Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks

Abstract

A novel multi-class classification method named the voting-cross support vector machine (SVM) method was proposed in this study, for classifying vehicle targets in wireless sensor networks. The advantages and disadvantages of available methods were summarized, after a comparative analysis of commonly used multi-objective classification algorithms. To improve the classification accuracy of multi-class classification and ensure the low complexity of the algorithm for engineering implementation on wireless sensor network (WSN) nodes, a framework was proposed for cross-matching and voting on the category to which the vehicle belongs after combining the advantages of the directed acyclic graph SVM (DAGSVM) method and binary-tree SVM method. The SVM classifier was selected as the basis two-class classifier in the framework, after comparing the classification performance of several commonly used methods. We utilized datasets acquired from a real-world experiment to validate the proposed method. The calculated results demonstrated that the cross-voting SVM method could effectively increase the classification accuracy for the classification of multiple vehicle targets, with a limited increase in the algorithm complexity. The application of the cross-voting SVM method effectively improved the target classification accuracy (by approximately 7%), compared with the DAGSVM method and the binary-tree SVM method, whereas time consumption decreased by approximately 70% compared to the DAGSVM method.

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN

“…In our previous study [ 17 ], three groups of classification methods mentioned above were compared for their effectiveness in classifying vehicle targets in WSNs. KNN, DT, NB, AdaBoost, DAGSVM, and NN-based methods were utilized for classifying the vehicle targets, respectively.…”

“…The Lance–Williams method [ 20 ] is suitable for calculating the distance between sample data for training the classifier. The details of the calculations process is explained in the literature [ 17 ].…”

“…In our prior study [ 17 ], we conducted a comparison of three categories of classification methods for classifying vehicle signals in WSNs. The results show that the NN-based algorithm exhibited superior classification accuracy.…”

Identification Failure Data for Cluster Heads Aggregation in WSN Based on Improving Classification of SVM

“…As the same managing energy in WSN, the SVM was also used as a classifier to identify the level powers of nodes fro prolonging network life [26]. A study of classifying vehicle targets [27] in WSNs was implemented by applying SVM for cross-matching or voting on the car classification through sensor nodes.…”

Hybrid Routing Topology Control for Node Energy Minimization For WSN

“…WSN incorporates the sending and reception of data from the data center or sinks node via a wireless channel [6,9]. With the rise of countries with less established infrastructure, investment in wireless sensor networks (WSN) has become an inescapable consequence because of their low cost and high communication capabilities [10][11][12][13]. There are still substantial challenges in WSN related to network capacity restrictions, increasing data loss and collision rates [7].…”