Enhancing Mobile Capacity through Generic and Efficient Resource Sharing

Mobile computing devices are becoming indispensable in every aspect of human life, but diverse hardware limits make current mobile devices far from ideal for satisfying the performance requirements of modern mobile applications and being used anytime, anywhere. Mobile Cloud Computing (MCC) could be a viable solution to bypass these limits which enhances the mobile capacity through cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. Traditional schemes either restrict to share a specific type of hardware resource within individual applications, which requires tremendous reprogramming efforts; or disregard the runtime execution pattern and transmit too much unnecessary data, resulting in bandwidth and energy waste.To address the aforementioned challenges, we present three novel designs of resource sharing frameworks which utilize the various system resources from a remote or personal cloud to enhance the mobile capacity in a generic and efficient manner. First, we propose a novel method-level offloading methodology to run the mobile computational workload on the remote cloud CPU. Minimized data transmission is achieved during such offloading by identifying and selectively migrating the memory contexts which are necessary to the method execution. Second, we present a systematic framework to maximize the mobile performance of graphics rendering with the remote cloud GPU, during which the redundant pixels across consecutive frames are reused to reduce the transmitted frame data. Last, we propose to exploit the unified mobile OS services and generically interconnect heterogeneous mobile devices towards a personal mobile cloud, which complement and flexibly share mobile peripherals (e.g., sensors, camera) with each other

Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps

Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile app? This thesis answers this question for two kinds of systems, a single mobile device and a mobile cloud computing system. First, two privacy attacks under the same framework are proposed based on supervised learning algorithms. Then these attacks are implemented and explored in a mobile device and in a cloud computing environment. Experimental evaluations show that the type of app can be inferred with high probability. In particular, the attacks achieve up to 100% accuracy on a mobile device, and 66.7% accuracy in the mobile cloud computing environment. This study shows that resource usage patterns of mobile apps can be used to infer the type of apps being used, and thus can cause privacy leakage if not protected

Universal Mobile Service Execution Framework for Device-To-Device Collaborations

There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or significantly overburdened, such as on a battlefield, disaster zones, isolated rural areas, or crowded public venues. To enable collaboration among the devices in opportunistic networks, code offloading and Remote Method Invocation are the two major mechanisms to ensure code portions of applications are successfully transmitted to and executed on the remote platforms. Although these domains are highly enjoyed in research for a decade, the limitations of multi-device connectivity, system error handling or cross platform compatibility prohibit these technologies from being broadly applied in the mobile industry. To address the above problems, we designed and developed UMSEF - an Universal Mobile Service Execution Framework, which is an innovative and radical approach for mobile computing in opportunistic networks. Our solution is built as a component-based mobile middleware architecture that is flexible and adaptive with multiple network topologies, tolerant for network errors and compatible for multiple platforms. We provided an effective algorithm to estimate the resource availability of a device for higher performance and energy consumption and a novel platform for mobile remote method invocation based on declarative annotations over multi-group device networks. The experiments in reality exposes our approach not only achieve the better performance and energy consumption, but can be extended to large-scaled ubiquitous or IoT systems

Designing Wireless Networks for Delay-Sensitive Internet of Things

Internet of Things (IoT) applications have stringent requirements on the wireless network delay, but have to share and compete for the limited bandwidth with other wireless traffic. Traditional schemes adopt various QoS-aware traffic scheduling techniques, but fail when the amount of network traffic further increases. In addition, CSMA with collision avoidance (CSMA/CA) mechanism enables the coexistence of multiple wireless links but avoids concurrent transmissions, yielding severe channel access delay on the delay-sensitive traffic when the channel is busy. To address the aforementioned limitations, we present two novel designs of wireless side channel, which operate concurrently with the existing wireless network channel without occupying extra spectrum, but dedicates to real-time traffic. Our key insight of realizing such side channel is to exploit the excessive SNR margin in the wireless network by encoding data as patterned interference. First, we design such patterned interference in form of energy erasure over specific subcarriers in OFDM systems. Delay-sensitive messages can be delivered simultaneously along with other traffic from the same transmitter, which reduces the network queuing delay. Furthermore, we propose EasyPass, another side channel design that encodes data in the same OFDM scheme as being used by the main channel, but using weaker power and narrower frequency bands. By adapting the side channel's transmit power under the main channel's SNR margin, the simultaneous main channel transmission would suffer little degradation. EasyPass reduces the channel access delay by providing extra transmission opportunities when the channel is occupied by other links. Last, we present a novel modulation design that transforms the choices of link rate adaptation from discrete to continuous. With minimum extra overhead, it improves the network throughput and therefore reduces the network delay