# Wireless Sensor Networks for Big Data Systems
## 1 Introduction  
When constructing a big data system, data collection, storage, processing, analysis, and visualization are steps that need to be followed in the said order. In ongoing research on big data systems, the research communities focus on fundamental aspects of dealing with big data: specific platforms, technology, beneficial applications, standards, and best practices (for applications in social web, financial issues, and so on). Moreover, there are many platforms and tools that can implement these functions in the real world. Thus, data-intensive applications are now being developed to benefit from them. [PERSON] et al. [1] present state-of-the-art technologies to deal with the problems of big data systems (specifically, computing infrastructure for data deluge that includes granular computing, cloud computing, bio-inspired computing, and quantum computing). Moreover, seven principles to be applied in big data systems are defined and explained. In addition, [PERSON] et al. [2] provided a general overview of big data systems, focusing on the four phases of value chain of big data. In their survey, they discuss the technical challenges in data generation, acquisition, storage, and analysis as separate phases. Moreover, a new survey of big data was recently published by [PERSON] et al. [3], which addresses new data techniques and platforms, with theaim to select a right combination of technologies according to the technological needs and the specific applications requirements. As reported in the aforementioned studies, data collection is the first step of building a big data system. Among the many available data-generating sources, wireless sensor networks (WSNs) are receiving considerable research attention with regard to environmental monitoring. A WSN consists of a large number of sensor nodes that monitor and record the physical conditions of an environment, and the sensor data are collected at a so-called sink node. WSNs are used to measure environmental conditions: temperature, sound, pollution levels, humidity, wind, and so on. However, the limited capacity of a single node and a narrow wireless link (compared to typical networks) cause problems with delivering the sensor data to the sink node. Nevertheless, an effective data aggregation and in-network processing are beneficial to big data systems. Therefore, there is a need of analyzing research studies that link WSN and big data systems while overcoming the deficiencies of WSN and improving system performance. Figure 1 illustrates a basic structure of a big data system based on a WSN as an example of a fundamental system architecture. As shown in Figure 1, a sink node collects the data from sensor nodes and then delivers the data to a temporary storage for consequent data aggregation. After this step, the aggregated data can be manipulated by a big data framework using the main storage. The transformed data are handled by big data platforms and applications. Special issues of journals and many other papers have been published mechanisms to integrate a WSN as the main source in a big data system using the abovementioned architecture. An example of the special issue is \"Big Data in Ubiquitous Wireless Sensor Networks\" edited by [PERSON] et al. [4]; in this issue, 18 papers are presented to show advances of big data systems with ubiquitous WSNs in terms of computation and storage over cloud computing. Moreover, technical issues for big data in three-dimensional underwater sensor networks, wireless video sensor networks, wireless multimedia sensor networks, and typical WSNs are explained and described. In a comprehensive survey, [PERSON] et al. [5] presented the issues and challenges of data collection through WSNs. The authors focused on energy consumption and heterogeneity of WSNs, which are observed during the analysis of big data in WSN. [PERSON] et al.