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Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning machine (ELM) which efficiently reduces redundant and erroneous data. Mahalanobis distance-based radial basis function is applied to the projection stage of the ELM to reduce the instability of the training process. Kalman filter is also used to filter the data at each sensor node before transmitted to the cluster head. Computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy of the data and energy efficiency of WSN. [ABSTRACT FROM AUTHOR]

As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters. [ABSTRACT FROM AUTHOR]

The amount of data is increasing explosively, and many in-memory-based database management systems have been developed to efficiently manage data in real time. However, these in-memory databases mainly use DRAM main memory, which raises problems due to price and energy consumption. To mitigate these problems, we propose a hybrid main memory structure based on DRAM and NAND flash that is cheaper and consumes less energy than DRAM. The proposed system incorporates a prefetching mechanism in last-level cache based on regression analysis to handle irregular memory access from the in-memory application and a migration technique based on clustering between DRAM and NAND flash to mitigate NAND flash slow access latency, which could otherwise significantly degrade system performance. We experimentally confirmed approximately 58% and 51% execution time and energy improvement compared with using DRAM alone. We also compared existing prefetching models without migration to evaluate the proposed prefetching and migration techniques and showed approximately 24% and 23% improvement for execution time and an energy consumption, respectively. [ABSTRACT FROM AUTHOR]