Sadia Din

Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where High-Performance Computing solution has become a key issue and has attracted attention in recent years. However, these systems are either memoryless or computational inefficient. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that enhances the working of traditional MapReduce by incorporating parallel processing algorithm. Moreover, complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed parallel processing algorithm. The proposed system architecture both read and writes operations that enhance the efficiency of the Input/Output operation. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.

A Cluster-Based Data Fusion Technique to Analyze Big Data in Wireless Multi-Sensor System

With the development of the latest technologies and changes in market demand, the wireless multi-sensor system is widely used. These multi-sensors are integrated in a way that produces an overwhelming amount of data, termed as big data. The multi-sensor system creates several challenges, which include getting actual information from big data with high accuracy, increasing processing efficiency, reducing power consumption, providing a reliable route toward destination using minimum bandwidth, and so on. Such shortcomings can be overcome by exploiting some novel techniques, such as clustering, data fusion, and coding schemes. Moreover, data fusion and clustering techniques are proven architectures that are used for efficient data processing; resultant data have less uncertainty, providing energy-aware routing protocols. Because of the limited resources of the multi-sensor system, it is a challenging task to reduce the energy consumption to survive a network for a longer period. Keeping challenges above in view, this paper presents a novel technique by using a hybrid algorithm for clustering and cluster member selection in the wireless multi-sensor system. After the selection of cluster heads and member nodes, the proposed data fusion technique is used for partitioning and processing the data. The proposed scheme efficiently reduces the blind broadcast messages but also decreases the signal overhead as the result of cluster formation. Afterward, the routing technique is provided based on the layered architecture. The proposed layered architecture efficiently minimizes the routing paths toward the base station. Comprehensive analysis is performed on the proposed scheme with state-of-the-art centralized clustering and distributed clustering techniques. From the results, it is shown that the proposed scheme outperforms competitive algorithms in terms of energy consumption, packet loss, and cluster formation.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Toward modeling and optimization of features selection in Big Data based social Internet of Things

Abstract The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.

A Novel Security Model for Cooperative Virtual Networks in the IoT Era

The Internet of Things (IoT) has particular applications in public safety as well as other domains such as smart cities, health monitoring, smart homes and environments, smart industry, and various types of pervasive systems. The attacker can simply attack the IoT device in such applications, because it is randomly distributed, dynamic topology and not reliable due to energy and communication limitation. Moreover, the threat to confidentiality and security is increasing as the number of devices connected in IoT is increasing. As the numbers of devices connected to the Internet is expanding, the threat to confidentiality and security is increasing. The aim of this paper is design a typical network security model for cooperative virtual networks in the IoT era. This paper presents and discusses network security vulnerabilities, threats, attacks and risks in switches, firewalls and routers, in addition to a policy to mitigate those risks. The paper provides the fundamentals of secure networking system including firewall, router, AAA server and VLAN technology. It presents a novel security model to defense the network from internal and external attacks and threats in the IoT Era. A testbed is built to investigate the proposed model, and the performed assessment show an effective security performance with a good network performance.

Smart health monitoring and management system: Toward autonomous wearable sensing for internet of things using big data analytics

Abstract The current development and growth in the arena of Internet of Things (IoT) are providing a great potential in the route of the novel epoch of healthcare. The vision of the healthcare is expansively favored, as it advances the excellence of life and health of humans, involving several health regulations. The incessant increase of the multifaceted IoT devices in health is broadly tested by challenges such as powering the IoT terminal nodes used for health monitoring, real-time data processing and smart decision and event management. In this paper, we propose a healthcare architecture which is based analysis of energy harvesting for health monitoring sensors and the realization of Big Data analytics in healthcare. The rationale of proposed architecture is twofold: (1) comprehensive conceptual framework for energy harvesting for health monitoring sensors, and (2) data processing and decision management for healthcare. The proposed architecture is three-layered architecture, that comprised (1) energy harvesting and data generation, data pre-processing, and data processing and application. We also verified the consistent data sets on Hadoop server to validate the proposed architecture based on threshold limit value (TLV). The study reveals that the proposed architecture offer valuable imminent into the field of smart health.

Enabling multimedia aware vertical handover Management in Internet of Things based heterogeneous wireless networks

Enabling seamless connectivity in Internet of Things (IoT) based heterogeneous wireless networks and pervasive use of smartphones in daily life require high data speed and always-best-connected services. However, providing vertical handover management in heterogeneous wireless networks is a difficult and challenging task. Moreover, various issues are present in the current vertical handover management schemes such as inappropriate handover triggering, high handover delay, wrong network selection, etc. In order to address the aforementioned issues, we propose a generic vertical handover management scheme. Our research is twofold; firstly, the Mobile Node (MN) dynamically checks the data rate required by the applications running on the MN’s device. If the data rate drops below a predefined threshold, the MN initiates the handover. Secondly, the network selection is performed by considering various parameters such as end-to-end delay, jitter, Bit Error Rate, and packet loss. The Artificial Bee Colony (ABC) optimization algorithm uses the above parameters to select the target network with minimum handover delay and time. The proposed scheme is compared with the Simple Additive Weighting (SAW), Weighted Product Method (WPM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Fuzzy TOPSIS in context of energy consumption, throughput, average MN’s stay time in a network, handover delay, and handover time. The experimental results show that the proposed vertical handover management scheme outperforms the existing schemes used for similar purpose.

An adaptive hybrid fuzzy-wavelet approach for image steganography using bit reduction and pixel adjustment

Transform-based techniques partially address challenges like robustness and the imperceptibility in image steganography. Such approaches, however, increase the memory requirement and reduce the quality of the cover image and hiding capacity. Moreover, the steganography is always coupled with cryptography to strengthen the confidentiality. This paper presents an adaptive hybrid method for image steganography procedure based on bit reduction and pixel adjustment using the fuzzy logic and integer wavelet transform technique. The fuzzy set theory provides powerful tools to represent and process human knowledge in the form of fuzzy if-then rules that can resolve difficulties in image processing arising due to the uncertainty of the data, tasks, and results. We apply a bit reduction algorithm to each byte of the data which are to hide in the cover image. This decreases the memory usage and increases the capacity. The embedding of the input text into the cover image distorts the cover image. Hence, to minimize the visual difference between the cover image and the text embedded image, an optimum pixel adjustment algorithm is applied to the text embedded image. Simulation results demonstrate the effectiveness of our proposed approach.

Towards ontology-based multilingual URL filtering: a big data problem

Web content filtering is one among many techniques to limit the exposure of selective content on the Internet. It has gotten trivial with time, yet filtering of multilingual web content is still a difficult task, especially while considering big data landscape. The enormity of data increases the challenge of developing an effective content filtering system that can work in real time. There are several systems which can filter the URLs based on artificial intelligence techniques to identify the site with objectionable content. Most of these systems classify the URLs only in the English language. These systems either fail to respond when multilingual URLs are processed, or over-blocking is experienced. This paper introduces a filtering system that can classify multilingual URLs based on predefined criteria for URL, title, and metadata of a web page. Ontological approaches along with local multilingual dictionaries are used as the knowledge base to facilitate the challenging task of blocking URLs not meeting the filtering criteria. The proposed work shows high accuracy in classifying multilingual URLs into two categories, white and black. Evaluation results conducted on a large dataset show that the proposed system achieves promising accuracy, which is on a par with those achieved in state-of-the-art literature on semantic-based URL filtering.

MGR: Multi-parameter Green Reliable communication for Internet of Things in 5G network

Abstract Internet of Things (IoT) plays a major role in connecting the physical world with the cyber world through new services and seamless interconnection between heterogeneous devices. However, exploiting green schemes for IoT is still a challenge because as IoT attains a large scale and becomes more multifaceted, the current trends are not directly applicable to it. Similarly, achieving green communication through the use of 5G also poses new challenges when it comes to transferring huge volume of data efficiently. To address the challenges above, this paper presents a scheme for green IoT in 5G network. Grouping mobile nodes achieve green IoT in a cluster. Also, a mobility management model is designed that helps in triggering efficient handover and selecting optimal networks based on multicriteria decision modeling. Afterwards, we develop a system architecture which integrates green IoT with 5G network. It also helps them in communicating with other heterogeneous networks efficiently with minimum energy requirements. Moreover, the 5G network architecture is supported by the proposed protocol stack, which maps Internet Protocol (IP), Medium Access Protocol (MAC), and Location Identifiers (LOC). The proposed scheme is implemented using C programming language, and extensive mathematical and statistical analysis is carried out regarding cost, energy, and Quality of Service.