A performance comparison of three wireless multi hop ad-hoc network routing protocols when streaming MPEG4 traffic

Mobile ad-hoc networks are characterised by constant topology changes, the absence of fixed infrastructure and lack of any centralised control. Traditional routing algorithms prove to be inefficient in such a changing environment. Ad-hoc routing protocols such as dynamic source routing (DSR), ad-hoc on-demand distance vector routing (AODV) and destination-sequence distance vector (DSDV) have been proposed to solve the multi hop routing problem in ad-hoc networks. Performance studies of these routing protocols have assumed constant bit rate (CBR) traffic. Real-time multimedia traffic generated by video-on demand and teleconferencing services are mostly variable bit rate (VBR) traffic. Most of these multimedia traffic is encoded using the MPEG standard. (ISO moving picture expert group). When video traffic is transferred over MANETs a series of performance issues arise. In this paper we present a performance comparison of three ad-hoc routing protocols - DSR, AODV and DSDV when streaming MPEG4 traffic. Simulation studies show that DSDV performs better than AODV and DSR. However all three protocols fail to provide good performance in large, highly mobile network environments. Further study is required to improve the performance of these protocols in mobile ad-hoc networks offering VBR services.

An evaluation of streaming digital video resources in on-and off-campus engineering management education

A recent television documentary on the Columbia space shuttle disaster was converted to streaming digital video format for educational use by on- and off-campus students in an engineering management study unit examining issues in professional engineering ethics. An evaluation was conducted to assess the effectiveness of this new resource. Use of the video was optional, and about half of the class reported using the video, though usage was 90.0% for off-campus students. Most on-campus students accessed the video on-line, while all off-campus students accessed the video via CD-ROM. Off-campus students rated the educational value of the video higher than on-campus students, and were more likely to indicate that the video helped them understand the issues being studied. Most students were able to view the videos without any technical playback problems.

Implementing an online audio lecture streaming facility

This paper describes our experiences in implementing an audio lecture streaming facility for Deakin University. For many years Deakin students have benefited from some of the most comprehensive printed study notes of any university in Australia. In 2002, portable digital audio recorders were utilised by academic staff to capture lecture presentations in order to supplement existing unit learning materials and teaching delivery methods. Audio recordings were processed to enable streamed access via the web browser interface using QuickTime. A trial of incorporating PowerPoint presentations was conducted on a limited basis. 68 undergraduate and postgraduate units implemented lecture streaming. This represented over1700 lecture recordings and 20000 audio streams. Evaluation findings indicate that students find this facility highly valuable to their studies and regularly access the audio recordings throughout semester. Benefits include; access to lecture presentations for off-campus enrolled students, the ability to revisit lecture presentations, and the ability to study at a place and time of convenience. Future enhancement to the audio lecture streaming may include implementing a hard-wired audio capture system into lecture theatres and providing for a more rapid turn around of audio processing.

On the communal analysis suspicion scoring for identity crime in streaming credit applications

This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for application-pairs, the incorporation of temporal and spatial weights, and smoothed k-wise scoring of multiple linked application-pairs. Results on mining several hundred thousand real credit applications demonstrate that CASS reduces false alarm rates while maintaining reasonable hit rates. CASS is scalable for this large data sample, and can rapidly detect early symptoms of identity crime. In addition, new insights have been observed from the relationships between applications.

Video streaming: Implementation and evaluation in an undergraduate nursing program

Video streaming technology enables video content, held on the web sites, to be streamed via the web. We report the implementation and evaluation of video streaming in an undergraduate nursing program in a metropolitan university in Australia. Students (n = 703) were emailed a survey with a 15% response rate. We found that 91% (n = 74) of respondents stated that video streaming assisted their learning. Forty-six percent(n = 50) of students had difficulty accessing video streaming (particularly at the beginning of the study period). Over a 97-day period there were 8440 “hits” to the site from 1039 different internet protocol (IP) addresses. There were 4475 video streaming sessions undertaken by users. Video streaming was used for reviewing previously attended lectures (52%, n = 56), examination preparation (34%, n = 37), viewing missed lectures (27%, n = 29) and class preparation (9%, n = 10). Our experience with the introduction of video streaming has met with general enthusiasm from both students and teaching staff. Video streaming has particular relevance for rural students.

Sacrificial girls : a case study of the impact of streaming and setting on gender reform

This article reports on research funded by the Australian Research Council to investigate school responses to gender equity. It addresses the efforts of a disadvantaged school to tackle what they perceived to be gender inequalities, but in the process of constructing a top-set and bottom-set/ stream class they are developing new forms of old inequalities and new forms of inequalities. This research indicates that despite popular assertions that girls’ education has become the priority of schools and education systems, girls are being further disadvantaged through attempts to implement market strategies coupled with gender reform agendas grounded in liberal notions of equity and relying on unsophisticated notions of affirmative action. In addition, this study highlights the extent to which a media-driven debate about boys’ education has influenced the constitution of boys as the ‘new disadvantaged’ with the capacity to determine the nature of gender reform agendas and programmes in schools.

Streaming variational inference for dirichlet process mixtures

Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they re-quire truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our ex-periments demonstrate the usefulness of our framework in both synthetic and real-world data.

Maximizing real-time streaming services based on a multi-servers networking framework

In recent years, we have witnessed substantial exploitation of real-time streaming applications, such as video surveillance system on road crosses of a city. So far, real world applications mainly rely on the traditional well-known client-server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the processing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi-server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real-time streams delivery and storage. Based on this framework, we further introduce a (1-?) approximation algorithm to solve the NP-complete "maximum services"(MS) problem with the intention of handling large number of streaming flows originated by networks and maximizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on simulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme significantly outperforms the traditional solutions.

A QoE centric distributed caching approach for vehicular video streaming in cellular networks

Distributed caching-empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized-reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%.

A performance evaluation of machine learning-based streaming spam tweets detection

The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.