# Wireless Sensor Networks for Big Data Systems
## 2 Applications of WSN-Based Big Data Systems  
Before starting a detailed analysis of related work, it is highly desirable to understand which big data applications can be implemented and deployed through WSNs. Since a WSN is usually built to meet application-specific requirements, it is reasonable to review big data applications prior to addressing their technical issues. The following monitoring applications can benefit from using WSNs: smart grids, monitoring human body, and monitoring the environment. Figure 2: Protocol layering of wireless big data systems. In case of smart grids [10], smart sensor networks are introduced in big data systems for energy management. These systems run smart grid applications that include power monitoring, demand-side energy management, coordination of distributed storage, and integration of renewable energy generators. Additionally, techniques used to manage big data generated by sensors and meters are proposed. Moreover, feasible recommendations and practices for smart grid are discussed. The authors in [11] focus on managing data efficiently and extracting required information from the big data system. Because reliability and low latency are two objectives in a smart grid, streaming processing on fog computing architecture is studied for real-time applications over a well-designed platform. The next example is monitoring the human body: wireless body area networks (WBAN). Collection of a vast amount of health and medical data via body sensor networks is presented for big data systems in [12]. To implement body sensor networks, an activity recognition application is required to implement the following functionality: feature extraction and selection, classification, supporting software platforms, and sensor and user authentication. This typical activity recognition procedure is classified and illustrated in Figure 3, which describes the general steps in [12]. [PERSON] et al. [13] present a feasible WBAN big data framework, which uses MapReduce for processing big data in real time, and Hadoop/HBase for storing and analyzing WBAN's big data. Another interesting application of monitoring abnormal conditions of heart rate is proposed by [PERSON] et al. [14], with ZigBee and big data analysis based pulse monitoring system. To avoid missing the pulse signal, two following methods were proposed: (1) the photo-electricity based dynamic continuous heart rate monitoring methods, and (2) comprehensive anti-jamming methods. Using these two methods, a training model based on big data is proposed to improve the physical training level and to create training plans. With regard to data classification and detecting atypical events (anomaly detection), [PERSON] et al. [15] proposed a new feature-based learning system for IoT applications that is based on the theory of distributed compression as well as the sparsity and relativity of data. Next, we consider examples of environmental monitoring, such as big data systems that monitor air quality in industrial workplace buildings. A case study of a big data system that monitors air quality collected by WSN is presented in [16]. Locations include two workshops that are part of a large on-shore logistics base of a regional shipping industry in Norway. The study is conducted to prove the efficiency of  
Figure 3: Typical activity recognition process for inferring activities from WBAN sensors. data analytics and visualization. Substantially, the case study reveals the possibility to monitor worker safety in other high-risk industries, as well as the quality of goods in supply chain management by integrating WSNs and big data systems. Moreover, as a possible monitoring application, cooperative fire security system using human agent robot machine sensor (CFS\({}^{2}\)H) message protocol [17] for a firefight system is presented to provide fast communication and stable collaboration. The stationary WSN node is responsible of generating the data, while a big data center controls the whole system's work in the suggested architecture.