# Wireless Sensor Networks for Big Data Systems
## 3 Technical Approaches to WSN-Based Big Data Systems
### Data System
#### 3.4.1 Infrastructure  
Because of application-specific requirements and usage, manipulation, and exploitation of the data generated by WSN in the relevant data system for big data are in high demand. Thus, multiple research papers address the research challenges related with data systems.  
First, an infrastructure may include big data tools for gathering, storage, and analysis of data generated by a WSN that monitors air pollution levels in a city [21]. The proposed framework combines Hadoop and Storm for data processing, storage, and analysis, and Arduino-based kits for constructing unique sensor prototypes. In terms of components, the proposed system is composed of three main modules:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Key Feature** & **Advantage** & **Disadvantage** \\ \hline SWV4 WSN [17] & Multi-layer model & Good lifecycle & High management cost \\ \hline
[18] & K-medoid clustering & High energy efficiency & High maintenance cost \\ \hline
[20] & Mobile sinks & Good for network partition & High energy consumption in few clusters \\ \hline
[21] & QoS provisioning & Balanced energy consumption & High complexity \\ \hline
[22] & Name and in-network caching & High energy efficiency & Low energy consumption in many clusters \\ \hline
[23] & Spatial correlation & High energy efficiency & No consideration for temporal correlation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of advantages and disadvantages for infrastructure.  
data acquisition module (DAM), data processing module (DPM), and a messaging tool between DAM and DPM.  
In addition to models of complete systems, there are models of specific components. For example, as for distributed data-centric storage in WSN, [PERSON] et al. [35] propose a big data storage and retrieval algorithm for WSN under nonuniform node distribution. It aims at estimating the additional overhead for the real distribution of sensor nodes. As a technical enhancement, data redundancy among neighbor nodes and a simple routing protocol are proposed and evaluated in the experiments. The main contribution of their work is to reduce the energy consumption and improve the retrieval efficiency with the aid of proximity and routing algorithms, without the aid of GPS. Furthermore, three-tier data mining paradigm for big data in WSN is presented by [PERSON] [36]. The micro-controller, smartphone, and host server tier are responsible for streamlining the sensor data transmission, forming the patterns in sensor data sets, and providing human expertise associated with the patterns. The proposed architecture leads to low-lost data transmission, early time-critical data mining, and urgent response for medical as well as healthcare applications. Additionally, it is interesting to introduce a smartphone device in the tier. Furthermore, a detailed algorithm and approach for data mining are proposed to achieve the goal.  
New requirements for integrating a WSN system and its associated services into a big data system are defined in [37]. To implement this system, a holistic architecture is proposed to consider the flows of the data from sensors. Specifically, a constrained application protocol (CoAP) and a Linux service to integrate Hadoop with HBase datastore are employed over the core architecture on Linux. In the proposed architecture, multiple layers for node possess different capabilities depending on their roles.  
In summary, major approaches in this research category are compared in Table 2 in terms of advantages and disadvantages. Because most of these schemes are evaluated only for prototypes, further validation is required.