# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 2 Background
### UAVs-Based Data Aggregation  
In FSNets, the collection and transmission of information through multiple hops increase energy utilization [22]. The data aggregation approach reduces energy utilization and increases network lifetime by minimizing UAVs load. The data aggregation approaches reduce the communication cost and energy consumption [23]. The researchers developed aggregation approaches for FSNet without redundant data elimination [24]. [PERSON] et al. [25] introduced a UAV-assisted topology-aware data aggregation protocol in WSN (TA-UAV-DA). The data aggregation approach was inspired by the compressive sensing approach to reduce the errors rate in the data reconstruction process, extra overhead, and energy consumption. Balanced tree-based topology construction is performed tominimize the scope and update the matrix measurement. The CMs send data to CH, and UAV gathers data from the CHs. The simulation results show that the approach performs better when reconstructing data, and it is more efficient in data aggregation and storage constraints than a random walk and intelligent compressive sensing. [PERSON] et al. [26] developed an energy-efficient UAV-based data aggregation protocol (EE-UAV-DA) for WSNs. In EE-UAV-DA scheme, UAVs gather data as data mules. The proposed method calculates the optimum link for the data mule through all CHs, using a genetic algorithm that achieves high-energy efficiency. By balancing system throughput, the proposed approach reduces the delay between the sensors and the sink node. The optimization scheme provided by heuristic search identifies optimal solutions for joint CH selection and optimal routes for the data mule to decrease energy consumption. The objective function calculates and measures each solution's quality for the optimum path. [PERSON] [27] proposed optimizing communication and computation for multi-UAV information-gathering applications called OC-mUAV. Multi-hop clustering incorporates data aggregation by using a mixed-integer optimization formulation with mixed-integer nonlinear programming. In order to determine the roles of UAVs, the optimal control problem was formulated. The system framework tries to ensure that the optimal number of UAVs communicate with BS. To maintain minimum energy consumption during routing, each UAV acts as an aggregator. An adaptive energy consumption model minimizes energy consumption by considering sensing energy, aggregation energy, transmitting energy, and receiving multi-UAV energy. To reduce communication and communication energy, area mapping and target tracking were addressed during testing. Target and sensor models are used to select the sensor UAV based on a subset of UAVs. The distance between UAVs is used for mapping. As a result of the data aggregation framework, the network is flexible and reliable since it is a self-organized network, which prolongs the lifetime of the network due to multi-hop networks and provides better performance due to clustering of heterogeneous UAVs. [PERSON] et al. [28] proposed an algorithm to collect and process data in WSN, using UAVs and mobile agents (MAs) to search for victims at disaster sites. MAs move around the area to collect data from sensor nodes and share information with UAVs. The UAV assigns MAs to group leaders. The density of sensor nodes in a group known by the group leader has high residual energy and is an optimum link to a UAV. MAs' routing is based on information-driven static and dynamic mobile agent planning algorithms. The proposed scheme is efficient in energy and time for any dense network using MAs and UAVs. To decrease energy utilization in a UAV-aided WSN, [PERSON] and [PERSON] [29] proposed an energy-efficient data collection method. Sensor nodes are placed randomly in the environment. The proposed approach uses three transmission modes to solve the short buffer size of sensor nodes to transmit data within the allotted time slots. The sensor node selects the modes, i.e., waiting, transmission to a sink node, and uploading to UAV in each discrete time slot.