Data preprocessing is an important step for designing classification model. Normalization is one of the preprocessing techniques used to handle the out-of-bounds attributes. This work develops 14 classification models using different learning algorithms for dynamic selection of normalization technique. This work extracts 12 data complexity measures for 48 datasets drawn from the KEEL dataset repository. Each of these datasets is normalized using min–max and z-score normalization technique. G-mean index is estimated for these normalized datasets using Gaussian Kernel Extreme Learning Machine (KELM) in order to determine the best-suited normalization technique. The data complexity measures along with the best-suited normalization technique are used as an input for developing the aforementioned dynamic models. These models predict the best suitable normalization technique based on the estimated data complexity measures of the dataset. The result shows that the model developed using Gaussian Kernel ELM (KELM) and Support Vector Machine (SVM) give promising results for most of the evaluated classification problems.

Dynamic selection of normalization techniques using data complexity measures

A typical wireless sensor network (WSN) is often deployed in harsh or hostile environment to work in an unattended mode. It is prone to various attacks during routing process as the open wireless medium. Traditional encryption and authentication-based secure mechanisms cannot avoid the attacks from internal compromised nodes, and they also need much resource for complex computing. To conquer these problems, trust-based secure mechanism is usually used in WSNs. However, how to get the behavior evidences for trust assessment is rarely addressed to date. In this paper, we present a game theory-based dynamic behavior monitoring scheme for evidences collection in WSNs. A tradeoff between network security and energy conservation can be achieved. Based on this behavior monitoring scheme, a trust assessment mechanism is proposed, which is further integrated into cluster-based routing protocol. Simulation results show that our trust evaluation scheme can gain a higher network lifetime than full-time behavior monitoring-based trust evaluation scheme, while the performance of network security is not degraded.

A Dynamic Behavior Monitoring Game-Based Trust Evaluation Scheme for Clustering in Wireless Sensor Networks

A wireless sensor network typically consists of large number of low-cost densely deployed sensor nodes that have strictly constrained sensing, computation, and communication capabilities. Because of resource restricted sensor nodes, it is necessary to reduce the amount of information transmission so that average lifetime of sensor and thus the bandwidth consumption are improved. As wireless sensor networks are typically deployed in remote and hostile environments to transmit sensitive data, sensor nodes are in danger of node compromise attacks and security issues like data confidentiality and integrity are terribly necessary. Therefore, in this paper we have explored general security threats in wireless sensor network and made an extensive study to categorize available data gathering protocols and analyze possible security threats on them.

Security issues in wireless sensor networks: A review

Although numerous proposals for electrical and communication topologies for distribution automation had been presented, not all of them are suitable for the use of renewable energy as a source of electricity. Selecting the most appropriate topology requires good knowledge of the particular characteristics and requirements. This paper presents the analysis of the basic characteristics, topologies and the main functions of the distribution network and evaluates the communication architecture suitable for a sustainable energy resources integration. Finally, recommendations for a new architecture are presented as well as for the communication to use.

Scope of electrical distribution system architecture considering the integration of renewable energy in large and small scale

Data sharing is one critical step to implement data fusion, and how to encourage sensors to share their data is an important issue. In this paper, we propose a reputation-based incentive framework, where the data sharing stimulation problem is modeled as an indirect reciprocity game. In the proposed game, sensors choose how to report their results to the fusion center and gain reputations, based on which they can obtain certain benefits in the future. Taking the sensing and fusion accuracy into account, reputation distribution is introduced in the proposed game, where we prove theoretically the Nash equilibrium of the game and its uniqueness. Furthermore, we apply the proposed scheme to the cooperative spectrum sensing. We show that within an appropriate cost-to-gain ration, the optimal strategy for the secondary users is to report when the average received energy is above a given threshold and keep silence otherwise. Such an optimal strategy is also proved to be a desirable evolutionarily stable strategy. Finally, simulation results are shown to verify the theoretical results and demonstrate that compared with the existing schemes, our proposed scheme achieves better operating characteristic curve and higher system throughput with convincing performance on fairness.

Indirect-Reciprocity Data Fusion Game and Application to Cooperative Spectrum Sensing

Until recently use of sensors to collect sensitive parameters had only few risk factors such as sensor malfunction, uncertainty of data collection, or missing data coverage. Some issues in these categories were addressed by multi-sensor application or sensor fusion. However, advancement in technology and advent of wireless sensors in a networked environment, brought along a new risk factor related to the security in wireless sensor network. Therefore security in Wireless Sensor Network WSNs is challenging and critical to the functionality of the networked sensors. This is very important in cases of highly secure environment, especially in industrial, military, and medical domains. The standard WSN protocols focus on energy efficiency; transmission efficiency, and routing. WSN is known for limitations on hardware and software and for resources-constrained in general. An adaptive model of security that meets requirements and constraints in WSN is Intrusion Detections. This article investigates security in WSN and presents a design process for achieving optimum security based on requirements and constraints in WSNs. Further, comparative results between a proposed technique and other security current approaches are discussed.

A security model for wireless sensor networks

The objective was to train several Artificial Neural Networks (ANNs) with different training functions in order to gain an understanding of the effect of dataset complexity on performance. The utilization of varying training functions permitted ANN diversity; and allowing for enhanced diagnostic reasoning in classification. This improvement is achieved by expediting training stage, calibrating classification. The proposed technique is applied to a number of dataset to verify performance improvements. Particular application of the proposed technique is demonstrated by applying methodology for medical diagnostics.

Effects of data complexity on the intelligent diagnostic reasoning

The aim of this paper is to present a model for solving multi-objective optimization problems with high dimensionality using distributed computing. It demonstrates feasibility of the model by applying Bees Algorithm to solve Economic Dispatch problem using ACEnet Grid resources.

Bio-inspired solution to Economic Dispatch problem using distributed computing

Due to sporadic climate change and global warming, world have signed international protocols promising to reduce their nation's emissions. This study focuses on the application of the bees algorithm, embedded with an artificial neural network, to determine practical yearly reductions for minimizing oil, natural gas, and coal emissions as by-products of energy consumption in Canada's manufacturing sector based on the Copenhagen Targets for Canada for 2020.

Achieving CO 2 emission targets for energy consumption at Canadian manufacturing and beyond; using hybrid optimization model

Achieving CO2 emission targets for energy consumption at Canadian manufacturing and beyond; using Hybrid Optimization Model

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