Nauman Aslam

A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks

Clustering techniques have emerged as a popular choice for achieving energy efficiency and scalable performance in large scale sensor networks. Cluster formation is a process whereby sensor nodes decide which cluster head they should associate with among multiple choices. Typically this cluster head selection decision involves a metric based on parameters including residual energy and distance to the cluster head. This decision is a critical embarkation point as a poor choice can lead to increased energy consumption, thus compromising network lifetime. In this paper we present a novel energy efficient cluster formation algorithm based on a multi-criterion optimization technique. Our technique is capable of using multiple individual metrics in the cluster head selection process as input while simultaneously optimizing on the energy efficiency of the individual sensor nodes as well as the overall system. The proposed technique is implemented as a distributed protocol in which each node makes its decision based on local information only. The feasibility of the proposed technique is demonstrated with simulation results. It is shown that the proposed technique outperforms all other well known protocols including LEACH, EECS and HEED resulting in a significant increase in network life.

A web-based remote interactive laboratory for Internetworking education

A Web-based remote interactive laboratory (RIL) developed to deliver Internetworking laboratory experience to geographically remote graduate students is presented in this paper. The onsite Internetworking program employs hands-on laboratories in a group setting that correlates with the constructivist and collaborative pedagogical approach. This paper discusses the pedagogical and technical considerations that influence the design and implementation of the remote laboratory environment given the constraints of the special hardware and learning outcomes of the program. For wide-ranging usability, the remote Internetworking (INWK) laboratory uses de facto networking standards and commercial and broad-band Internet connectivity to ensure real-time secure interaction with equipment. A four-tier role architecture consisting of faculty, local facilitators, remote facilitators, and students has been determined appropriate to maintain academic integrity and ensure good quality of interaction with the remote laboratory. A survey employing a five-point scale has been devised to measure the usability of the remote access INWK laboratory.

An Energy Efficient Fuzzy Logic Cluster Formation Protocol in Wireless Sensor Networks

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Researchers have investigated architectures and topologies that allow energy efficient operation of WSNs. One of the popular techniques in this regard is clustering. While many researchers have investigated cluster head selection, this paper investigates the cluster formation. In particular, we propose a novel scheme, the Fuzzy Logic Cluster Formation Protocol (FLCFP), which uses Fuzzy Logic Inference System (FIS) in the cluster formation process. We demonstrate that using multiple parameters in cluster formation reduces energy consumption. We compare our technique with the well known LEACH protocol to show that using a multi parameter FIS enhances the network lifetime significantly.

Intelligent phishing detection and protection scheme for online transactions

Phishing is an instance of social engineering techniques used to deceive users into giving their sensitive information using an illegitimate website that looks and feels exactly like the target organization website. Most phishing detection approaches utilizes Uniform Resource Locator (URL) blacklists or phishing website features combined with machine learning techniques to combat phishing. Despite the existing approaches that utilize URL blacklists, they cannot generalize well with new phishing attacks due to human weakness in verifying blacklists, while the existing feature-based methods suffer high false positive rates and insufficient phishing features. As a result, this leads to an inadequacy in the online transactions. To solve this problem robustly, the proposed study introduces new inputs (Legitimate site rules, User-behavior profile, PhishTank, User-specific sites, Pop-Ups from emails) which were not considered previously in a single protection platform. The idea is to utilize a Neuro-Fuzzy Scheme with 5 inputs to detect phishing sites with high accuracy in real-time. In this study, 2-Fold cross-validation is applied for training and testing the proposed model. A total of 288 features with 5 inputs were used and has so far achieved the best performance as compared to all previously reported results in the field.

Intelligent facial emotion recognition using a layered encoding cascade optimization model

A layered cascade optimization model is developed for facial emotion recognition.Two layered cascade-based evolutionary algorithms are proposed for feature selection.They focus on within-class and between-class variations for feature optimization.Both a neural network and an adaptive ensemble classifier are employed for expression recognition.Superior performance is shown in both frontal and 90? side-view expression recognition. In this research, we propose a facial expression recognition system with a layered encoding cascade optimization model. Since generating an effective facial representation is a vital step to the success of facial emotion recognition, a modified Local Gabor Binary Pattern operator is first employed to derive a refined initial face representation and we then propose two evolutionary algorithms for feature optimization including (i) direct similarity and (ii) Pareto-based feature selection, under the layered cascade model. The direct similarity feature selection considers characteristics within the same emotion category that give the minimum within-class variation while the Pareto-based feature optimization focuses on features that best represent each expression category and at the same time provide the most distinctions to other expressions. Both a neural network and an ensemble classifier with weighted majority vote are implemented for the recognition of seven expressions based on the selected optimized features. The ensemble model also automatically updates itself with the most recent concepts in the data. Evaluated with the Cohn-Kanade database, our system achieves the best accuracies when the ensemble classifier is applied, and outperforms other research reported in the literature with 96.8% for direct similarity based optimization and 97.4% for the Pareto-based feature selection. Cross-database evaluation with frontal images from the MMI database has also been conducted to further prove system efficiency where it achieves 97.5% for Pareto-based approach and 90.7% for direct similarity-based feature selection and outperforms related research for MMI. When evaluated with 90? side-view images extracted from the videos of the MMI database, the system achieves superior performances with >80% accuracies for both optimization algorithms. Experiments with other weighting and meta-learning combination methods for the construction of ensembles are also explored with our proposed ensemble showing great adpativity to new test data stream for cross-database evaluation. In future work, we aim to incorporate other filtering techniques and evolutionary algorithms into the optimization models to further enhance the recognition performance.

Energy-aware Peering Routing Protocol for indoor hospital Body Area Network Communication

The recent research in Body Area Networks (BAN) is focused on making its communication more reliable, energy efficient, secure, and to better utilize system resources. In this paper we propose a novel BAN network architecture for indoor hospital environments, and a new mechanism of peer discovery with routing table construction that helps to reduce network traffic load, energy consumption, and improves BAN reliability. We have performed extensive simulations in the Castalia simulation environment to show that our proposed protocol has better performance in terms of reduced BAN traffic load, increased number of successful packets received by nodes, reduced number of packets forwarded by intermediate nodes, and overall lower energy consumption compared to other protocols.

A new patient monitoring framework and Energy-aware Peering Routing Protocol (EPR) for Body Area Network communication

The recent research in Body Area Networks (BANs) is focused on making its communication more reliable, energy efficient, secure, and to better utilize system resources. In this paper we propose a novel BAN architecture for indoor hospital environments, and a new mechanism of peer discovery with routing table construction that helps to reduce network traffic load, energy consumption, and improves BAN reliability. The three scenarios with fixed and variable number of packets sent by source nodes are considered for better analysis. Static nodes are considered in first and second scenarios whereas mobile nodes are used in third scenario. We have performed extensive simulations in the OMNeT++ based Castalia-3.2 simulation environment to show that our proposed protocol has better performance in terms of reduced BAN traffic load, increased successful transmission rate, reduced number of packets forwarded by intermediate nodes, no packets dropped due to buffer overflow, and overall lower energy consumption when compared with a similar protocols.

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

Composite metric for quality of service routing in OLSR

Quality of service (QoS) support in mobile ad hoc networks (MANET) is a very challenging task because of the dynamic topology, limited resources and wireless link characteristics. The optimized link state routing protocol (OLSR) is a pro-active routing protocol for MANET. The metric used in OLSR protocol is hop count, which is not suitable for the wireless environment due to its inherent dynamic link characteristics. In this paper we discuss the different approaches used to add QoS functionality in OLSR. Most of these techniques are centered on using one or two performance metrics. We propose a composite metric using multiple parameters to find the optimal route given the QoS constraints.

A Critical Analysis of Research Potential, Challenges, and Future Directives in Industrial Wireless Sensor Networks

In recent years, industrial wireless sensor networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems, and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment, and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper, a detailed discussion on design objectives, challenges, and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines, and possible hazards in industrial atmosphere are discussed. This paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. This paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs.