Clone Node Detection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are often deployed in unfavourable situations where an assailant can physically capture some of the nodes, first can reprogram, and then, can replicate them in a large number of clones, easily taking control over the network. This replication node is also called as Clone node. The clone node or replicated node behave as a genuine node. It can damage the network. In node replication attack detecting the clone node important issue in Wireless Sensor Networks. A few distributed solutions have been recently proposed, but they are not satisfactory. First, they are intensity and memory demanding: A serious drawback for any protocol to be used in the WSN- resource constrained environment. In this project first investigate the selection criteria of clone detection schemes with regard to device types, detection methodologies, deployment strategies, and detection ranges. Further, they are vulnerable to the specific assailant models introduced in this paper. In this scenario, a particularly dangerous attack is the replica attack, in which the assailant takes the secret keying materials from a compromised node, generates a large number of assailant-controlled replicas that share the node’s keying materials and ID, and then spreads these replicas throughout the network. With a single captured node, the assailant can create as many replica nodes as he has the hardware to generate.. The replica nodes are controlled by the assailant, but have keying materials that allow them to seem like authorized participants in the network. Our implementation specifies, user will specify its ID, which means client id, secret key will be create, and then include the port number. The witness node will verify the internally bounded user Id and secret key. The witness node means original node. If the verification is success, the information collecting to the packets that packets are send to the destination

ATR-FTIR Analysis on Aliphatic Hydrocarbon Bond (C-H) Formation and Carboxyl Content during the Ageing of DC Air Plasma Treated Cotton Cellulose and Its Impact on Hydrophilicity

The surface of the cotton fabric was modified using a Direct current (DC) air plasma treatment and hence enhances its hydrophilicity. The Box-Behnken approach (design expert software) was used to optimise the input process parameters. The sample prepared under optimized condition is subjected to ATR-FTIR and Field Emission Scanning Electron Microscopy (FESEM) studies in order to determine the changes in hydrogen bond energies (EH), Total Crystallinity Index (TCI), Hydrogen Bond Intensity (HBI), Lateral Order Index (LOI), functionalization, lattice parameters (a, b, c & β), degree of crystallinity (in %) and surface etching. The ageing of this sample has been studied by comparing the values of carboxyl content and AC-C/AC-O-C ratio calculated using data extracted from ATR-FTIR spectra of the sample recorded periodically for one month

An Efficient Hybrid Classifier Model for Customer Churn Prediction

Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance.  Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy.  Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall

Comparative Study on Multivariate Methods Using Chronic Kidney Disease

The human being is currently one of the most serious illnesses in the modern world, and accurate diagnosis is necessary as soon as possible. In this modern world, there are numerous diseases that exist. Chronic kidney disease is regarded as the most serious of these disorders in humans. There are several methods in the medical area for disease diagnosis, and the prediction criterion is also significant in the medical field for determining the consequences of the study in the future. Many statistical methods are employed in order to forecast the medical dataset and provide accurate and reliable findings. A lot of models are available in multivariate methods to predict the dataset. In this paper, the computational algorithms for detecting CKD using Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Logistic Regression (LR) are reviewed. The first, based on the association, inference for the study. Decision tree and logistic regression approaches are used to more correctly diagnose chronic renal disease based on the results of the association. Finally, the study came to the conclusion that greatest fit for forecasting chronic renal disease

Seismic Response of Reinforced Soil Retaining Walls with Block Facings

Reinforced soil walls have become very popular in seismic areas owing to their flexible nature and cost effectiveness when compared to the conventional retaining structures. Although the use of reinforced soil walls with modular block facings and gabion facings is growing world wide at a rapid rate, the seismic response of these walls is yet to be analyzed. This paper discusses the response of these walls in terms of lateral facing deflection, reinforcement tensile force and crest surface settlement when subjected to seismic loading simulated by means of a variable amplitude harmonic vibration using the finite element analysis package, PLAXIS V8. From the study, it was found that there is significant effect of seismic loading on the response of reinforced soil walls and the analyses and design of these walls are to be done only after considering the dynamic earthquake loading in seismic prone areas. The gabion faced reinforced soil walls were found to be more effective than the segmental walls in resisting the dynamic excitations due to earthquake loading. The study also confirmed that various reinforcement design parameters and backfill parameters play an important role in minimizing the facing deflection and the settlement of the wall subjected to dynamic earthquake excitation

Low Power High Gain Op-Amp using Square Root based Current Generator

A very high gain two stage CMOS operational amplifier has been presented The proposed circuit is implemented in 180nm CMOS technology with a supply voltage of 0 65V The current source in the OPAMP is replaced by a square root based current generator which helps to reduce the impact of process variations on the circuit and low power consumption due to the operation of MOS in subthreshold region So with the help of square root based current generator the better controllability over gain can be obtained The proposed opamp shows a high gain of 121 9dB and low power consumption of 11 89uW is achieve

A DYNAMIC WATERMARKING MODEL FOR MEDICAL IMAGE AUTHENTICATION

This paper proposes a dynamic watermarking model for the purpose of medical authentication. While transferring the data through a public network there is jittering or tampering of data. This is a matter of concern as any jitter or tampered data is not desirable in the medical field. It is noted that there is loss of life due to corrupted data received leading to wrong diagnosis. The proposed dynamic model proves that the medical image watermarked with the proposed system provides near lossless original image. Since the watermark is generated dynamically it is unique to the images considered therefore enhances the security of the images. The Proposed scheme is in the TIFF (Tagged Image File Format) using RGB colour space. The given watermar

Antimicrobial potential of leaf extract of normal and tissue cultured plants of Andrographis Paniculata nees

Various extracts of root, stem and leaf of normal and tissue cultured plants of Andrographis paniculata were tested against five pathogenic bacteria. Four months old field grown and tissue cultured plants raised on MS+BAP (8.86 ÎM) were used as source plants. Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa, Staphylococcus aureus and Proteus vulgaris were pathogenic bacteria tested by Agar well diffusion method. The effect of various extracts measured by zone of inhibition varies with pathogens and also the source. Comparatively, methanolic leaf extracts of tissue cultured plants showed better zone of inhibition than the normal plants against all pathogens. Enhanced antibacterial activity of extracts of tissue cultured plants is related to the better growth performance and high content of secondary metabolites than the normal plants

Data storage lock algorithm with cryptographic techniques

The cloud computing had its impact far and wide, and Enterprise solutions are getting migrated to different types of clouds. The services are delivered from the data centers which are located all over the world. As the data is roaming with less control in any data centers, data security issues in cloud are very challenging. Therefore we need multi-level authentication, data integrity, privacy and above all encryption to safeguard our data which is stored on to the cloud. The data and applications cannot be relocated to a virtual server without much degree of security concern as there can be much confidential data or mission-critical applications. In this paper, we propose Data Storage Lock Algorithm (DSLA) to store confidential data thereby provides secure data storage in cloud computing based on cryptographic standards