# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 1 Introduction  
A multi-unmanned aerial vehicle (UAV)-aided flying sensor network (FSNet) is constrained by various energy factors, such as limited energy, computation, memory, and communication [1, 2]. The energy consumption for sensing and computation is less than the energy used for communication among the UAVs or to the ground station (GS) [3, 4]. The available energy resources are sometimes insufficient for transmission and computation during the mission. However, the collected data need to be communicated to the destination for further processing. The performance of the network lifetime depends on efficient energy utilization [5]. The researchers tried to minimize energy utilization in other wireless networks, but energy utilization still exists. Due to the flying speed of UAVs, the rapid variation in topology, terrain structure, and diverse directions make it difficult to collect and route information [6]. The researchers proposed energy-efficient schemes by considering different parameters such as reducing the communication distance, computation cost, mobility, and degree. However, data collection and minimization of communication load go unnoticed to save bandwidth and energy [7, 8].  
A data aggregation approach reduces the energy consumption of UAVs and increases their lifespan. The data aggregation approach is different in wireless sensor networks(WSNs) and vehicular ad hoc networks (VANETs) from UAV networks [9]. In WSN, the uses of the data aggregation approach are for decreasing energy consumption rather than minimizing network capacity usage [10]. In VANET, due to the high variation in the topology, data aggregation is performed by many vehicles. In addition to the degree, mobility, density, and other parameters, the multi-UAV system is constrained by energy factors, which is why it is compatible with WSN and VANET requirements [11]. UAVs also consume energy by processing and storing more data, just like flight and communication [12].  
UAV-based data aggregation has emerged as a promising solution for collecting data from remote and hard-to-reach areas. One of the main motivations for UAV-based data aggregation is the ability to obtain data more efficiently and accurately. UAVs can cover a larger area in a shorter amount of time compared to traditional data collection methods. This means that data can be collected faster, allowing for a quicker analysis and quicker decision-making. Additionally, UAV-based data aggregation can also reduce data collection costs. Traditional methods often require expensive equipment and personnel, whereas UAVs can be operated by a single person and require minimal equipment.  
The fundamental operation in FFNet is data aggregation, which aims to transmit data among UAVs or to the GS. The data aggregation approach reduces communication costs and bandwidth utilization while obtaining aggregated data. Data aggregation utilizes the concept of many-to-few. A data aggregation protocol describes how the data are gathered, how they are routed to the destination, and when they should be transmitted.  
Therefore, in this study, we developed a cluster-based data aggregation scheme for UAV-based FFNet. This research contributes the following:  
* An effective mechanism designed based on a honey-bee algorithm (HBA) to select optimal unmanned aerial vehicles-cluster head (UAVs-CH).
* The formation of balanced and stable clusters reduces re-affiliation rates.
* Data aggregation algorithm proposed to limit duplicated data communication to the base station (BS).
* Avoids the transmission of unwanted packets to the BS and save FFNet bandwidth.
* Mathematical techniques measure the accuracy and correctness of the proposed scheme.  
The remaining article is structured as follows: Section 2 elaborates on the existing literature. Section 3 discusses the proposed cluster formation and data aggregation schemes. In Section 4, experiment evaluation and simulation outcomes are outlined. In the Section 6, the paper concludes and states future directions.