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 locationbased 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. INDEX TERMS Unmanned aerial vehicles (UAVs), coverage probability, stable clustering, k-means clustering.

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“…Bhandari et al [46] proposed a location-aware k-means UAV clustering algorithm by considering the UAV mobility and relative locations. Their location-aware clustering scheme enhances the performance of UAV networks with limited resources.…”

“…CH mp (47) where d CH fs and d CH mp are the average distances from the CM to their corresponding CH in the free space and multipath models, respectively. For the case of d ≥ d th from (40), substituting ( 45), (46), and (47), we obtain…”

An Energy-Efficient Opportunistic Routing Protocol Based on Trajectory Prediction for FANETs

“…And it uses kmeans density clustering algorithm to choose cluster heads. The authors in [31] proposed mobility and location-aware stable clustering mechanism to help reduce the unnecessary overhead in the network, and used k-means clustering algorithm based on location to enhance the reliability of UAV networks. The disadvantage of this protocol is the lack of consideration for the high speed movement of nodes.…”

Safeguarding Cluster Heads in UAV Swarm Using Edge Intelligence: Linear Discriminant Analysis-Based Cross-Layer Authentication

“…Consequently, the cost of the UAVs becomes lower, which enables the use of multiple UAVs in one mission to enhance the performance and broader the range of applications such as expanding wireless networks coverage, smart policing, search and rescue missions, and disaster monitoring [2]- [4]. To route, allocate resources and relay both the intra-cluster communication and down-link communication for the members, a cluster head (CH) is selected and served as the central node of the UAV swarm [5]. However, due to the vital information contained in the swarm, the attackers may target the security vulnerability and initiate attacks to acquire the sensitive information or compromise the network.…”

“…This makes the CH in the UAV network carry more sensitive information than in MANETs which increases the interest of being attacked. Besides, the UAV network has unique challenges such as the high mobility in which the maximum speed of a UAV can reach 460km/h [5], [8], [9]. Typical MANET or VANETs are associated with moving human users or cars and they usually travel in the same direction [10].…”