A framework for policy based coordinated adaptation in mobile systems

Adaptation is an important requirement for mobile applications due to the varying levels of resource availability that characterizes mobile environments. However without proper control, multiple applications can each adapt independently in response to a range of different adaptive stimuli, causing conflicts or sub optimal performance. In this thesis we presented a framework, which enables multiple adaptation mechanisms to coexist on one platform. The key component of this framework was the 'Policy Server', which has all the system policies and governs the rules for adaptation. We also simulated our framework and subjected it to various adaptation scenarios to demonstrate the working of the system as a whole. With the help of the simulation it was shown that our framework enables seamless adaptation of multiple applications.

Control of mobile robotic platforms by handheld devices

The intent of this work was to develop a mobile robotic platform that was controlled by a Palm Pilot PDA. Advances in consumer electronics are producing powerful yet small handheld devices. Some of these devices present quasi-PC capabilities for a fraction of the cost; furthermore, they are compact enough that they fit in all but the smallest of platforms. The platform prototype built for testing purposes has a differential-drive configuration to provide simple but agile movement control. The sensor package consisted of two infrared ranging sensors mounted on servomotors that provide a wide area of detection. Building such a platform involved selection of hardware, circuit integration and software development. The software suite selected to develop code for the Palm Pilot was CodeWarrior, a C compiler that can generate code in Palm-native PRC files.

Delay-Sensitive Service Request Scheduling for Cloud Computing

Cloud computing realizes the long-held dream of converting computing capability into a type of utility. It has the potential to fundamentally change the landscape of the IT industry and our way of life. However, as cloud computing expanding substantially in both scale and scope, ensuring its sustainable growth is a critical problem. Service providers have long been suffering from high operational costs. Especially the costs associated with the skyrocketing power consumption of large data centers. In the meantime, while efficient power/energy utilization is indispensable for the sustainable growth of cloud computing, service providers must also satisfy a user's quality of service (QoS) requirements. This problem becomes even more challenging considering the increasingly stringent power/energy and QoS constraints, as well as other factors such as the highly dynamic, heterogeneous, and distributed nature of the computing infrastructures, etc. In this dissertation, we study the problem of delay-sensitive cloud service scheduling for the sustainable development of cloud computing. We first focus our research on the development of scheduling methods for delay-sensitive cloud services on a single server with the goal of maximizing a service provider's profit. We then extend our study to scheduling cloud services in distributed environments. In particular, we develop a queue-based model and derive efficient request dispatching and processing decisions in a multi-electricity-market environment to improve the profits for service providers. We next study a problem of multi-tier service scheduling. By carefully assigning sub deadlines to the service tiers, our approach can significantly improve resource usage efficiencies with statistically guaranteed QoS. Finally, we study the power conscious resource provision problem for service requests with different QoS requirements. By properly sharing computing resources among different requests, our method statistically guarantees all QoS requirements with a minimized number of powered-on servers and thus the power consumptions. The significance of our research is that it is one part of the integrated effort from both industry and academia to ensure the sustainable growth of cloud computing as it continues to evolve and change our society profoundly.

Vertical Handoff Target Selection in a Heterogeneous Wireless Network Using Fuzzy ELECTRE

Global connectivity is on the verge of becoming a reality to provide high-speed, high-quality, and reliable communication channels for mobile devices at anytime, anywhere in the world. In a heterogeneous wireless environment, one of the key ingredients to provide efficient and ubiquitous computing with guaranteed quality and continuity of service is the design of intelligent handoff algorithms. Traditional single-metric handoff decision algorithms, such as Received Signal Strength (RSS), are not efficient and intelligent enough to minimize the number of unnecessary handoffs, decision delays, call-dropping and blocking probabilities. This research presents a novel approach for of a Multi Attribute Decision Making (MADM) model based on an integrated fuzzy approach for target network selection.

Lightweight Middleware for Software Defined Radio (SDR) Inter-Components Communication

The ability to use Software Defined Radio (SDR) in the civilian mobile applications will make it possible for the next generation of mobile devices to handle multi-standard personal wireless devices and ubiquitous wireless devices. The original military standard created many beneficial characteristics for SDR, but resulted in a number of disadvantages as well. Many challenges in commercializing SDR are still the subject of interest in the software radio research community. Four main issues that have been already addressed are performance, size, weight, and power. This investigation presents an in-depth study of SDR inter-components communications in terms of total link delay related to the number of components and packet sizes in systems based on Software Communication Architecture (SCA). The study is based on the investigation of the controlled environment platform. Results suggest that the total link delay does not linearly increase with the number of components and the packet sizes. The closed form expression of the delay was modeled using a logistic function in terms of the number of components and packet sizes. The model performed well when the number of components was large. Based upon the mobility applications, energy consumption has become one of the most crucial limitations. SDR will not only provide flexibility of multi-protocol support, but this desirable feature will also bring a choice of mobile protocols. Having such a variety of choices available creates a problem in the selection of the most appropriate protocol to transmit. An investigation in a real-time algorithm to optimize energy efficiency was also performed. Communication energy models were used including switching estimation to develop a waveform selection algorithm. Simulations were performed to validate the concept.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

Trajectory Privacy Preservation in Mobile Wireless Sensor Networks

In recent years, there has been an enormous growth of location-aware devices, such as GPS embedded cell phones, mobile sensors and radio-frequency identification tags. The age of combining sensing, processing and communication in one device, gives rise to a vast number of applications leading to endless possibilities and a realization of mobile Wireless Sensor Network (mWSN) applications. As computing, sensing and communication become more ubiquitous, trajectory privacy becomes a critical piece of information and an important factor for commercial success. While on the move, sensor nodes continuously transmit data streams of sensed values and spatiotemporal information, known as ``trajectory information". If adversaries can intercept this information, they can monitor the trajectory path and capture the location of the source node. This research stems from the recognition that the wide applicability of mWSNs will remain elusive unless a trajectory privacy preservation mechanism is developed. The outcome seeks to lay a firm foundation in the field of trajectory privacy preservation in mWSNs against external and internal trajectory privacy attacks. First, to prevent external attacks, we particularly investigated a context-based trajectory privacy-aware routing protocol to prevent the eavesdropping attack. Traditional shortest-path oriented routing algorithms give adversaries the possibility to locate the target node in a certain area. We designed the novel privacy-aware routing phase and utilized the trajectory dissimilarity between mobile nodes to mislead adversaries about the location where the message started its journey. Second, to detect internal attacks, we developed a software-based attestation solution to detect compromised nodes. We created the dynamic attestation node chain among neighboring nodes to examine the memory checksum of suspicious nodes. The computation time for memory traversal had been improved compared to the previous work. Finally, we revisited the trust issue in trajectory privacy preservation mechanism designs. We used Bayesian game theory to model and analyze cooperative, selfish and malicious nodes' behaviors in trajectory privacy preservation activities.