arXiv subjects

K R Venugopal

Publications and source records attributed to K R Venugopal.

10 recordsLinked to original sources

arXiv2016

RRDVCR: Real-Time Reliable Data Delivery Based on Virtual Coordinating Routing for Wireless Sensor Networks

Real-time industrial application requires routing protocol that guarantees data delivery with reliable, efficient and low end-to-end delay. Existing Routing(THVR) [13] is based velocity of Two-Hop Velocity and protocol relates two-hop velocity to delay to select the next forwarding node, that has overhead of exchanging control packets, and depleting the available energy in nodes. We propose a Real-Time Reliable Data delivery based on Virtual Coordinates Routing (RRDVCR) algorithm, based on the number of hops to the destination rather than geographic distance. Selection of forwarding node is based on packet progress offered by two-hops, link quality and available energy at the forwarding nodes. All these metric are co-related by dynamic co-relation factor. The proposed protocol uses selective acknowledgment scheme that results in lower overhead and energy consumption. Simulation results shows that there is about 22% and 9.5% decrease in energy consumption compared to SPEED [8] and THVR [13] respectively, 16% and 38% increase in packet delivery compared to THVR [13] and SPEED[8] respectively, and overhead is reduced by 50%.

cs.NI↗

arXiv2014

Cancer Prognosis Prediction Using Balanced Stratified Sampling

High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge exists in double. The use of effective sampling technique in classification algorithms always yields good prediction accuracy. The SEER public use cancer database provides various prominent class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling techniques in classifying the prognosis variable and propose an ideal sampling method based on the outcome of the experimentation. In the first phase of this work the traditional random sampling and stratified sampling techniques have been used. At the next level the balanced stratified sampling with variations as per the choice of the prognosis class labels have been tested. Much of the initial time has been focused on performing the pre_processing of the SEER data set. The classification model for experimentation has been built using the breast cancer, respiratory cancer and mixed cancer data sets with three traditional classifiers namely Decision Tree, Naive Bayes and K-Nearest Neighbor. The three prognosis factors survival, stage and metastasis have been used as class labels for experimental comparisons. The results shows a steady increase in the prediction accuracy of balanced stratified model as the sample size increases, but the traditional approach fluctuates before the optimum results.

cs.LG↗

arXiv2014

Two Stage Prediction Process with Gradient Descent Methods Aligning with the Data Privacy Preservation

Privacy preservation emphasize on authorization of data, which signifies that data should be accessed only by authorized users. Ensuring the privacy of data is considered as one of the challenging task in data management. The generalization of data with varying concept hierarchies seems to be interesting solution. This paper proposes two stage prediction processes on privacy preserved data. The privacy is preserved using generalization and betraying other communicating parties by disguising generalized data which adds another level of privacy. The generalization with betraying is performed in first stage to define the knowledge or hypothesis and which is further optimized using gradient descent method in second stage prediction for accurate prediction of data. The experiment carried with both batch and stochastic gradient methods and it is shown that bulk operation performed by batch takes long time and more iterations than stochastic to give more accurate solution.

cs.DB↗

arXiv2014

Two-Hop Routing with Traffic-Differentiation for QoS Guarantee in Wireless Sensor Networks

This paper proposes a Traffic-Differentiated Two-Hop Routing protocol for Quality of Service (QoS) in Wireless Sensor Networks (WSNs). It targets WSN applications having different types of data traffic with several priorities. The protocol achieves to increase Packet Reception Ratio (PRR) and reduce end-to-end delay while considering multi-queue priority policy, two-hop neighborhood information, link reliability and power efficiency. The protocol is modular and utilizes effective methods for estimating the link metrics. Numerical results show that the proposed protocol is a feasible solution to addresses QoS service differenti- ation for traffic with different priorities.

cs.NI↗

arXiv2014

Mathematical Model of Semantic Look - An Efficient Context Driven Search Engine

The WorldWideWeb (WWW) is a huge conservatory of web pages. Search Engines are key applications that fetch web pages for the user query. In the current generation web architecture, search engines treat keywords provided by the user as isolated keywords without considering the context of the user query. This results in a lot of unrelated pages or links being displayed to the user. Semantic Web is based on the current web with a revised framework to display a more precise result set as response to a user query. The current web pages need to be annotated by finding relevant meta data to be added to each of them, so that they become useful to Semantic Web search engines. Semantic Look explores the context of user query by processing the Semantic information recorded in the web pages. It is compared with an existing algorithm called OntoLook and it is shown that Semantic Look is a better optimized search engine by being more than twice as fast as OntoLook.

cs.IR↗

arXiv2014

Link-Reliability Based Two-Hop Routing for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) emerge as underlying infrastructures for new classes of large scale net- worked embedded systems. However, WSNs system designers must fulfill the Quality-of-Service (QoS) requirements imposed by the applications (and users). Very harsh and dynamic physical environments and extremely limited energy/computing/memory/communication node resources are major obstacles for satisfying QoS metrics such as reliability, timeliness and system lifetime. The limited communication range of WSN nodes, link asymmetry and the characteristics of the physical environment lead to a major source of QoS degradation in WSNs. This paper proposes a Link Reliability based Two-Hop Routing protocol for wireless Sensor Networks (WSNs). The protocol achieves to reduce packet deadline miss ratio while consid- ering link reliability, two-hop velocity and power efficiency and utilizes memory and computational effective methods for estimating the link metrics. Numerical results provide insights that the protocol has a lower packet deadline miss ratio and longer sensor network lifetime. The results show that the proposed protocol is a feasible solution to the QoS routing problem in wireless sensor networks that support real-time applications.

cs.NI↗

arXiv2014

Improved Bully Election Algorithm for Distributed Systems

Electing a leader is a classical problem in distributed computing system. Synchronization between processes often requires one process acting as a coordinator. If an elected leader node fails, the other nodes of the system need to elect another leader without much wasting of time. The bully algorithm is a classical approach for electing a leader in a synchronous distributed computing system, which is used to determine the process with highest priority number as the coordinator. In this paper, we have discussed the limitations of Bully algorithm and proposed a simple and efficient method for the Bully algorithm which reduces the number of messages during the election. Our analytical simulation shows that, our proposed algorithm is more efficient than the Bully algorithm with fewer messages passing and fewer stages.

cs.DC↗

arXiv2014

EDOCR: Energy Density On-demand Cluster Routing in Wireless Sensor Networks

Energy management is one of the critical parameters in Wireless Sensor Networks. In this paper we attempt for a solution to balance the energy usage for maximizing the network lifetime, increase the packet delivery ratio and throughput. Our proposed algorithm is based on Energy Density of the clusters in Wireless Sensor Networks. The cluster head is selected using two step method and on-demand routing approach to calculate the balanced energy shortest path from source to sink. This unique approach maintains the balanced energy utilization among all nodes by selecting the different cluster heads dynamically. Our simulation results have compared with one of the plain routing scheme (EBRP) and cluster based routing (TSCHS), which shows the significant improvements in minimizing the delay and energy utilization and maximizing the network lifetime and throughput with respect to these works.

cs.NI↗

arXiv2013

DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams

Similarity matching and join of time series data streams has gained a lot of relevance in today's world that has large streaming data. This process finds wide scale application in the areas of location tracking, sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream increases, the cost involved to retain all the data in order to aid the process of similarity matching also increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into a compact representation such that it retains all the crucial information by a technique called Multi-level Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have performed exhaustive experimental trials to show that the proposed framework is both efficient and competent in comparison with earlier works.

cs.DB↗

arXiv2009

Fingerprint Verification based on Gabor Filter Enhancement

Human fingerprints are reliable characteristics for personnel identification as it is unique and persistence. A fingerprint pattern consists of ridges, valleys and minutiae. In this paper we propose Fingerprint Verification based on Gabor Filter Enhancement (FVGFE) algorithm for minutiae feature extraction and post processing based on 9 pixel neighborhood. A global feature extraction and fingerprints enhancement are based on Hong enhancement method which is simultaneously able to extract local ridge orientation and ridge frequency. It is observed that the Sensitivity and Specificity values are better compared to the existing algorithms.

cs.CR↗