# Big Data Collection in Large-Scale Wireless Sensor Networks
## 1 Introduction  
### Background  
According to the United Nations, the world's population is estimated at 7.6 billion in 2018. In addition, more than 2 billion people are regularly connected to the Internet and roughly 5 billion of the world's population use various kinds of connected objects. During the last decade, the amount of global generated data has exploded with the increasing use of connected objects, and especially the Internet of Things (IoT), which continuously produces most heterogeneous data. Cisco reported that, by the end of 2019, the IoT will annually generate more than 500 zettabytes of structured and unstructured data, and that number is expected to exponentially grow [1]. Moreover, a number of industries' forecasts are projecting 50 billion connected devices to the Internet by 2020 [2]. Furthermore, in recent years, with the expansion of the IoT and recent technological advancements, a growing number of intelligent information-aware devices, such as sensors, actuators, smartphones,smart wristbands, tablets, devices based on readers' Radio Frequency Identification (RFID) and Machine-to-Machine (M2M), have led to the exponential growth of generated-data volume [3; 4; 5; 6; 7; 8; 9]. In general, these devices, and especially sensors, are used both in industry and research to collect data in various application contexts, such as healthcare, environmental monitoring, and precision agriculture [10]. The significant advance in research and engineering has introduced novel architectures for data acquisition, transmission, large-scale data storage, and processing. Moreover, the emerging Big Data paradigm, mainly used to describe massive data, is experiencing renewed interest in both the industrial and scientific world [11]. A number of domains, such as the IoT, artificial intelligence, and data science, are benefiting from digital transformation allowed by Big Data. Moreover, Internet-based companies, such as Google, Twitter, Facebook, and Amazon, motivate the current thrust of Big Data research, where real-time data originating from human sources, which include e-mails, online shopping history, as well as tweets, are used in large-scale data mining and for data analysis to produce a number of value-added services [12]. Fundamentally, Big Data may be seen as a very large number of structured, semistructured, and unstructured data of a wide variety of data types that is generated with high velocity and has the potential to help us in understanding hidden information in datasets, and also in managing and organizing these datasets [13]. Usually, the Big Data process relies on four steps: data generation, acquisition, storage, and analysis. Contrary to traditional data, Big Data need more real-time analysis given the fact that they include massive amounts of unstructured data of various types. Nowadays, IoT devices are commonly used in a large spectrum of critical application domains, including eHealth, smart environments, smart cities, smart building, as well as precision agriculture. The massive data collection enabled by IoT technology, leads to the emergence of a huge number of sensor-based applications. Given the virtual abstraction of smart objects enabled by IoT technology, Wireless Sensor Networks (WSNs) have become the most commonly used IoT-based platforms for data collection [14]. Therefore, WSNs are known to be a fundamental component that allowed the development of the IoT system. In such networks, a large number of sensors are deployed in the order of thousands, collaborating to collect and transfer information to the base station [15; 16]. Increasingly, there is a growing trend towards large-scale sensor networks for data collection. Although they represent a new generation of sensor networks, their use is subjected to many constraints, including adaptability with existing methods for network scaling. Several important challenges need to be addressed in order to allow a network to effectively play its role.