Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time, to reduce overfilling by enhancing generalization, and to avoid the curse of dimensionality. Besides, we evaluate and compare each accuracy and performance of the classification model, such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). The highest accuracy of the model is the best classifier. Practically, this paper adopts Random Forest to select the important feature in classification. Our experiments clearly show the comparative study of the RF algorithm from different perspectives. Furthermore, we compare the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination (RFE) to get the best percentage accuracy and kappa. Experimental results demonstrate that Random Forest achieves a better performance in all experiment groups.

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Selecting critical features for data classification based on machine learning methods

Secure routing for internet of things

Energy efficiency is one of the main issues that will drive the design of fog-supported wireless sensor networks (WSNs). Indeed, the behavior of such networks becomes very unstable in node's heterogeneity and/or node's failure. In WSNs, clusters are dynamically built up by neighbor nodes, to save energy and prolong the network lifetime. One of the nodes plays the role of Cluster Head (CH) that is responsible for transferring data among the neighboring sensors. Due to pervasive use of WSNs, finding an energy-efficient policy to opt CHs in the WSNs has become increasingly important. Due to this motivations, in this paper, a modified Stable Election Protocol (SEP), named Prolong-SEP (P-SEP) is presented to prolong the stable period of Fog-supported sensor networks by maintaining balanced energy consumption. P-SEP enables uniform nodes distribution, new CH selecting policy, and prolong the time interval of the system, especially before the failure of the first node. P-SEP considers two-level nodes' heterogeneities: advanced and normal nodes. In P-SEP, the advanced and normal nodes have the opportunity to become CHs. The performance of the proposed approach is evaluated by varying the various parameters of the network in comparison with other state-of-the-art cluster-based routing protocols. The simulation results point out that, by varying the initial energy and node heterogeneity parameters, the network lifetime of P-SEP improved by 31, 29, 20 and 40 % in comparison with SEP, Low-Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection (LEACH-DCHS), Modified SEP (M-SEP) and an efficient modified SEP (EM-SEP), respectively.

P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks

Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network

Multi-sink distributed power control algorithm for Cyber-physical-systems in coal mine tunnels

In mesh networks architecture, it should be permitted to visit the mobile client points. Whereas in mesh networks environment, the main throughput flows usually communicate with the conventional wired network. The so-called gateway nodes can link directly to traditional Ethernet, depending on these mesh nodes, and can obtain access to data sources that are related to the Ethernet. In wireless mesh networks WMNs, the quantities of gateways are limited. The packet-processing ability of settled wireless nodes is limited. Consequently, throughput loads of mesh nodes highly affect the network performance. In this paper, we propose a queuing system that relied on traffic model for WMNs. On the basis of the intelligent adaptivenes, the model considers the influences of interference. Using this intelligent model, service stations with boundless capacity are defined as between gateway and common nodes based on the largest hop count from the gateways, whereas the other nodes are modeled as service stations with certain capacity. Afterwards, we analyze the network throughput, mean packet loss ratio, and packet delay on each hop node with the adaptive model proposed. Simulations show that the intelligent and adaptive model presented is precise in modeling the features of traffic loads in WMNs. Copyright © 2013 John Wiley & Sons, Ltd.

Q. Xu

Papers

GI/Geom/1 queue based on communication model for mesh networks