# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 1 Introduction  
The use of Internet of Things (IoT) technology to collect up-to-date information from crop fields to protect it from any kind of damage is the main objective of this research. Dynamic clustering is used to collect versatile data under harsh conditions. Clustering is an important way of increasing network lifetime and reliability. Many clustering techniques are proposed, which can be classified into four broad categories: static-sink static-nodes (Low-energy Adaptive Clustering Hierarchy (LEACH) and Hybrid Energy-Efficient Distributed clustering (HEED) [1; 2]), mobile-sink static-nodes (Rendez-vous base routing [3; 4; 5; 6]), static-sink mobile-nodes (cellular network [7]), and mobile-sink mobile-nodes (ad-hoc routing [8]). This research focuses on the mobile sink static nodes clustering method as all agriculture sensors are assumed to be static and we consider a mobile UAV to collect data from crop fields. For static sensor nodes, researchers propose predefined clusters and cluster head schemes to collect data. This type of clustering is not feasible in our case because of the frequent fluctuations of the sensor nodes. The situation becomes more critical, if the cluster head (CH) is down and the whole network becomes unfunctional. In addition, the path of the UAV is dynamic and sensor nodes are unaware of it; in this case, it rarely happens that a predefined CH lays on the path of the UAV and has a good link to it. Network-defined and Rendez-vous base clustering are also proposed in the literature, where all the nodes send periodic updates to maintain up-to-date CH locations orRendez-vous Points (RVPs) from where an UAV can collect data. In this situation, the main drawback is the overhead for all nodes to update the CH locations continuously which results in battery drainage and reduces the network lifetime. Besides, UAVs have to search for and track the network-assigned CHs (Rendez-vous), which will affect the throughput of the system and deflect the UAV from its path. To the best of our knowledge, none of previously published clustering schemes considers the UAV path as a clustering criterion.  
We proposals a dynamic clustering scheme. All the field sensor nodes are initially considered indistinguishable (no potential CH), so the the UAV sends a beacon message to activate all nodes residing in its vicinity, forming a cluster by considering its path and type of required data. The next step is to choose one node as the CH, merge all the cluster data on this point, and locate and connect it with the UAV at some reasonable height and distance.  
IoT for agriculture is proposed in many research articles like [9; 10; 11], where they elaborate how sensors are useful in this field, what are the potential areas of application and how to collect and process information. To the best of our knowledge none of them provide in depth details like how to form clusters, select a CH, proper utilization of UAVs, independent movement of UAVs and energy preservation of the nodes. The most important aspect of our article is the evaluation of the proposed system through intensive simulations and real life experiments.  
The rest of the paper is organized as follows: Section 2 provides a short background of the developed system. Sections 3 and 4 present our dynamic cluster formation and cluster head selection techniques. The proposed three layer architecture is described in Section 5, while the system algorithm is given in Section 6. Different system parameters and their characteristics are defined in Section 7. The developed system in evaluated by using simulation models and IoT devices in Sections 8 and 9, respectively. Finally Section 10 concludes this article.