A stable energy efficient clustering protocol for wireless sensor networks

Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period.     

Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.

     

Detecting DDoS Attacks in Cloud Computing Using Extreme Learning Machine and Adaptive Differential Evolution

Wireless Personal Communications - Distributed denial of service (DDoS) attacks disrupt the availability of cloud services. The detection of these attacks is a major challenge in the cloud...     

Attack Resilient and Efficient Protocol based on Greedy Perimeter Coordinator Routing—Mobility Awareness for Preventing the Attack in the VANET

Wireless Personal Communications - A Greedy Perimeter Coordinator Routing and Mobility Awareness (GPCR-MA) vehicular routing is a widely accepted routing protocol for VANET (Vehicular Ad hoc...     

Manifold Preserving Features and Regression for Semantic Labelling in High Dimensional Images

Wireless Personal Communications - This paper proposes an combined method for manifold preservation and Subspace Eigenvectors(SE) based regression in high dimensional (HD) images. We studied...     

Bacteria-Responsive Multidrug Delivery Nanosystem for Combating Long-Term Biofilm-Associated Infections

Microbial biofilm formation on implantable devices causes chronic infections that cannot be treated with existing antimicrobials. Quorum sensing inhibitors (QSIs) have recently emerged as novel antimicrobials for the prevention of biofilm formation. But blocking QS alone is insufficient to inhibit biofilm-associated chronic infections. Herein, chitosan hollow nanospheres are capped by bacteria-responsive β-casein to form a synergistic antifouling nanosystem consisting of a QSI and bactericide. β-casein is degraded by protease in a bacteria-colonized microenvironment in situ thus, QSI and bactericide are released sequentially. The release of QSI sensitises bacteria effectively through reduction of surface hydrophobicity, eDNA content, and lipopolysaccharide production in biofilms, amplifying the chemotherapeutic action of the bactericide. Compared to the uncoated surface, the coated surface inhibits biofilm formation and removes preformed biofilms of Pseudomonas aeruginosa PAO1 and methicillin-resistant Staphylococcus aureus by 1.8 logs and 1.9 logs of biomass inhibition, respectively. The coated catheters are found to stay clean for 30 days under artificial urine flow, while the uncoated catheters are clogged by bacterial biofilms within 5 days. Finally, the long term antifouling activity in vivo is confirmed. Overall, the nanosystem is devoted to making urinary catheters resistant to bacterial biofilm formation for the long term.     

Analysis of ATPMOS configurations-based 4 × 1 multiplexer with estimation of power and delay

In this article, two consecutive augmenting transistor P-channel metal oxide semiconductor (ATPMOS) configurations are proposed. These two ATPMOS configurations (ST ATPMOS and DT ATPMOS) are implemented on a 4 × 1 (multiplexer) mux circuit. Leakage power dissipation, dynamic power dissipation and delay performance parameters are calculated for both (ST ATPMOS and DT ATPMOS) ATPMOS configurations-based 4 × 1 mux circuits at different values of transistor’s width. Due to simulation, it is realised that the leakage power dissipation and dynamic power dissipation are reduced and delay is improved (delay is reduced) in the DT ATPMOS configuration-based mux circuit compared to the ST ATPMOS configuration-based mux circuit. The whole simulation process was carried out in 45-nm technology. The circuits were operated at 1-V power supply.     

Use of Fourier transform infrared spectroscopy to study ageing characteristics of asphalt binders

The present study aims at studying ageing characteristics of conventional bitumen, polymer modified bitumen (PMB) and warm mix asphalt (WMA) by using FTIR. Results of obtained FTIR spectrum were quantified by calculating different indices and finally based on these indices ageing index of the binders for short- and long-term ageing were calculated. Results showed ageing increased amount of complex and high molecular weight components of the binders. PMB was found to have greater ageing resistance than conventional asphalt binders. In case of WMA, results found to be inconclusive, requiring further investigation.     

Bayes estimation of the linear hazard-rate model

In life testing, the failure-time distributions are often specified by choosing an appropriate hazard-rate function. The class of life-time distribution characterized by a linear hazard-rate includes the one-parameter exponential and Rayleigh distributions. Usually the parameters of the linear hazard-rate model are estimated by the method of least squares. This work is concerned with Bayes estimation of the two-parameters from a type-2 censored sample. Monte Carlo simulation is used to compare the Bayes risk of the regression estimator with the minimum Bayes risk. Discrete mixtures of decreasing failure rate distributions are known to have decreasing failure rates. The authors prove that the result holds for continuous mixtures as well     

Effects of strain on the high speed modulation of GaAs- andInP-based quantum-well lasers

A small-signal numerical analysis of pseudomorphic GaAs- and InP-based Fabry-Perot quantum-well lasers using calculated optical gain spectra with strain effects included is reported. Examination of the effect of lifetime broadening shows that the resonance frequency increases at a rate of ~250-MHz/meV reduction in the lifetime broadening for a GaAs-based strained layer laser. The modulation speed is limited by either device heating or facet damage. If the limitation is imposed by the optical power then the modulation speed increases as the laser cavity becomes shorter and the number of quantum wells increases. If the limitation is imposed by the injection current density, however, then the modulation speed decreases for the laser with shorter cavity length. The highest modulation speed is given by an optimum well number. A resonance frequency of ~16 GHz is predicted for a pseudomorphic GaAs-based laser with 30% excess In and average output power of ~5 mW