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Muthulakshmi A.,MOP Vaishnav College for Women | Shyamala K.,Drambedkar College
International Journal of Applied Engineering Research | Year: 2015

The emergence of Wireless Body Area Networks (WBAN) has paved the way for patient friendly health monitoring. WBAN, a wireless network of sensors is able to monitor the patient’s condition collaboratively and provide a proactive health care service, relieving them from the hospital environment. Monitoring the vital signs of the patients and reporting any emergency situation without any delay is the main goal of any WBAN. The MAC protocol which takes care of access mechanisms of wireless channel plays a major role in deciding the delay and reliability constraints of data packets. In this paper we have discussed about various MAC protocols for WBAN that handles emergency and prioritized data transmission, also we have studied in detail the efficiency of two important standards IEEE 802.15.4 and IEEE 802.15.6 in handling emergency situation. The simulation studies shows that IEEE 802.15.6 (BaselineMAC) performs much better in terms of Packet delivery ratio (99%) and end-to-end delay (30ms) when compared to IEEE 802.15.4 (ZigBeeMAC), but with respect to energy consumption and delivery of normal packets IEEE 802.15.4 outperforms IEEE 802.15.6. © Research India Publications. Source

Kumaresh S.,MOP Vaishnav College for Women | Baskaran R.,Anna University
Proceedings of the 2012 International Conference on Recent Advances in Computing and Software Systems, RACSS 2012 | Year: 2012

Practitioners come across many defects that impede the rapid progress of software process development, which is critical to the Organization's operation and growth [11]. In order to improve the quality of software development process, a phase-based Defect Removal Model (DRM) is used in this study to judge the Inspection/Test effectiveness and to find out the count of defects that remains at the exit of each phase. The aim of this study is to reduce the defects at the exit of a phase so that defects do not penetrate to subsequent stages, and to improve the Inspection/Test effectiveness. This has been achieved in this study by proposing a Strategy for Process Improvement (SPI). SPI insists that reviews and inspections of software artefacts should be done by a special independent team called Q-GoD (Quality-Guard of Defects) in addition to the Internal Review Team throughout the development life cycle for identifying defects that escape review/inspection process thereby improving software quality. © 2012 IEEE. Source

Kumaresh S.,MOP Vaishnav College for Women | Baskaran R.,Anna University
Journal of Communications Software and Systems | Year: 2015

Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to reduce similar defects that might get reported in future releases. Classification, if performed manually, will consume more time and effort. Human resource having expert testing skills & domain knowledge will be required for labeling the data. Therefore, the need of automatic classification of software defect is high. This work attempts to categorize defects by proposing an algorithm called Software Defect CLustering (SDCL). It aims at mining the existing online bug repositories like Eclipse, Bugzilla and JIRA for analyzing the defect description and its categorization. The proposed algorithm is designed by using text clustering and works with three major modules to find out the class to which the defect should be assigned. Software bug repositories hold software defect data with attributes like defect description, status, defect open and close date. Defect extraction module extracts the defect description from various bug repositories and converts it into unified format for further processing. Unnecessary and irrelevant texts are removed from defect data using data preprocessing module. Finally grouping of defect data into clusters of similar defect is done using clustering technique. The algorithm provides classification accuracy more than 80% in all of the three above mentioned repositories. © 2015 CCIS. Source

Muthulakshmi A.,MOP Vaishnav College for Women | Shyamala K.,Drambedkar Government College
International Journal of Engineering and Technology | Year: 2016

Wireless Body Area Network (WBAN), a network of sensor nodes placed in and around human body to monitor the patient's health condition remotely in their day to day routine life without the person being in the hospital environment is the mostenvisioned area of WSN research. The main objective of WBAN is to deliver the patient's vital signs during the emergency conditions with minimum time delay and maximum reliability. The emergency packets generated by sensor nodes in WBAN during such emergency situation should be provided prioritized service. This paper discusses a multiple Queuing technique to minimize the time delay of emergency packets. The emergency situation is phased based on the threshold value of the parameter being measured which results in high priority and low priority packets. Three types of packets generated by the nodes are prioritized in the following order, High Priority packets, Low priority packets and Normal packets. Simulation studies with varying number of nodes shows that nearly 90% of the low priority packets and 85% of the high priority packets are delivered within 30ms time delay with multiple queuing mechanism as compared to 60% (high priority packets) and 58% (low priority packets) with single queue. Source

Kumaresh S.,MOP Vaishnav College for Women | Baskaran R.,Anna University
Journal of Theoretical and Applied Information Technology | Year: 2015

Automated bug report clustering and classification plays a significant role in managing, assigning, and understanding the bug categories. The most challenging problem in bug report classification is the inadequate amount of labeled dataset. The proposed framework introduces an Ontology-assisted Semisupervised Clustering Based Classification (OS-CBC) for bug reports amid a small size of the labeled dataset scenario. The proposed approach enriches the data set of the bug report using constructed Bug and Enriched Meta-feature Extraction (BEME) ontology. Semantic constraints based semi-supervised hierarchical clustering (Semantic-HAC) algorithm prioritizes the constraints for clustering the bug reports based on the BEME ontology. The cluster formation of bug reports depends on the transitive dissimilarity and ultrametric distance using ontology-based prioritized constraints. It extends the dataset (stretched) of the bug reports based on the maximum likelihood of the features in the cluster for labeling the unlabeled data. Moreover, the proposed approach categorizes the bug reports of stretched test set under the category of training set label using Multi-label Naive Bayes (MLNB) classifier. The classification technique focuses on the threshold based filtered weight of each term in the training set to improve the accuracy. The proposed OS-CBC approach significantly improves the classification accuracy of the bug reports. © 2005 - 2015 JATIT & LLS. All rights reserved. Source

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