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On-demand charging of sensor nodes (SNs) in wireless rechargeable sensor networks has garnered immense attention. Existing works with multiple mobile chargers (MCs) overlooked the benefits of partial charging and distributed control in large-scale wireless rechargeable sensor networks. In addition, most of them have not considered the complete heterogeneity of SNs’ energy profiles and idleness of the MCs. In this article, we address the aforesaid issues and present a game theory based distributed mobile charging protocol, called DMCP. We formulate the mobile charging problem as a series of repeated games played among the MCs and represent a game as a 0 − 1 integer linear programming to maximize the total profit of the MCs. In each game, DMCP dynamically determines the factor of charging of the energy-deficit SNs based on their relative criticality and selects the next-to-be-recharged SNs using an efficient bi-objective payoff function. In addition, it utilizes an innovative strategy and a self-enforcing agreement to promote cooperation among the MCs. Through extensive simulations and hypothesis testing, we show that DMCP reduces the charging delay up to 44.04% and enhances the charging coverage and survival rate up to 45.12% and 49.97%, respectively. [ABSTRACT FROM AUTHOR]

Information theory-based feature selection (ITFS) methods select a single subset of features for all classes based on the following criteria: 1) minimizing redundancy between the selected features and 2) maximizing classification information of the selected features with the classes. A critical issue with selecting a single subset of features is that they may not represent the feature space in which individual class labels can be separated exclusively. Existing methods fail to provide a way to select the feature space specific to the individual class label. To this end, we propose a novel feature selection method called class-label specific mutual information (CSMI) that selects a specific set of features for each class label. The proposed method maximizes the information shared among the selected features and target class label but minimizes the same with all classes. We also consider the dynamic change of information between selected features and the target class label when a candidate feature is added. Finally, we provide a general framework for the CSMI to make it classifier-independent. We perform experiments on sixteen benchmark data sets using four classifiers and found that the CSMI outperforms five traditional, two state-of-the-art ITFS (multi-class classification), and one multi-label classification methods. [ABSTRACT FROM AUTHOR]