Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization

A mobile ad hoc network (MANET) is dynamic in nature and is composed of wirelessly connected nodes that perform hop-by-hop routing without the help of any fixed infrastructure. One of the important requirements of a MANET is the efficiency of energy, which increases the lifetime of the network. Several techniques have been proposed by researchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-efficient solution. Clustering involves the selection of cluster-heads (CHs) for each cluster and fewer CHs result in greater energy efficiency as these nodes drain more power than noncluster-heads. In the literature, several techniques are available for clustering by using optimization and evolutionary techniques that provide a single solution at a time. In this paper, we propose a multi-objective solution by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad hoc network as well as energy dissipation in nodes in order to provide an energy-efficient solution and reduce the network traffic. In the proposed solution, inter-cluster and intra-cluster traffic is managed by the cluster-heads. The proposed algorithm takes into consideration the degree of nodes, transmission power, and battery power consumption of the mobile nodes. The main advantage of this method is that it provides a set of solutions at a time. These solutions are achieved through optimal Pareto front. We compare the results of the proposed approach with two other well-known clustering techniques; WCA and CLPSO-based clustering by using different performance metrics. We perform extensive simulations to show that the proposed approach is an effective approach for clustering in mobile ad hoc networks environment and performs better than the other two approaches.     

Cumin Prevents 17β-Estradiol-Associated Breast Cancer in ACI Rats

Breast cancer (BC) is a leading cause of cancer deaths in women in less developed countries and the second leading cause of cancer death in women in the U.S. In this study, we report the inhibition of E2-mediated mammary tumorigenesis by Cuminum cyminum (cumin) administered via the diet as cumin powder, as well as dried ethanolic extract. Groups of female ACI rats were given either an AIN-93M diet or a diet supplemented with cumin powder (5% and 7.5%, w/w) or dried ethanolic cumin extract (1%, w/w), and then challenged with subcutaneous E2 silastic implants (1.2 cm; 9 mg). The first appearance of a palpable mammary tumor was significantly delayed by both the cumin powder and extract. At the end of the study, the tumor incidence was 96% in the control group, whereas only 55% and 45% animals had palpable tumors in the cumin powder and extract groups, respectively. Significant reductions in tumor volume (660 ± 122 vs. 138 ± 49 and 75 ± 46 mm³) and tumor multiplicity (4.21 ± 0.43 vs. 1.16 ± 0.26 and 0.9 ± 0.29 tumors/animal) were also observed by the cumin powder and cumin extract groups, respectively. The cumin powder diet intervention dose- and time-dependently offset E2-related pituitary growth, and reduced the levels of circulating prolactin and the levels of PCNA in the mammary tissues. Mechanistically, the cumin powder diet resulted in a significant reversal of E2-associated modulation in ERα, CYP1A1 and CYP1B1. Further, the cumin powder diet reversed the expression levels of miRNAs (miR-182, miR-375, miR-127 and miR-206) that were highly modulated by E2 treatment. We analyzed the composition of the extract by GC/MS and established cymene and cuminaldehyde as major components, and further detected no signs of gross or systemic toxicity. Thus, cumin bioactives can significantly delay and prevent E2-mediated mammary tumorigenesis in a safe and effective manner, and warrant continued efforts to develop these clinically translatable spice bioactives as chemopreventives and therapeutics against BC.     

Self and static interference mitigation scheme for coexisting wireless networks

High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.     

Your comments matter: incorporating viewers’ comments for ranking online video content using bibliometrics

ABSTRACT

The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices h_vc, g_vc and m_vc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.     

Adaptive dynamic surface control of a hypersonic flight vehicle with improved tracking

This work presents a nonlinear adaptive dynamic surface air speed and a flight path angle control design procedure for the longitudinal dynamics of a generic hypersonic flight vehicle. The proposed design scheme takes into account the magnitude, rate, and bandwidth constraints on the actuator signals. A new approach is used to enhance tracking performance and avoid a large initial control signal. The uncertain nonlinear functions in the flight vehicle model are approximated by using radial basis function neural networks. A detailed stability analysis of the designed controllers shows that all the signals of the closed‐loop system are uniformly ultimately bounded. The robust performance of the design scheme is verified through numerical simulations of the flight vehicle model for various parameter variation test cases. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Iterative methods for solving extended general mixed variational inequalities

In this paper, we introduce and consider a new class of mixed variational inequalities involving four operators, which are called extended general mixed variational inequalities. Using the resolvent operator technique, we establish the equivalence between the extended general mixed variational inequalities and fixed point problems as well as resolvent equations. We use this alternative equivalent formulation to suggest and analyze some iterative methods for solving general mixed variational inequalities. We study the convergence criteria for the suggested iterative methods under suitable conditions. Our methods of proof are very simple as compared with other techniques. The results proved in this paper may be viewed as refinements and important generalizations of the previous known results.     

ELF-Miner: using structural knowledge and data mining methods to detect new (Linux) malicious executables

Linux malware can pose a significant threat—its (Linux) penetration is exponentially increasing—because little is known or understood about Linux OS vulnerabilities. We believe that now is the right time to devise non-signature based zero-day (previously unknown) malware detection strategies before Linux intruders take us by surprise. Therefore, in this paper, we first do a forensic analysis of Linux executable and linkable format (ELF) files. Our forensic analysis provides insight into different features that have the potential to discriminate malicious executables from benign ones. As a result, we can select a features’ set of 383 features that are extracted from an ELF headers. We quantify the classification potential of features using information gain and then remove redundant features by employing preprocessing filters. Finally, we do an extensive evaluation among classical rule-based machine learning classifiers—RIPPER, PART, C4.5 Rules, and decision tree J48—and bio-inspired classifiers—cAnt Miner, UCS, XCS, and GAssist—to select the best classifier for our system. We have evaluated our approach on an available collection of 709 Linux malware samples from vx heavens and offensive computing. Our experiments show that ELF-Miner provides more than 99% detection accuracy with less than 0.1% false alarm rate.