# Wireless Sensor Networks for Big Data Systems
## 3 Technical Approaches to WSN-Based Big Data Systems
### Network System  
#### 3.3.1 Infrastructure  
In this research area, most work is conducted by proposing either network architectures or communication protocols. First, the authors propose structural construction of the WSN based on big data processing called service-oriented architecture and virtualization cloud for WSN (SVC4 WSN) [23] and simulates it numerically through comparison with multi-hop direct forwarding for local wireless sensor network (MDF4 LWSN) architecture. The SVC4 WSN consists of four layers: a large-scale WSN, gateway, cloud center, and users (as shown in Figure 5). In this architecture, there are two critical issues: congestion caused by the big data, and communication latency. To deal with these issues, flexible and multi-layer data processing and storage models based on cloud computing are proposed. Another challenging task for gathering big data in a densely distributed sensor network with high energy efficiency is addressed in [24]. The method can be used to determine the sink node's trajectory and data-gathering through clustering. Unlike the typical clustering scheme, K-medoid clustering in six steps is proposed to keep energy consumption balanced in continuous iterations. [PERSON] et al. [25] proposed a similar approach based on clustering. In their work, the mobility of a sink node was selected as an effective solution to manage big data collection. Moreover, a framework to leverage the correlation between sets of active sensor nodes was presented. Another approach to utilize a mobile data collector is presented in [26], where two different approaches are suggested: data collection using data mule (MULE) and sensor network with mobile access point (SENMA). These approaches are characterized by the number of hops that are required to handle unexpected network partition during mobile data collection. [PERSON] [27] conducted a study that deals with the routing protocol over cluster architecture to deliver multimedia data in WSN. Figure 4: Classification of related work. It aims to reduce network congestion, in order to improve the reliability of data transmission, as well as reduce packet loss rate. To achieve these goals simultaneously, an efficient traffic load balancing algorithm is proposed to ensure balanced energy consumption within the network. In addition to data collection, the data aggregation scheme for big data is discussed in [28] through the information-centric networking (ICN) approach, where data are retrieved by names and in-network caching. The proposed framework operates according to the following steps: (1) the network is initialized and the communication nodes are clustered using low-energy adoptive clustering hierarchy (LEACH) protocol; (2) collected data is aggregated to the cluster head memory; (3) an aggregatable name-based routing (ANBR) method retrieves the data and forwards it to the data center. From another point of view, another architecture for WSN is proposed to prevent excessive energy consumption on a sensor node in case of the high redundancy of sensed data. A framework called structure fidelity data collection (SFDC) [29] leverages the spatial correlations between the nodes by reducing the number of active sensor nodes while maintaining a low structural distortion of the collected data. A node's duty cycle is controlled by a structural distortion depending on the image quality assessment approach. Thus, the data fidelity in terms of structural similarity in the continuous sensing applications for WSNs can be accomplished by SFDC. In addition, [PERSON] et al. [30] proposed a method of compressing collected data at the first level as well as eliminating redundant data generated by the neighboring node at the second level. The proposed scheme adapts clustering methods (EKmeans and TopK) to decrease the communication costs and enhance data mining in WSN.