# Robust Wireless Local Area Networks Based on Compressed Sensing
## 2 Overview of the Related Works
It is a key tool for network operators to see what is flowing over their wireless networks in order to support their various networking goals and protocols. RTNTC algorithm is a core part of wireless network automated intrusion detection systems to detect patterns indicative of denial of service attacks, trigger automated re-allocation of wireless network resources for priority routing algorithms, or identify different paths for the wireless networks. The conventional classification approaches have traditionally relied on the SNS procedure. The SNS theory demonstrates that analog and digital signals, images, and videos can be exactly recovered from a set of uniformly spaced samples taken at SNS, which is twice the highest frequency in the signal of interest. Therefore, a signal must be sampled at least twice its bandwidth in order to be represented without error on the receiver side. The traditional approaches based on SNS have two major drawbacks. First, they generate huge intolerable samples for many applications with a large bandwidth. Second, even for low signal bandwidths, they produce a large amount of redundant digital samples. That is why it is desirable to reduce the number of acquired samples by utilizing sparsity. The main motivation is to replace the conventional sampling and reconstruction operation with a general random linear measurement process and an optimization scheme in order to recover the original signal from a small number of random measurements. Ensemble clustering techniques involve the generation of clusters using more than one process in order to identify an ultimate clustering by consensus. Many of the existing methods are expensive, which restricts their applications for clustering of large and highly dimensional datasets in the WLANs. The main drawbacks for the existing clustering and classification algorithm are (1) they are not able to provide real-time clustering and classification; (2) the classification is performed offline, due to lack performance of clustering method; (3) they are not effective for wireless communication systems because they consume huge power; (4) they suffer from low processing time and speed. Therefore, we need to design a new classification approach based on CS for the different applications, such as WLANs, WPANs, WMANs, and WANs. The CS method involves using a signal processing technique to reconstruct a signal from a series of sampled measurements, assuming that the signal vector is sparse to the small number of non-zero samples. In addition, classifying network traffic enables us to allocate network resources to deliver optimal performance for different types of wireless networks. RTNTM is a strong tool which we need, not only to fix network problems in time, but also to prevent network failure, to detect inside and outside threats, to improve the quality of our networking, and make good decisions for network planning for different wireless networking applications. As the wireless networking systems and applications keep growing, the need of network monitoring, control, and analysis tools have been increasing. Monitoring wireless network traffic in different applications, such as WLAN, WPAN, WMAN, and WAN, has become increasingly challenging in terms of number of hosts, protocol proliferation, and probe placement topologies. Network monitoring and management in different types of wireless networks, especially in WLANs, have become more and more important in a modern complicated network, including WLAN, WPAN, WMAN, and WAN networks. Based on a new application of wireless networks, there is a critical need to design a new network monitoring tool for both LAN and WAN, in order to maintain the network system stability and availability, such as to fix network problems on time or to avoid network failure, to ensure the network security strength, and to make good decisions for network planning.