# Robust Wireless Local Area Networks Based on Compressed Sensing
## 3 Proposed Algorithms
### RTNTC Algorithm
The proposed algorithm is based on the performance of the CM for the following steps:  
* The CM sorts all cases from the packets into classes, by determining whether the predicted value of each packet matched the actual value of each packet. * All the packets in each class are then counted, and the totals are displayed in the CM. * The classification matrix is applied to evaluate the performance of the classification algorithm. The grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid. Figure 2 shows the proposed flowchart for the RTNTC algorithm. We validate the proposed algorithm's performance with the following techniques:  
* Principal Component Analysis (PCA) [33]. * K-Nearest Neighbors (K-NN) [33]. * K-Means clustering algorithm. Then, dimensionality reduction methods followed by sorting the data using the K-NN and K-Means clustering algorithms are evaluated for the performance of the proposed algorithm. The proposed algorithm is established based on the following steps:  
* The proposed RTNTC classification algorithm uses a set of features or parameters of packets to characterize each class, where these features should be relevant to the suitable classes. The set of known objects is called the training set, because it is used by the proposed classification algorithm to learn how to classify different packets. * The proposed RTNTC algorithm is based on two phases to construct a robust classifier. In the training phase, the training set is used to decide how the parameters ought to be weighted and combined, in order to separate the various classes of packets. In the application phase,  
Figure 2: The proposed Real-Time Network Traffic Classification (RTNTC) algorithm. the weights determined in the training set are applied to a set of packet objects that do not have known classes in order to determine what their classes are likely to be in any wireless path or wireless device. * The proposed RTNTC algorithm is based on a nearest-neighbor approach. In this method, one packet simply finds in the N-dimensional feature space the closest packet from the training set to an object being classified. The proposed algorithm has the following advantages: (1) Perform online clustering and classification; (2) Establish strong background to expend the current WLANs to ultra-low-power WLANs; (3) Classify network traffic to allow us to organize traffic of packets into traffic of packets classes or categories on the basis of whether the traffic of packets matches specific criteria; (4) Classify network traffic is the foundation for enabling many QoS features on the WLANs; (5) Classify network traffic to a set of group traffic classes in order to specific QoS treatments; (6) Identify and categorize network traffic into traffic classes for different types of networks, effectively separating network traffic into different paths; (7) Classify network traffic by using class maps and policy maps with the Modular Quality of Service Command-Line Interface (MQC) method in order to improve the level of QoS; (8) The proposed algorithm is easy to implement; (9) The proposed algorithm can also give quite good results if the features are chosen carefully; (10) The proposed algorithm is weighted carefully in the computation of the distance between wireless nodes and wireless devices