# Gathering Big Data in Wireless Sensor Networks by Drone +
## 2 Related Works  
There are several types of research about mobile collectors in WSN. Some of them focused on data gathering in large scale WSN. However, most of them only focus on network energy-saving and do not consider the mobile collector's limitations. Furthermore, they do not consider sensor nodes storing a large volume of data to send to the mobile collector. [PERSON] et al. [13] present an algorithm for data collection based on neural networks. The algorithm first divides the sensor field into several equal squares to create clusters, and each cluster selects a cluster head (CH). Then, the cluster members transmit data to their corresponding CH, directly or using relay nodes. Data fusion is conducted on each CH by using a pretrained neural network when the CH receives data from all its members. Finally, the merged data is sent to the mobile collector. However, the authors do not consider the amount of data to be transmitted by each sensor or the drone battery's limitation. [PERSON] et al. [14] focused on the data gathering problem of maximizing the volume of information obtained by a mobile sink from rechargeable sensor nodes. The sensor nodes store data collected from the environment and harvest energy from the sun or wind. The mobile sink has to move on a straight road within the monitored area to gather the highest possible volume of data, considering sensor nodes with different energy levels. The authors proposed the Distributed Data Gathering Approach (DDGA), a distributed algorithm to obtain the near-optimal solution by generating data gathering routes. However, they do not control the sink movement to optimize energy consumption. [PERSON] et al. [15] developed a scheme for collaborative data collection using multiple mobile nodes (MN) as a sink. The randomly arranged sensor nodes are divided into clusters, and manually the cluster heads (CH) are positioned in the center of each cluster, with higher energy levels. The CHs receive and store data from all nodes in their respective clusters and wait for an MN to send data. This work studies routing strategy and path planning for multiple MNs to reduce network energy consumption. However, the authors do not consider the amount of data sent by each sensor node. Hence, they do not guarantee that the MNs will stay a minimum time inside each CH's radio range. [PERSON] et al. [10] proposed the Data Quality Maximization (DQM) routing protocol to transmit data to a mobile collector. This protocol assumes the mobile sink movement's predictability and selects as gateways the sensor nodes adjacent to the path followed by the collector. The sensor nodes use the Floyd-Warshall algorithm [16] to establish the shortest data path between each node and the closest gateway. The gateways aggregate received data and wait until the mobile is close enough to transmit. The authors consider the mobile sink moving with constant speed; hence the sink does not have time to receive a large volume of data. [PERSON] et al. [17] proposed an algorithm based on virtual grids to divide the WSN into clusters and define routes to deliver data to the mobile sink. Each cluster elects a node as its cluster head, which is responsible for receiving data from the other nodes in the cluster. The cluster heads together form a backbone to relay data to the mobile sink, which moves counterclockwise inside the sensor field. The authors focused on reducing the network energy consumption by defining the data routes from the backbone to the mobile sink in constant movement. [PERSON] et al. [18] proposed algorithms to schedule data mules dispatching for data gathering in WSN. They proved that the problem can be solved by linear time complexity algorithms when the handling time is uniform and that the problem is NP-Complete when the handling time is nonlinear. Hence, the article proposed a linear algorithm and an approximate algorithm (with a guaranteed approximation ratio) to solve the analyzed problem.