• Energy Research

AbstractDespite the great efforts to secure wireless sensor network (WSN), the dynamic nature and the limited resources of sensor nodes make searching for a secure and optimal network structure an open challenge. In this paper, we propose a novel encryption schema based on Elliptic Curve Cryptography (ECC) and homomorphic encryption to secure data transmission in WSN. The proposed encryption schema is built upon GASONeC algorithm (Elhoseny et al., 2014) that uses genetic algorithm to build the optimum network structure in the form of clusters. ECC is used to exchange public and private keys due to its ability to provide high security with small key size. The proposed encryption key is 176-bit and is produced by combining the ECC key, node identification number, and distance to its cluster head (CH). To reduce energy consumption of CH, homomorphic encryption is used to allow CH to aggregate the encrypted data without having to decrypt them. We demonstrated that the proposed method is capable to work with different sensing environments that need to capture text data as well as images. Compared with the state-of-the-art methods, our experimental results demonstrated that our proposed method greatly improve the network performance in terms of lifetime, communication overhead, memory requirements, and energy consumption.

Abstract Data from many real-world applications can be high dimensional and features of such data are usually highly redundant. Identifying informative features has become an important step for data mining to not only circumvent the curse of dimensionality but to reduce the amount of data for processing. In this paper, we propose a novel feature selection method based on bee colony and gradient boosting decision tree aiming at addressing problems such as efficiency and informative quality of the selected features. Our method achieves global optimization of the inputs of the decision tree using the bee colony algorithm to identify the informative features. The method initializes the feature space spanned by the dataset. Less relevant features are suppressed according to the information they contribute to the decision making using an artificial bee colony algorithm. Experiments are conducted with two breast cancer datasets and six datasets from the public data repository. Experimental results demonstrate that the proposed method effectively reduces the dimensions of the dataset and achieves superior classification accuracy using the selected features.

Abstract The potential of long short-term memory network on ultra-short term wind speed forecast attracted attentions of researchers in recent years. Extending a probabilistic long short-term memory network model to provide an uncertainty estimation than to make a point forecast is more valuable in practice. However, due to complex recurrent structure and feedback algorithm, large scale ensemble forecast based on resampling faces great challenges in reality. Instead, a reliable forecast method needs to be devised. Gaussian process regression is a probabilistic regression model based on Gaussian Process prior. It is reasonable to integrate Gaussian process regression with long short-term memory network for probabilistic wind speed forecast to leverage the superior fitting ability of the deep learning methods and to maintain the probability characteristics of Gaussian process regression. Hence, avoid the repeated training and heavy parameter optimization. The method is evaluated for wind speed forecast using the monitoring dataset provided by the National Wind Energy Technology Center. The results indicated that the proposed method improves the point forecast accuracy by up to 17.2%, and improves the interval forecast accuracy by up to 18.5% compared to state-of-the-art models. This study is of great significance for improving the accuracy and reliability of wind speed prediction and the sustainable development of new energy sources.

In this article, we study the subspace function granularity and present a method to estimate the sharing distance and the optimal population size. To achieve multimodal function optimization, niching techniques diversify the population of Evolutionary Algorithms (EA) and encourage heterogeneous convergence to multiple optima. The key to a successful diversification is effective resource sharing. Without knowing the fitness landscape, resource sharing is usually determined by uninformative assumptions on the number of peaks. Using the Probably Approximately Correct (PAC) learning theory and the ∊-cover concept, a PAC neighborhood for a set of samples is derived. Within this neighborhood, we sample the fitness landscape and compute the subspace Fitness Distance Correlation (FDC) coefficients. Using the estimated granularity feature of the fitness landscape, the sharing distance and the population size are determined. Experiments demonstrate that by using the estimated population size and sharing distance an Evolutionary Algorithm successfully identifies multiple optima.