Energy-Aware System-Level Design of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are heterogeneous systems in which one or several computational cores interact with the physical environment. This interaction is typically performed through electromechanical elements such as sensors and actuators. Many CPSs operate as part of a network and some of them present a constrained energy budget (for example, they are battery powered). Examples of energy constrained CPSs could be a mobile robot, the nodes that compose a Body Area Network or a pacemaker. The heterogeneity present in the composition of CPSs together with the constrained energy availability makes these systems challenging to design. A way to tackle both complexity and costs is the application of abstract modelling and simulation. This thesis proposed the application of modelling at the system level, taking energy consumption in the different kinds of subsystems into consideration. By adopting this cross disciplinary approach to energy consumption it is possible to decrease it effectively. The results of this thesis are a number of modelling guidelines and tool improvements to support this kind of holistic analysis, covering energy consumption in electromechanical, computation and communication subsystems. From a methodological point of view these have been framed within a V-lifecycle. Finally, this approach has been demonstrated on two case studies from the medical domain enabling the exploration of alternative systems architectures and producing energy consumption estimates to conduct trade-off analysis

Scalable and Energy Efficient Software Architecture for Human Behavioral Measurements

Understanding human behavior is central to many professions including engineering, health and the social sciences, and has typically been measured through surveys, direct observation and interviews. However, these methods are known to have drawbacks, including bias, problems with recall accuracy, and low temporal fidelity. Modern mobile phones have a variety of sensors that can be used to find activity patterns and infer the underlying human behaviors, placing a heavy load on the phone's battery. Social science researchers hoping to leverage this new technology must carefully balance the fidelity of the data with the cost in phone performance. Crucially, many of the data collected are of limited utility because they are redundant or unnecessary for a particular study question. Previous researchers have attempted to address this problem by modifying the measurement schedule based on sensed context, but a complete solution remains elusive. In the approach described here, measurement is made contingent on sensed context and measurement objectives through extensions to a configuration language, allowing significant improvement to flexibility and reliability. Empirical studies indicate a significant improvement in energy efficiency with acceptable losses in data fidelity

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Towards Confident Body Sensor Networking

With the recent technology advances of wireless communication and lightweight low-power sensors, Body Sensor Network (BSN) is made possible. More and more researchers are interested in developing numerous novel BSN applications, such as remote health/fitness monitoring, military and sport training, interactive gaming, personal information sharing, and secure authentication. Despite the unstable wireless communication, various confidence requirements are placed on the BSN networking service. This thesis aims to provide Quality of Service (QoS) solutions for BSN communication, in order to achieve the required confidence goals.;We develop communication quality solutions to satisfy confidence requirements from both the communication and application levels, in single and multiple BSNs. First, we build communication QoS, targeting at providing service quality guarantees in terms of throughput and time delay on the communication level. More specifically, considering the heterogeneous BSN platform in a real deployment, we develop a radio-agnostic solution for wireless resource scheduling in the BSN. Second, we provide a QoS solution for both inter- and intra-BSN communications when more than one BSNs are involved. Third, we define application fidelity for two neurometric applications as examples, and bridge a connection between the communication QoS and application QoS

Dependable Computing on Inexact Hardware through Anomaly Detection.

Reliability of transistors is on the decline as transistors continue to shrink in size. Aggressive voltage scaling is making the problem even worse. Scaled-down transistors are more susceptible to transient faults as well as permanent in-field hardware failures. In order to continue to reap the benefits of technology scaling, it has become imperative to tackle the challenges risen due to the decreasing reliability of devices for the mainstream commodity market. Along with the worsening reliability, achieving energy efficiency and performance improvement by scaling is increasingly providing diminishing marginal returns. More than any other time in history, the semiconductor industry faces the crossroad of unreliability and the need to improve energy efficiency. These challenges of technology scaling can be tackled by categorizing the target applications in the following two categories: traditional applications that have relatively strict correctness requirement on outputs and emerging class of soft applications, from various domains such as multimedia, machine learning, and computer vision, that are inherently inaccuracy tolerant to a certain degree. Traditional applications can be protected against hardware failures by low-cost detection and protection methods while soft applications can trade off quality of outputs to achieve better performance or energy efficiency. For traditional applications, I propose an efficient, software-only application analysis and transformation solution to detect data and control flow transient faults. The intelligence of the data flow solution lies in the use of dynamic application information such as control flow, memory and value profiling. The control flow protection technique achieves its efficiency by simplifying signature calculations in each basic block and by performing checking at a coarse-grain level. For soft applications, I develop a quality control technique. The quality control technique employs continuous, light-weight checkers to ensure that the approximation is controlled and application output is acceptable. Overall, I show that the use of low-cost checkers to produce dependable results on commodity systems---constructed from inexact hardware components---is efficient and practical.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113341/1/dskhudia_1.pd

System-level design of energy-efficient sensor-based human activity recognition systems: a model-based approach

This thesis contributes an evaluation of state-of-the-art dataflow models of computation regarding their suitability for a model-based design and analysis of human activity recognition systems, in terms of expressiveness and analyzability, as well as model accuracy. Different aspects of state-of-the-art human activity recognition systems have been modeled and analyzed. Based on existing methods, novel analysis approaches have been developed to acquire extra-functional properties like processor utilization, data communication rates, and finally energy consumption of the system

Amulet: An Energy-Efficient, Multi-Application Wearable Platform

Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage.\r\n\r\nWe introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime