Adaptive Multipath Multimedia Streaming Architecture for Mobile Networks with Proactive Buffering Using Mobile Proxies

Real-time multimedia transport has stringent bandwidth, delay and loss requirements. Providing support for such applications in infrastructure-based single hop wireless networks is a great challenge. Since mobile networks are characterized by host mobility, providing continuous streaming service in such an environment is an uphill task. In order to achieve continuous multimedia streaming, we propose an innovative multipath architecture for multimedia streaming. Existing multipath architectures are not efficient for mobile networks, where, in addition to normal streaming requirements we need to handle the frequently occurring hand-offs. In our architecture, multiple paths, identified using an efficient genetic algorithm, are used to provide robust streaming in case of link failures. Dynamic encoding schemes are used in the server to adapt according to network conditions based on the feedback received from the network. In addition hand-offs are predicted proactively and mobile agents containing the buffered data are migrated to the predicted base station. Altogether the architecture provides robust multimedia streaming service under varying network conditions. We have simulated the performance of our architecture using Network Simulator (NS - 2) and the results are promising

Knowledge-based Systems and Interestingness Measures: Analysis with Clinical Datasets

Knowledge mined from clinical data can be used for medical diagnosis and prognosis. By improving the quality of knowledge base, the efficiency of prediction of a knowledge-based system can be enhanced. Designing accurate and precise clinical decision support systems, which use the mined knowledge, is still a broad area of research. This work analyses the variation in classification accuracy for such knowledge-based systems using different rule lists. The purpose of this work is not to improve the prediction accuracy of a decision support system, but analyze the factors that influence the efficiency and design of the knowledge base in a rule-based decision support system. Three benchmark medical datasets are used. Rules are extracted using a supervised machine learning algorithm (PART). Each rule in the ruleset is validated using nine frequently used rule interestingness measures. After calculating the measure values, the rule lists are used for performance evaluation. Experimental results show variation in classification accuracy for different rule lists. Confidence and Laplace measures yield relatively superior accuracy: 81.188% for heart disease dataset and 78.255% for diabetes dataset. The accuracy of the knowledge-based prediction system is predominantly dependent on the organization of the ruleset. Rule length needs to be considered when deciding the rule ordering. Subset of a rule, or combination of rule elements, may form new rules and sometimes be a member of the rule list. Redundant rules should be eliminated. Prior knowledge about the domain will enable knowledge engineers to design a better knowledge base

A Hashing Scheme for Multi-channel Wireless Broadcast

The rapid development of wireless communication technology and battery-powered portable devices is making mobile information services increasingly popular. Since the bandwidth resource of wireless networks is scarce and the mobile devices have a limited battery capacity, any solution for information access must be devised in such a way that time and power consumption for the devices are minimized. Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. This paper proposes a hashing scheme for information access via wireless broadcast through multiple channels in which hash functions are used to index broadcast information across multiple channels. In this scheme, two different hash functions called Primary Hash Function (PHF) and Secondary Hash Function (SHF) are used, where PHF is used to determine the channel in which the desired data item is to be broadcasted and SHF is used to locate the data item within that channel. The proposed hashing scheme reduces both the access latency and tuning time and shortens the broadcast length. Moreover, Access Probabilities of data items and User Profiles that indicate the client behavior in the environment at any given time are considered in this system to construct an efficient broadcast schedule. This broadcast schedule is a non-flat data broadcast that further reduces the average access latency. Finally, Caching techniques are also implemented to further improve the access latency and tuning time

Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets

A Cluster-Based Energy-Efficient Secure Optimal Path-Routing Protocol for Wireless Body-Area Sensor Networks

Recently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical. The existing routing algorithms focus more on energy efficiency than security. However, security attacks will lead to more energy consumption, which will reduce overall network performance. To handle the issues of reliability, energy efficiency, and security in WBAN, a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection