# Big Data Collection in Large-Scale Wireless Sensor Networks
## 6 Challenges of Big Data Collection in LS-WSNs
In addition, in this type of architecture, the most common problem is called the access point problem [122], that is to say, that sensor nodes that are in proximity to the sink are rapidly depleting their energy compared to other sensor nodes. This is justified by the high concentration of data traffic to the sink. Thus, network coverage rate is affected, with an increased risk of sink disconnection. Furthermore, to overcome the disadvantages of multihop communication, other works propose to multiply the number of sinks within a network. Thus, a WSN can have several static sinks [120]. In this architecture, in general, these sinks have more hardware resources compared to other sensor nodes [94]. They are used to collect data from other sensor nodes, and to reduce the energy consumed in transmissions [120]. The data are then sent from the sinks to the end user via an Internet or satellite connection [94]. In this approach, the problem is the choice of a sink compared to others. Thus, in most jobs, metrics such as distance are used to determine which sink is closest to the sensor node to which the data will be sent. However, the issue of the optimal deployment of sinks remains a big problem, because it is difficult to choose the right place to deploy the sinks in order to balance the exchanges between nodes and sinks [122; 102]. Even though static sinks are deployed in optimal locations, we still have the access point problem, which is that the nodes near the sinks consume more power. Sinks can be disconnected from the rest of the network if the surrounding nodes exhaust their battery. Thus, the optimal position of the sinks ensures good coverage of the area of interest, but not permanent coverage [93]. Another study [81] proposed an approach based on sink mobility. In addition to saving energy consumption at the sensor level of sink mobility, it also satisfactorily reduces increased data latency [15; 24; 40]. By choosing to organize the cluster network, this reduces the time spent in the sensor nodes and therefore increases the frequency of its visits to the cluster heads. In addition, using a method called service cycle can further reduce power consumption [102; 122]. Furthermore, the problem of storing and computing very large amounts of collected data from LS-WSNs needs more attention. In this light, some companies, such as Nokia and Apple, integrate a number of sensors in their mobile phones and other smart devices on the Internet. This leads to the collection of petabytes of data each year. Certainly, some distributed database solutions, such as Google's Bigtable, Amazon's Dynamo, Windows' Azure Storage, and Apache Hadoop, can achieve the storage of such large amounts of data. However, unless these Big Data are associated with programming models, such as Mapreduce, the existing distributed database solutions cannot process the large amount of data generated by sensors from LS-WSNs. Unfortunately, a number of works are focused on sensors' interconnection and power-efficient WSNs [21; 36; 121]. In spite of the number of interesting challenges that should be addressed in sensor networks, large amounts of data collection, storage, and processing schemes in LS-WSNs should be developed.