Shahaboddin Shamshirband

A survey on indexing techniques for big data: taxonomy and performance evaluation

The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or ‘big data.’ Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques’ performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.

Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks

Abstract Owing to the distributed nature of denial-of-service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a game theoretic method is introduced, namely cooperative Game-based Fuzzy Q-learning (G-FQL). G-FQL adopts a combination of both the game theoretic approach and the fuzzy Q-learning algorithm in WSNs. It is a three-player strategy game consisting of sink nodes, a base station, and an attacker. The game performs at any time a victim node in the network receives a flooding packet as a DDoS attack beyond a specific alarm event threshold in WSN. The proposed model implements cooperative defense counter-attack scenarios for the sink node and the base station to operate as rational decision-maker players through a game theory strategy. In order to evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using NS-2 simulator. The model is subsequently compared against other existing soft computing methods, such as fuzzy logic controller, Q-learning, and fuzzy Q-learning, in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed model׳s attack detection and defense accuracy yield a greater improvement than existing above-mentioned machine learning methods. In contrast to the Markovian game theoretic, the proposed model operates better in terms of successful defense rate.

Soft computing approaches for forecasting reference evapotranspiration

The GP, SVM-FFA, ANN and SVM-Wavelet modeling of ET0 was reported.SVM-Wavelet had the smallest RMSE of 0.233mmday-1 in testing phase.The ANN model had the largest RMSE of 0.450mmday-1.SVM-Wavelet model was found to perform better than the GP, SVM-FFA and ANN models. Accurate estimation of reference evapotranspiration (ET0) is needed for planning and managing water resources and agricultural production. The FAO-56 Penman-Monteith equation is used to determinate ET0 based on the data collected during the period 1980-2010 in Serbia. In order to forecast ET0, four soft computing methods were analyzed: genetic programming (GP), support vector machine-firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine-wavelet (SVM-Wavelet). The reliability of these computational models was analyzed based on simulation results and using five statistical tests including Pearson correlation coefficient, coefficient of determination, root-mean-square error, absolute percentage error, and mean absolute error. The end-point result indicates that SVM-Wavelet is the best methodology for ET0 prediction, whereas SVM-Wavelet and SVM-FFA models have higher correlation coefficient as compared to ANN and GP computational methods.

Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks

Abstract Due to the distributed nature of Denial-of-Service attacks, it is tremendously challenging to identify such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a bio-inspired method is introduced, namely the cooperative-based fuzzy artificial immune system (Co-FAIS). It is a modular-based defense strategy derived from the danger theory of the human immune system. The agents synchronize and work with one another to calculate the abnormality of sensor behavior in terms of context antigen value (CAV) or attackers and update the fuzzy activation threshold for security response. In such a multi-node circumstance, the sniffer module adapts to the sink node to audit data by analyzing the packet components and sending the log file to the next layer. The fuzzy misuse detector module (FMDM) integrates with a danger detector module to identify the sources of danger signals. The infected sources are transmitted to the fuzzy Q-learning vaccination modules (FQVM) in order for particular, required action to enhance system abilities. The Cooperative Decision Making Modules (Co-DMM) incorporates danger detector module with the fuzzy Q-learning vaccination module to produce optimum defense strategies. To evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using a network simulator. The model was subsequently compared against other existing soft computing methods, such as fuzzy logic controller (FLC), artificial immune system (AIS), and fuzzy Q-learning (FQL), in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed method improves detection accuracy and successful defense rate performance against attacks compared to conventional empirical methods.

Incremental proxy re-encryption scheme for mobile cloud computing environment

Due to the limited computational capability of mobile devices, the research organization and academia are working on computationally secure schemes that have capability for offloading the computational intensive data access operations on the cloud/trusted entity for execution. Most of the existing security schemes, such as proxy re-encryption, manager-based re-encryption, and cloud-based re-encryption, are based on El-Gamal cryptosystem for offloading the computational intensive data access operation on the cloud/trusted entity. However, the resource hungry pairing-based cryptographic operations, such as encryption and decryption, are executed using the limited computational power of mobile device. Similarly, if the data owner wants to modify the encrypted file uploaded on the cloud storage, after modification the data owner must encrypt and upload the entire file on the cloud storage without considering the altered portion(s) of the file. In this paper, we have proposed an incremental version of proxy re-encryption scheme for improving the file modification operation and compared with the original version of the proxy re-encryption scheme on the basis of turnaround time, energy consumption, CPU utilization, and memory consumption while executing the security operations on mobile device. The incremental version of proxy re-encryption scheme shows significant improvement in results while performing file modification operations using limited processing capability of mobile devices.

Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology

The monthly ET0 data were obtained by the Penman-Monteith method.ANFIS was applied for selection of the most influential ET0 parameters.Tmin, ea and sunshine hours are the most influential for ET0 estimation.Variables selection with ANFIS improves ET0 predictive accuracies.The ANFIS model can be used for ET0 estimation with high reliability. The adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential reference evapotranspiration (ET0) parameters. This procedure is typically called variable selection. It is identical to finding a subset of the full set of recorded variables that illustrates good predictive abilities. The full weather datasets for seven meteorological parameters were obtained from twelve weather stations in Serbia during the period 1980-2010. The monthly ET0 data are obtained by the Penman-Monteith method, which is proposed by Food and Agriculture Organization of the United Nations as the standard method for the estimation of ET0. As the performance evaluation criteria of the ANFIS models the following statistical indicators were used: the root mean squared error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2). Sunshine hours are the most influential single parameter for ET0 estimation (RMSE=0.4398mm/day). The obtained results indicate that among the input variables sunshine hours, actual vapor pressure and minimum air temperature, are the most influential for ET0 estimation. The maximum relative humidity and maximum air temperature are the most influential optimal combination of two parameters (RMSE=0.2583mm/day).

Extreme learning machine based prediction of daily dew point temperature

An ELM-based model is proposed to predict daily dew point temperature.Weather data for two Iranian stations with different climate conditions were used.ELM model enjoys much greater predictions capability than SVM and ANN.Application of the proposed ELM model would be highly promising and appealing. The dew point temperature is a significant element particularly required in various hydrological, climatological and agronomical related researches. This study proposes an extreme learning machine (ELM)-based model for prediction of daily dew point temperature. As case studies, daily averaged measured weather data collected for two Iranian stations of Bandar Abass and Tabass, which enjoy different climate conditions, were used. The merit of the ELM model is evaluated against support vector machine (SVM) and artificial neural network (ANN) techniques. The findings from this research work demonstrate that the proposed ELM model enjoys much greater prediction capability than the SVM and ANN models so that it is capable of predicting daily dew point temperature with very favorable accuracy. For Tabass station, the mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R) achieved for the ELM model are 0.3240?C, 0.5662?C and 0.9933, respectively, while for the SVM model the values are 0.7561?C, 1.0086?C and 0.9784, respectively and for the ANN model are 1.0324?C, 1.2589?C and 0.9663, respectively. For Bandar Abass station, the MABE, RMSE and R for the ELM model are 0.5203?C, 0.6709?C and 0.9877, respectively whereas for the SVM model the values are 1.0413?C, 1.2105?C and 0.9733, and for the ANN model are 1.3205?C, 1.5530?C and 0.9617, respectively. The study results convincingly advocate that ELM can be employed as an efficient method to predict daily dew point temperature with much higher precision than the SVM and ANN techniques.

A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

Forecasting lake level at various horizons is reported here.SVM coupled with FA was used to forecast lake level.Results demonstrate the SVM-FA superiority. Forecasting lake level at various horizons is a critical issue in navigation, water resource planning and catchment management. In this article, multistep ahead predictive models of predicting daily lake levels for three prediction horizons were created. The models were developed using a novel method based on support vector machine (SVM) coupled with firefly algorithm (FA). The FA was applied to estimate the optimal SVM parameters. Daily water-level data from Urmia Lake in northwestern Iran were used to train, test and validate the used technique. The prediction results of the SVM-FA models were compared to the genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results showed that an improvement in the predictive accuracy and capability of generalization can be achieved by the SVM-FA approach in comparison to the GP and ANN in 1 day ahead lake level forecast. Moreover, the findings indicated that the developed SVM-FA models can be used with confidence for further work on formulating a novel model of predictive strategy for lake level prediction.

BSS: block-based sharing scheme for secure data storage services in mobile cloud environment

For the last few years, academia and research organizations are continuously investigating and resolving the security and privacy issues of mobile cloud computing environment. The additional consideration in designing security services for mobile cloud computing environment should be the resource-constrained mobile devices. The execution of computationally intensive security services on mobile device consumes battery’s charging quickly. In this regard, the study presents a novel energy-efficient block-based sharing scheme that provides confidentiality and integrity services for mobile users in the cloud environment. The block-based sharing scheme is compared with the existing schemes on the basis of energy consumption, CPU utilization, memory utilization, encryption time, decryption time, and turnaround time. The experimental results show that the block-based sharing scheme consumes less energy, reduces the resources utilization, improves response time, and provides better security services to the mobile users in the presence of fully untrusted cloud server(s) as compared to the existing security schemes.

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

Multisensor data fusion can be considered as a strong nonlinear system. A precise analytical solution is challenging to obtain, thus making it hard to dissect with routine diagnostic systems. Since tried-and-true logical systems are extremely difficult to undertake, soft computing methodologies are deemed having potential for such applications. This paper presents the support vector regression (SVR) methodology for sensor fusion to improve tracking ability. Radial basis function (RBF) and polynomial function are used as SVR kernel functions. The system combines Kalman filtering and soft computing principle, i.e., SVR, to structure an effective information combination method for the target framework. A radar-infrared system is proposed to adapt contextual changes and lessen the dubious unsettling influence of an information estimation from multisensory data. The experimental results show that an improvement in predictive accuracy and generalization capability can be achieved using the SVR with RBF kernel compared with the SVR with polynomial kernel approach.