# Gathering Big Data in Wireless Sensor Networks by Drone +
## 1 Introduction  
Wireless Sensor Network (WSN) is a computer network composed of small devices called sensor nodes. These devices are spatially distributed to monitor physical or environmental conditions to detect some phenomena located around them. They communicate by radio and can relay packets received from other nodes to forward them to a special node called sink, which is the interface with the users. Hence, these nodes create multihop networks to support many applications that require unattended operations. Traditionally in the literature, the major challenge for this kind of network is collecting, gathering, storage, and processing sensed data in an energy-efficient way since each sensor node has a battery as its energy supply [1]. A well-known strategy to reduce energy consumption and extend the WSN lifetime is adopting a mobile collector. As defined here, the term mobile collectoris correlated to the terms mobile sink [2] or data mule [3] found in the literature. It consists of a particular node able to move within the area monitored by the WSN to gather data from the sensor nodes. Compared with the fixed collector, mobile collectors better distribute the sensor nodes' energy consumption, avoiding premature disconnections. Furthermore, it decreases the energy consumption since the average size of the path followed by each data packet is reduced. The literature presents several works that consider mobile collectors in WSNs, such as [4; 5; 6; 7; 8; 9; 10; 11]. A WSN can generate a very large volume of data due to the high number of devices scattered across vast geographic areas or the relatively significant volume of data stored in each sensor node [8]. According to [9], this is called Big Data, and the traditional data processing algorithms are inadequate to manipulate it. Despite our research efforts, almost all studies in the literature do not guarantee that the mobile collector will stay a minimum time covered by every sensor node's radio to receive a large volume of data. Furthermore, the analyzed works only consider WSN limitations. The mobile collectors have no restrictions; that is, paths followed by these collectors have no length limitation. The collector can stop at any place inside the monitored area, and they have no time limitation to complete their trips for data gathering. This work considers a drone with hovering capability (such as quad-copters) as a mobile collector and WSNs composed of sensor nodes with many data packets to transmit to the mobile collector (Big Data), as in [12]. The problem studied here is how to find a drone tour that minimizes the data gathering time. We focus on finding drone trajectories to reduce the drone's time to gather all data stored in the sensor node memories. This type of drone can hover over any point in the monitored area. However, they have limited flying time due to their batteries. Hence, the algorithms for Big Data Gathering must consider both limitations of the sensor nodes and mobile collectors. Since each sensor node has a large volume of data stored in their memory, the drone must stay at least a minimum time inside each sensor node's radio range to receive all data. Hence, we assume the drone eventually has to hover over some locations to perform the total data gathering. The majority of related works found in the literature consider sensor nodes having only a single data package to transmit. Therefore, the mobile collector does not need to stop for data gathering, and they do not consider the minimum time it must stay inside the sensor nodes' radio range. [PERSON] and [PERSON] [12] proposed two heuristics to define a sequence of points (hovering points) where the drone has to hover to gather data and the subset of sensor nodes that will send its data to the drone over each hovering point. They considered that sensor nodes contain a relatively large volume of data stored in their memories. The drone only receives data packets when it is hovering; there is no transmission when the drone is moving.