# Big Data Collection in Large-Scale Wireless Sensor Networks
## 6 Challenges of Big Data Collection in LS-WSNs  
With increasingly miniaturized and integrated sensors, a new paradigm of sensor networks is emerging in LS-WSNs. These networks are based on the cooperation of a set of nodes to perform various operations, such as monitoring and data collection, on the environment in which they are deployed. The term 'large-scale' refers to the fact that these are networks deployed over a large area with high sensor density. Consequently, all techniques and methods that can be used in so-called small WSNs cannot be directly used on these networks. They must undergo a modification to adapt to the extension of the network range. In general, a sensor is a resource-limited device whose energy resources and sensors are deployed in most cases in hard-to-reach areas, making it difficult or impossible to change the batteries. The management of energy resources is a strategic issue to increase the duration of a sensor network [24; 46; 108]. Even though WSNs have been used in a number of applications, data collection is the main activity of WSNs, whether LS-WSNs or not. For researchers, this operation consists of finding solutions to maximize data collection, and for others to minimize the energy consumption at the level of the sensors. In the literature [33; 45], it appears that of all the basic functions of a sensor, communication is the most energy-consuming activity. This energy consumption largely depends on communication distance. Indeed, to reduce this consumption and extend the life of the sensors, and therefore that of the network, several techniques and methods have been developed in recent years. In most cases, these methods relax several constraints, such as latency, coverage, and QoS, as well as the Quality of Experience (QoE) of the network in favor of extending network lifetime. In addition, the requirements specified by data-collection applications in LS-WSNs also lead to a number of issues that need to be considered. Among them, WSN coverage is an essential issue in sensor network deployment, which must make sure that sensors deployment in a given area of interest may need to allow an acceptable coverage, especially for certain WSNs' applications. Moreover, in certain cases, sensors should be located in particular locations in order to more accurately gather data. This fact is important for industrial 3D sensor networks or sensors deployed in aircrafts [112; 113]. Furthermore, the way and rate of acquiring data cannot be the same for different kinds of data, such as vibration, temperature, movement, and humidity [10; 114; 115]. These issues, if not handled well, lead to an unbalanced energy consumption resulting to power wastage and a short network lifetime. Otherwise, delivering data to the base station while minimizing the rate of data loss is also a challenge. Some works focus on network structuring and data aggregation [36; 80; 100; 109; 116; 117], while others rely on routing techniques to reduce the energy consumption of sensor nodes [12; 103; 118; 119; 46]. However, operations like data aggregation, compression, and fusion remain difficult to apply in the case of large  
Figure 18: Flowchart of data collection in LS-WSNs. amounts of structured or unstructured data. It is, therefore, necessary to develop novel schemes for data collection in LS-WSNs. Moreover, in terms of network structuring, hierarchical networks coupled with clustering techniques based on multihop communication seem to be the appropriate solution for LS-WSNs. These techniques reduce intranode transmission distance in the routing of the collected data. This is economical in terms of energy consumption compared to direct communication, that is to say, that each sensor node directly routes these data to the sink without a relay. In contrast, clustering results in higher latency than in flat structuring [120; 121].