# Robust Wireless Local Area Networks Based on Compressed Sensing
## 2 Overview of the Related Works
This method involves using a signal processing technique called CS to reconstruct a signal from a series of sampled measurements, assuming that the signal vector is sparse, which means that it consists of a few non-zero elements, and the remaining element's value is zero. The amount of network traffic in WLANs is rapidly increasing, and traffic patterns are changing due to the widespread use of different kinds of applications for WLANs. Therefore, this is a key to understand traffic characteristics and QoS through network monitoring and measurement in order to ensure effective network management [25; 26]. For this purpose, it is very important to identify the cause of QoS degradation, which leads to a challenging networking issue. Related works on improving the QoS include various methods that have been proposed based on the SNS theorem for network monitoring and network measurement purposes. Based on the existing methods, failure localization and network tomography identify the location of a link/node failure or wireless nodes, and estimate the QoS of each link, then, by sending probe wireless packets between multiple pairs of edge nodes, wireless nodes, and segments. These methods are focused on locating link/node failures or estimating link QoS inside a WLAN with the probe packets, rather than analyzing the end-to-end QoS and identifying the QoS degradation factors, including wireless devices and applications by collecting QoS measurement from various wireless networking devices, on which we are focusing. WLAN's network traffic is the main component for network traffic measurement, network traffic control, and simulation. It is very important to classify network traffic. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into traffic classes in each path, and generate network traffic matrix in order to identify and organize network traffic, which is the foundation for improving the QoS feature. Network traffic classification in WLANs offers an important ability to automatically recognize the application that has generated a stream of packets of a wireless node from the direct and passive observation of the individual packets from other wireless nodes or wireless devices [27; 28]. This ability is instrumental to a number of active wireless nodes and wireless devices in a WLAN. Classification of packets is an important tool that is required to enable any traffic management operations, from differentiating traffic pricing and treatment to security operations [29]. The information provided by traffic classification in a WLAN is extremely important for improving the quality of the wireless network. Traditional traffic classification techniques rely on Shannon SNS procedure, which suffers from huge load of sampling and power consumption [30]. The existing classification methods are based on two main approaches to classifying network traffic in a WLAN:  
* Classifying the packet behavior based on the payload, that is, Payload-Based Classification (PBC). In this method, packets are classified based on the fields of the payload, such as source wireless node, or destination wireless node, or both. * Classification based on statistical algorithms in a WLAN that use statistical analysis of the network traffic behavior, like inter-packet arrival, session time, frequency, packet delay, bandwidth, accuracy, and so on. In order to improve the QoS in a WLAN, we need to divide the network traffic into different classes on a wireless network--identifying some important parameters for each packet to which packets belong is crucial when assigning them to the different class. The fundamental goal of the proposed RTNTC algorithm is to solve difficult wireless network management problems in different wireless network applications. The result of RTNTC algorithm could be applied for Internet service providers (ISPs) and their wireless networking equipment vendors.