IEEE Access (Jan 2020)
Mobility and Location-Aware Stable Clustering Scheme for UAV Networks
Abstract
In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a location-based $k$ -means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods.
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