# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 2 Background
### Energy Efficient Clustering  
Energy-efficient clustering strategies for FSNet are still in their infancy [16]. This section presents an overview of some existing energy-efficient solutions based on different parameters, criteria, and approaches. FSNet has many military and civil applications. Nevertheless, the main issues of UAVs in FSNet are limited energy, high mobility, frequent topology changes, and terrain structure, due to the limited flight time of UAVs and lack of routing efficiency [17]. [PERSON] et al. [18] addressed these issues and proposed a clustering model with energy-aware link-based clustering called EALC. For efficient clustering, parameters such as energy, degree, cluster formation, and cluster head lifetime are taken into account. Link quality and transmission range are considered first to reduce the energy consumption of flying nodes. The K-means density-clustering model used for high mobility considers distance and energy. This model selects cluster heads (CHs) with the least computation and maximum throughput and minimizes routing overhead. The lifetime of UAVs increased with the use of an energy-efficient selection of CHs. A simple clustering approach is used to reduce computation overhead. Nevertheless, EALC ignored the communication load, data aggregation, and bandwidth utilization factors.  
[PERSON] and [PERSON] [19] proposed an energy-efficient clustering scheme based on particle swarm optimization for an emergency mission. First, implement swarm-intelligence-based localization and clustering (SIL and SIC) schemes that define the search space with a boundary box to reduce computation power. UAVs are placed randomly in the search space. SIL uses a grouping scheme to calculate the distance between the target UAVs. The proposed model has an estimation model to measure UAV distance from CH. The distance between nodes and CH is considered for reducing energy consumption. The Euclidean distance is utilized for locating UAVs and balancing cluster size. In SIC, cluster formation and CH selection are performed with a fitness function that considers remaining energy and distance. The performance metrics used in this paper are energy consumption, communication load, and delay. Node degree and residual energy are considered to balance inter-cluster and intra-cluster energy utilization.  
[PERSON] et al. [20] proposed a probabilistic energy-aware clustering scheme to find the most efficient path for UAVs using ant colony optimization (ACO). WSN data-gathering efficiency is achieved with UAVs. The network is divided into clusters, each with a CH, cluster members (CMs), and UAV. The nodes are stationary with a position-aware CH; the CH receives information from the UAVs flying around the cluster heads. The proposed model has three stages. First, a UAV senses data about the farmland event via the ground segment. Second, the data gathered by UAVs is transmitted to the data center through the CH. Finally, the data center contains a database and management information system.  
To overcome packet loss, long delay, and increased routing overhead, [PERSON] et al. [21] proposed a clustering protocol based on ACO to enhance network performance. A reliable link supervision method was established due to UAV mobility and topology variation. The nonlinear processing scheme increases buffer size and link load to control and avoid congestion on the link. Based on the density of UAVs, two routing strategies are proposed, namely sparse formation and concentrated formation. ACO-based polymorphism-aware routing integrates dynamic source routing and ACO.