algorithm is used for selecting the best sequence of flying cells to be visited by a UAV for data collection. After selection of the best sequence of flying cells, NSGA-II-based meta-heuristic algorithm is used for computing the optimal path of the UAV for covering the all designated flying cells within the network. This paper derives a novel fitness function for judging the optimal path for UAV. Simulation results of the proposed scheme and its comparisons with the existing GA-based scheme are done using a custom simulator in Python 3. |
| Databáze: | OpenAIRE |
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