# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 2 Background  
Data collection from Wireless Sensor Networks (WSNs) installed in farm fields faces many challenges like extreme climatic conditions characterized by high temperatures, dry air, dust/sand storms, lack of infrastructure, remote and huge geographical locations, etc. We propose a dynamic data collection mechanism to overcome the above mentioned challenges. In this study, smartness in agriculture is achieved in four steps:  
1. Deployment of heterogeneous sensor nodes: a large number and wide range of heterogeneous sensors are used to monitor different parameters related to crop, soil and environment. 2. Use of Unmanned Aerial Vehicles (UAVs): UAVs are utilized to build the communication infrastructure between sensing devices and end-users. 3. Dynamic clustering: Clustering is the most important aspect of our study. It is the process used to arrange different sensing devices in groups according to geographical area, required data, path of the UAV, communication limitations, similarities or any other criterion. 4. Dynamic cluster head selection: Once available alive sensors have arranged themselves in a cluster formation then the next challenge is how to select a node as CH which will collect all the data from neighboring nodes and transmit it to the UAV. Selection of the CH is a tricky task, as the node having the best specifications and more suitable for the UAV (i.e., near to the UAV path) has to be selected as CH. The developed system has many advantages over conventional ones. First of all it is a quickly deployed handy system which can be employed anywhere without any existing infrastructure. A farmer can collect needed data from an area of interest by using this system as all the data from the whole area is rarely needed. The mission is established for the UAV in advance in the form of waypoints to collect data from selective sensors and area. Network life can be improved massively as we propose the use of dynamic clustering, hence there is no need to discover and maintain data routes continuously. In WSNs, the node energy is mostly exhausted in broadcasting periodic updates to keep the network alive all the time. In our system, the network is formed only when data really needs to be collected and only selected nodes will participate. The use of UAVs also helps us to prolong the network life, as it enables us to collect data by visiting the IoT nodes at a suitable pre-arranged height. We are introducing a dual frequency communication system to further reduce the energy initialization. A low frequency (e.g., 433 MHz) is used to locate, navigate and shake-hand with sensor nodes while the high bandwidth and more energy demanding transceiver (e.g., 2.4 GHz) is switched on only when data need to be transmitted. Sensor nodes don't have GPS modules and their reduced working loads are two other factors helping to optimize the node energy. Another important benefit of our proposed system is that it can survive and work well under harsh conditions, as sensor nodes are often fluctuating due to bad weather, and becoming covered by sand, water, mud or plant follicles. Pre-defined CHs and routes are not feasible, and in dynamic clustering only available nodes participate and the best among them will act as CH and formulate a temporary network to deliver the required data to the UAV. The life cycle of the proposed system is composed of seven steps, as shown in Figure 1. The UAV, which acts like data mule and the means of communication among the sensors, is the main part. The system life cycle starts when the UAV initiates the process of data gathering by sending a beacon message in step-1. Type of nodes, suggested data collection height and threshold to limit number of UAV-node connections are mentioned in this beacon. The nodes addressed in the UAV beacon are activated in step-2. In step-3 clusters are formed in the path of the UAV to preserve its predefined path.