# Big Data Collection in Large-Scale Wireless Sensor Networks
## 1 Introduction
### Background
Among these challenges, we can mention the optimal deployment of sinks, the minimization of sensors' energy consumption, as well as their lifetime [17; 18]. In a Big Data context, with a massive amount of high-speed, regularly changing, and variable data, large-scale WSNs (LS-WSNs) may provide valuable solutions for massive data collection [19]. However, power-aware Big Data gathering in large-scale sensor networks remains, therefore, a challenging research issue. In a computer vision domain, for example, managing a large number of learning instances of images is not an easy task [20]. Nevertheless, in LS-WSNs, real-time data generation and acquisition are achieved by various sensors, such as gathering information on proximity, location, humidity, and movement. Unfortunately, data generated and acquired from individual sensors may not seem significant. However, the global various types of data generated by a number of sensors in LS-WSNs can produce significant Big Data [21]. Moreover, data collection in LS-WSNs requires two important challenges to be addressed, that is, monitoring in large geographic environments with unconnected regions, and power-aware data collection. A number of studies that provide a background to WSNs and Big Data on the one hand, and overviews of data-collection schemes in the context of LS-WSNs on the other, have been proposed in the recent literature. We reviewed most of the latest existing papers related with the topics examined in this paper. In this light, [PERSON] et al. (2014) [22] proposed a review of recent developments in WSNs by surveying the leading research projects, platforms, standards, and technologies related with sensor networks. [PERSON] et al. (2016) [23] as well as [PERSON] et al. (2018) [24] have reviewed recent trendsof mobility management in WSNs while highlighting applications and platforms. [PERSON] et al. (2011) [25] provided an extensive survey and taxonomy of the data-collection process with mobile elements in WSNs. In addition to sensors, there has been a recent tendency to use unmanned aerial vehicles like drones for data acquisition. In this context, swarm intelligence schemes based on bacterial foraging optimization have been successfully used to collect massive data in LS-WSNs [26; 27]. In 2014, [PERSON] et al. [21] proposed a Big Data gathering scheme in densely deployed WSNs that exploit sink mobility. In the same year, [PERSON] et al. [28] proposed a Big Data collection scheme based on the structure-fidelity approach. The authors provided a framework for collecting structured data and to maintain data fidelity in terms of structural similarity in continuous sensing applications in WSNs. After defining what really represents a big sensor data system, [PERSON] et al. (2017) [29] surveyed the progress made in the development and applications of big sensor data research, especially in the context of smart cities. In order to enhance Big Data collection in an IoT system, [PERSON] et al. (2018) [12] proposed a way to collect Big Data in LS-WSNs using mobile collectors to optimize energy consumption. The authors adopted a data-collection scheme using a date mule and mobile access point, which are the mostly used components in data-collection models in sensor networks. According to the literature, we reviewed select research in the context of this study. We established that much less research has been done on Big Data collection with sensor-based systems compared with standard Big Data systems. Standard Big Data technology using stored data is subject to challenges related to the speed capacities of communication networks. Useful technologies, such as Hadoop or MapReduce, combined with cloud-computing technology are actually used to address communication networks by integrating distributed processing. Certainly, future 5G systems, which include a promising cloud-based radio access network model and a more powerful mmWave spectrum, will be more flexible, agile, converged, and useful for automating data analytics with machine-learning or swarm-intelligence algorithms.