Name
Abstract | ||
|---|---|---|
In heterogeneous wireless networks, collaboration between mobile and static elements allows optimal exchange (in terms of latency, reliability and energy savings) ensured by the mobile elements of the data collected with precision by the static elements. In this article, we focus on this collaboration. We base our study on genetic algorithms to select the best next destination from the event area according to different criteria, such as the amount of data collected and where the passage of the UAVs (Unmanned Aerial Vehicles) is delayed. Choosing the best destination ensures better latency and a high level of received data. The events and characteristics of the event area will be sensed by the static elements (this is to ensure optimal precision since the static element will be placed in the desired location to be studied), while the mobile elements will be charged to collect the data sensed by the static elements, and to ensure their routing to the collecting station. The results confirm the effectiveness of our collaborative approach compared to a solution based on random mobility of mobile elements. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-70866-5_6 | MACHINE LEARNING FOR NETWORKING, MLN 2020 |
Keywords | DocType | Volume |
Heterogeneous wireless networks, Mobility, Nodes collaboration, UAV, Genetic algorithm | Conference | 12629 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamel Barka | 1 | 0 | 0.34 |
| Lyamine Guezouli | 2 | 0 | 0.34 |
| Samir Gourdache | 3 | 0 | 2.03 |
| Sara Ameghchouche | 4 | 0 | 0.34 |