Limited energy in each node is the major design constraint in wireless sensor networks (WSNs), especially in mine tunnel scenario where the WSNs are required to work perpetually. To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. To keep the sensor nodes working perpetually, one fundamental question is how to design the charging scheme. Considering the special tunnel scenario, this paper proposes a Complete Feasible Charging Strategy (CFCS) to ensure the whole WRSNs is working perpetually. We divide the whole WRSN into several subnetworks and use several mobile chargers (MCs) to charge every subnetwork periodically and orderly. For a subnetwork, we formulate the main problem as a charging time distribution problem. A series of theorems are deduced to restrict the charging configurations, and a group nodes mechanism is proposed to expand the scale of the WRSNs. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results demonstrate which of the CFCS boundary theorems is correct and that our proposed CFCS can keep the WRSNs working perpetually. Furthermore, our Nodes-Grouped mechanism can support more nodes in WRSN compared to the state-of-the-art baseline methods.
Abstract-Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses two evolutionary computing approaches to optimize the parameters of SVM: particle swarm optimization (PSO) and genetic algorithm (GA). And we combine the two evolutionary methods with SVM to choose appropriate subset features and SVM parameters, experimental results demonstrate that the classification accuracy surpass traditional grid searching approach. Also the paper compares PSO with GA method based SVM classification and they have similar results.
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