A scheme for efficient peer-to-peer live video streaming over wireless mesh networks

Peers in a Peer-to-Peer (P2P) live video streaming system over hybrid wireless mesh networks (WMNs) enjoy high video quality when both random network coding (RNC) and an efficient hybrid routing protocol are employed. Although RNC is the most recently used method of efficient video streaming, it imposes high transmission overhead and decoding computational complexity on the network which reduces the perceived video quality. Besides that, RNC cannot guaranty a non-existence of linear dependency in the generated coefficients matrix. In WMNs, node mobility has not been efficiently addressed by current hybrid routing protocols that increase video distortion which would lead to low video quality. In addition, these protocols cannot efficiently support nodes which operate in infrastructure mode. Therefore, the purpose of this research is to propose a P2P live video streaming scheme which consists of two phases followed by the integration of these two phases known as the third phase to provide high video quality in hybrid WMNs. In the first phase, a novel coefficients matrix generation and inversion method has been proposed to address the mentioned limitations of RNC. In the second phase, the proposed enhanced hybrid routing protocol was used to efficiently route video streams among nodes using the most stable path with low routing overhead. Moreover, this protocol effectively supports mobility and nodes which operate in infrastructure mode by exploiting the advantages of the designed locator service. Results of simulations from the first phase showed that video distortion as the most important performance metric in live video streaming, had improved by 36 percent in comparison with current RNC method which employs the Gauss-Jordan Elimination (RNC-GJE) method in decoding. Other metrics including frame dependency distortion, initial start-up delay and end-to-end delay have also improved using the proposed method. Based on previous studies, although Reactive (DYMO) routing protocol provides better performance than other existing routing protocols in a hybrid WMN, the proposed protocol in the second phase had average improvements in video distortion of l86% for hybrid wireless mesh protocol (HWMP), 49% for Reactive (Dynamic MANET On-Demand-DYMO), 75% for Proactive (Optimized Link State Routing-OLSR), and 60% for Ad-hoc on-demand Distance Vector Spanning-Tree (AODV-ST). Other metrics including end-to-end delay, packet delay variation, routing overhead and number of delivered video frames have also improved using the proposed protocol. Finally, the third phase, an integration of the first two phases has proven to be an efficient scheme for high quality P2P live video streaming over hybrid WMNs. This video streaming scheme had averagely improved video distortion by 41%, frame dependency distortion by 50%, initial start-up delay by 15% and end-to-end delay by 33% in comparison with the average introduced values by three other considered integration cases which are Reactive and RNC-GJE, Reactive and the first phase, the second phase and RNC-GJE

Personal Smart Assistant for Digital Media and Advertisement

The expansion of the cyberspace and the enormous process in computing and software applications enabled technology to cover every aspect of our life, therefore, many of our goals are now technology driven. Consequently, the need of intelligent assistance to achieve these goals has increased. However, for this assistance to be beneficial for users, it should be targeted to them based on their needs and preferences. Intelligent software agents have been recognized as a promising approach for the development of user-centric, personalized, applications. In this thesis a generic personal smart assistant agent is proposed that provides relevant assistance to the user based on modeling his/her interests and behaviours. The main focus of this work is on developing a user behaviour model that captures the deliberative and reactive behaviours of the user in open environments. Furthermore, a prototype is built to utilize the personal assistant for personalized advertisement applications, where this assistant attempts to recommend the right advertisement to the right person at the right time

Data-Driven Understanding of Smart Service Systems Through Text Mining

Smart service systems are everywhere, in homes and in the transportation, energy, and healthcare sectors. However, such systems have yet to be fully understood in the literature. Given the widespread applications of and research on smart service systems, we used text mining to develop a unified understanding of such systems in a data-driven way. Specifically, we used a combination of metrics and machine learning algorithms to preprocess and analyze text data related to smart service systems, including text from the scientific literature and news articles. By analyzing 5,378 scientific articles and 1,234 news articles, we identify important keywords, 16 research topics, 4 technology factors, and 13 application areas. We define ???smart service system??? based on the analytics results. Furthermore, we discuss the theoretical and methodological implications of our work, such as the 5Cs (connection, collection, computation, and communications for co-creation) of smart service systems and the text mining approach to understand service research topics. We believe this work, which aims to establish common ground for understanding these systems across multiple disciplinary perspectives, will encourage further research and development of modern service systems

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure