Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

The internet of things (IoT) is expected to offer a significant impact on the industry domain leading to the concept of industrial IoT (IIoT). The IIoT comprises machine-to-machine (M2M) and communication technologies with data automation and exchange to improve product quality and decrease pro-duction costs. As a consequence, a large amount of data is collected and smartly processed to provide optimal industrial operations. This growing deployment enables adversaries to con-duct potential and destructive cyber-attacks to accomplish their malicious goals. Therefore, intelligent decision-making actions for cyber-attack detection in IIoT are sorely required. To address this challenge, we propose an intrusion detection system (IDS) using deep learning models. Specifically, the proposed system is based on the combination of convolutional neural network (CNN) and long short-term memory (LSTM) that are excellent techniques for intrusion detection and classification due to their ability in classifying main characteristics and their effectiveness in performing faster computations. We adopt the most recent dataset named Edge-IIoTset that contains a real traffic network of IoT and IIoT applications. The proposed model is evaluated in terms of accuracy, precision, false positive rate, and detection cost within binary and multi-class classifications. The obtained results show that our CNN-LSTM model provides better performance and robustness in cyber security intrusion detection for IIoT applications compared to LSTM and traditional machine learning models. Moreover, it outperforms two recent related models in terms of accuracy rate.