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Description
With tremendous increase in the popularity of networked multimedia applications, video data is expected to account for a large portion of the traffic on the Internet and more importantly next-generation wireless systems. To be able to satisfy a broad range of customers requirements, two major problems need to be solved. The first problem is the need for a scalable representation of the input video. The recently developed scalable extension of the state-of-the art H.264/MPEG-4 AVC video coding standard, also known as H.264/SVC (Scalable Video Coding) provides a solution to this problem. The second problem is that wireless transmission medium typically introduce errors in the bit stream due to noise, congestion and fading on the channel. Protection against these channel impairments can be realized by the use of forward error correcting (FEC) codes. In this research study, the performance of scalable video coding in the presence of bit errors is studied. The encoded video is channel coded using Reed Solomon codes to provide acceptable performance in the presence of channel impairments. In the scalable bit stream, some parts of the bit stream are more important than other parts. Parity bytes are assigned to the video packets based on their importance in unequal error protection scheme. In equal error protection scheme, parity bytes are assigned based on the length of the message. A quantitative comparison of the two schemes, along with the case where no channel coding is employed is performed. H.264 SVC single layer video streams for long video sequences of different genres is considered in this study which serves as a means of effective video characterization. JSVM reference software, in its current version, does not support decoding of erroneous bit streams. A framework to obtain H.264 SVC compatible bit stream is modeled in this study. It is concluded that assigning of parity bytes based on the distribution of data for different types of frames provides optimum performance. Application of error protection to the bit stream enhances the quality of the decoded video with minimal overhead added to the bit stream.
ContributorsSundararaman, Hari (Author) / Reisslein, Martin (Thesis advisor) / Seeling, Patrick (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2011
Description
The Internet of Things (IoT) has become a more pervasive part of everyday life. IoT networks such as wireless sensor networks, depend greatly on the limiting unnecessary power consumption. As such, providing low-power, adaptable software can greatly improve network design. For streaming live video content, Wireless Video Sensor Network Platform compatible Dynamic Adaptive Streaming over HTTP (WVSNP-DASH) aims to revolutionize wireless segmented video streaming by providing a low-power, adaptable framework to compete with modern DASH players such as Moving Picture Experts Group (MPEG-DASH) and Apple’s Hypertext Transfer Protocol (HTTP) Live Streaming (HLS). Each segment is independently playable, and does not depend on a manifest file, resulting in greatly improved power performance. My work was to show that WVSNP-DASH is capable of further power savings at the level of the wireless sensor node itself if a native capture program is implemented at the camera sensor node. I created a native capture program in the C language that fulfills the name-based segmentation requirements of WVSNP-DASH. I present this program with intent to measure its power consumption on a hardware test-bed in future. To my knowledge, this is the first program to generate WVSNP-DASH playable video segments. The results show that our program could be utilized by WVSNP-DASH, but there are issues with the efficiency, so provided are an additional outline for further improvements.
ContributorsKhan, Zarah (Author) / Reisslein, Martin (Thesis advisor) / Seema, Adolph (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2018
Description
Mobile devices have penetrated into every aspect of modern world. For one thing, they are becoming ubiquitous in daily life. For the other thing, they are storing more and more data, including sensitive data. Therefore, security and privacy of mobile devices are indispensable. This dissertation consists of five parts: two authentication schemes, two attacks, and one countermeasure related to security and privacy of mobile devices.
Specifically, in Chapter 1, I give an overview the challenges and existing solutions in these areas. In Chapter 2, a novel authentication scheme is presented, which is based on a user’s tapping or sliding on the touchscreen of a mobile device. In Chapter 3, I focus on mobile app fingerprinting and propose a method based on analyzing the power profiles of targeted mobile devices. In Chapter 4, I mainly explore a novel liveness detection method for face authentication on mobile devices. In Chapter 5, I investigate a novel keystroke inference attack on mobile devices based on user eye movements. In Chapter 6, a novel authentication scheme is proposed, based on detecting a user’s finger gesture through acoustic sensing. In Chapter 7, I discuss the future work.
Specifically, in Chapter 1, I give an overview the challenges and existing solutions in these areas. In Chapter 2, a novel authentication scheme is presented, which is based on a user’s tapping or sliding on the touchscreen of a mobile device. In Chapter 3, I focus on mobile app fingerprinting and propose a method based on analyzing the power profiles of targeted mobile devices. In Chapter 4, I mainly explore a novel liveness detection method for face authentication on mobile devices. In Chapter 5, I investigate a novel keystroke inference attack on mobile devices based on user eye movements. In Chapter 6, a novel authentication scheme is proposed, based on detecting a user’s finger gesture through acoustic sensing. In Chapter 7, I discuss the future work.
ContributorsChen, Yimin (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Reisslein, Martin (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2018
Description
Software Defined Networking has been the primary component for Quality of Service provisioning in the last decade. The key idea in such networks is producing independence between the control and the data-plane. The control plane essentially provides decision making logic to the data-plane, which in-turn is only responsible for moving the packets from source to destination based on the flow-table entries and actions. In this thesis an in-depth design and analysis of Software Defined Networking control plane architecture for Next Generation Networks is provided. Typically, Next Generation Networks are those that need to satisfy Quality of Service restrictions (like time bounds, priority, hops, to name a few) before the packets are in transit. For instance, applications that are dependent on prediction popularly known as ML/AI applications have heavy resource requirements and require completion of tasks within the time bounds otherwise the scheduling is rendered useless. The bottleneck could be essentially on any layer of the network stack, however in this thesis the focus is on layer-2 and layer-3 scheduling. To that end, the design of an intelligent control plane is proposed by paying attention to the scheduling, routing and admission strategies which are necessary to facilitate the aforementioned applications requirement. Simulation evaluation and comparisons with state of the art approaches is provided withreasons corroborating the design choices. Finally, quantitative metrics are defined and measured to justify the benefits of the designs.
ContributorsBalasubramanian, Venkatraman (Author) / Reisslein, Martin (Thesis advisor) / Suppappola, Antonia Papandreou (Committee member) / Zhang, Yanchao (Committee member) / Thyagaturu, Akhilesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Individuals and organizations have greater access to the world's population than ever before. The effects of Social Media Influence have already impacted the behaviour and actions of the world's population. This research employed mixed methods to investigate the mechanisms to further the understand of how Social Media Influence Campaigns (SMIC) impact the global community as well as develop tools and frameworks to conduct analysis. The research has qualitatively examined the perceptions of Social Media, specifically how leadership believe it will change and it's role within future conflict. This research has developed and tested semantic ontological modelling to provide insights into the nature of network related behaviour of SMICs. This research also developed exemplar data sets of SMICs. The insights gained from initial research were used to train Machine Learning classifiers to identify thematically related campaigns. This work has been conducted in close collaboration with Alliance Plus Network partner, University of New South Wales and the Australian Defence Force.
ContributorsJohnson, Nathan (Author) / Reisslein, Martin (Thesis advisor) / Turnbull, Benjamin (Committee member) / Zhao, Ming (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2022
Description
The Internet-of-Things (IoT) paradigm is reshaping the ways to interact with the physical space. Many emerging IoT applications need to acquire, process, gain insights from, and act upon the massive amount of data continuously produced by ubiquitous IoT sensors. It is nevertheless technically challenging and economically prohibitive for each IoT application to deploy and maintain a dedicated large-scale sensor network over distributed wide geographic areas. Built upon the Sensing-as-a-Service paradigm, cloud-sensing service providers are emerging to provide heterogeneous sensing data to various IoT applications with a shared sensing substrate. Cyber threats are among the biggest obstacles against the faster development of cloud-sensing services. This dissertation presents novel solutions to achieve trustworthy IoT sensing-as-a-service. Chapter 1 introduces the cloud-sensing system architecture and the outline of this dissertation. Chapter 2 presents MagAuth, a secure and usable two-factor authentication scheme that explores commercial off-the-shelf wrist wearables with magnetic strap bands to enhance the security and usability of password-based authentication for touchscreen IoT devices. Chapter 3 presents SmartMagnet, a novel scheme that combines smartphones and cheap magnets to achieve proximity-based access control for IoT devices. Chapter 4 proposes SpecKriging, a new spatial-interpolation technique based on graphic neural networks for secure cooperative spectrum sensing which is an important application of cloud-sensing systems. Chapter 5 proposes a trustworthy multi-transmitter localization scheme based on SpecKriging. Chapter 6 discusses the future work.
ContributorsZhang, Yan (Author) / Zhang, Yanchao YZ (Thesis advisor) / Fan, Deliang (Committee member) / Xue, Guoliang (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Access Networks provide the backbone to the Internet connecting the end-users to
the core network thus forming the most important segment for connectivity. Access
Networks have multiple physical layer medium ranging from fiber cables, to DSL links
and Wireless nodes, creating practically-used hybrid access networks. We explore the
hybrid access network at the Medium ACcess (MAC) Layer which receives packets
segregated as data and control packets, thus providing the needed decoupling of data
and control plane. We utilize the Software Defined Networking (SDN) principle of
centralized processing with segregated data and control plane to further extend the
usability of our algorithms. This dissertation introduces novel techniques in Dynamic
Bandwidth allocation, control message scheduling policy, flow control techniques and
Grouping techniques to provide improved performance in Hybrid Passive Optical Networks (PON) such as PON-xDSL, FiWi etc. Finally, we study the different types of
software defined algorithms in access networks and describe the various open challenges and research directions.
the core network thus forming the most important segment for connectivity. Access
Networks have multiple physical layer medium ranging from fiber cables, to DSL links
and Wireless nodes, creating practically-used hybrid access networks. We explore the
hybrid access network at the Medium ACcess (MAC) Layer which receives packets
segregated as data and control packets, thus providing the needed decoupling of data
and control plane. We utilize the Software Defined Networking (SDN) principle of
centralized processing with segregated data and control plane to further extend the
usability of our algorithms. This dissertation introduces novel techniques in Dynamic
Bandwidth allocation, control message scheduling policy, flow control techniques and
Grouping techniques to provide improved performance in Hybrid Passive Optical Networks (PON) such as PON-xDSL, FiWi etc. Finally, we study the different types of
software defined algorithms in access networks and describe the various open challenges and research directions.
ContributorsMercian, Anu (Author) / Reisslein, Martin (Thesis advisor) / McGarry, Michael P (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
The mobile crowdsensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of information at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing person or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications. These MCS applications require efficient processing and analysis to generate results in real time. A human user, mobile device and their interactions cause a change in context on the mobile device affecting the quality contextual data that is gathered. Usage of MCS data in real-time mobile applications is challenging due to the complex inter-relationship between: a) availability of context, context is available with the mobile phones and not with the cloud, b) cost of data transfer to remote cloud servers, both in terms of communication time and energy, and c) availability of local computational resources on the mobile phone, computation may lead to rapid battery drain or increased response time. The resource-constrained mobile devices need to offload some of their computation.
This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.
This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.
ContributorsPore, Madhurima (Author) / Gupta, Sandeep K. S. (Thesis advisor, Committee member) / Banerjee, Ayan (Committee member) / Reisslein, Martin (Committee member) / CERIN, CHRISTOPHE (Committee member) / Arizona State University (Publisher)
Created2019