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The online sequential extreme learning machine with persistent regularization and forgetting factor (OSELM-PRFF) can avoid potential singularities or ill-posed problems of online sequential regularized extreme learning machines with forgetting factors (FR-OSELM), and is particularly suitable for modelling in non-stationary environments. However, existing algorithms for OSELM-PRFF are time-consuming or unstable in certain paradigms or parameters setups. This paper presents a novel algorithm for OSELM-PRFF, named "Cholesky factorization based" OSELM-PRFF (CF-OSELM-PRFF), which recurrently constructs an equation for extreme learning machine and efficiently solves the equation via Cholesky factorization during every cycle. CF-OSELM-PRFF deals with timeliness of samples by forgetting factor, and the regularization term in its cost function works persistently. CF-OSELM-PRFF can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. Detailed performance comparisons between CF-OSELM-PRFF and relevant approaches are carried out on several regression problems. The numerical simulation results show that CF-OSELM-PRFF demonstrates higher computational efficiency than its counterparts, and can yield stable predictions. [ABSTRACT FROM AUTHOR]

With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue of edge networks. In real-world applications of edge computing, sensor nodes usually have different task burdens due to the environmental impact, which results in a dynamic change of the energy consumption rate at different nodes. Therefore, the traditional periodical charging mode cannot meet the nodes charging demand that have dynamic energy consumption. In this paper, we propose a real-time on-demand charging scheduling scheme (RCSS) under the condition of limited mobile charger capacity. In the process of building the charging path, RCSS adequately considers the dynamic energy consumption of different node, and puts forward the next node selection algorithm. At the same time, a method to determine the feasibility of charging circuit is also proposed to ensure the charging efficiency. During the charging process, RCSS is based on adaptive charging threshold to reduce node mortality. Compared with existing approaches, the proposed RCSS achieves better performance in the number of survival nodes, the average service time and charging efficiency. [ABSTRACT FROM AUTHOR]