Recently, a broad range of ENS applications have appeared for large-scale systems, introducing new requirements leading to new embedded architectures, associated algorithms, and supporting software systems. These new requirements include the need for diverse and complex sensor systems that present demands for energy and computational resources as well as for broadband communication. To satisfy application demands while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. In this thesis we described the LEAP design approach, in which the system is able to adaptively select the most energy efficient hardware components matching an application's needs. The LEAP approach supports highly dynamic requirements in sensing fidelity, computational load, storage media, and network bandwidth. It focuses on episodic operation of each component and considers the energy dissipation for each platform task by integrating fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems, including sensors. In addition to LEAP's unique hardware capabilities, its software architecture has been designed to provide an easy to use power management interface, a robust, fault tolerant operating environment, and to enable remote upgrade of individual software components.

Current research topics such as mobile computing and embedded networked sensing (ENS) have been addressing energy efficiency as a cornerstone necessity, due to their requirement for portability and long battery life times. This thesis discusses one such related project that, while currently directed toward ENS computing applications, is generally applicable to a wide ranging set of applications including both mobile and enterprise computing. While relevant to many applications, it is focuses on ENS environments necessitating high performance computing, networking, and storage systems while maintaining low average power operations.

This paper describes a new embedded networked sensor platform architecture that combines hardware and software tools providing detailed, fine-grained real-time energy usage information. We introduce the LEAP2 platform, a qualitative step forward over the previously developed LEAP and other similar platforms. LEAP2 is based on a new low power ASIC system and generally applicable supporting architecture that provides unprecedented capabilities for directly observing energy usage of multiple subsystems in real-time. Real-time observation with microsecond-scale time resolution now enables direct accounting of energy dissipation for each computing task as well as for each hardware subsystem. This new hardware architecture is exploited with our new software tools, etop and endoscope. A series of experimental investigations provide high-resolution power information in networking, storage, memory and processing for primary embedded networked sensing applications. Using these results obtained in real-time we show that for a large class of wireless sensor network nodes, there exist several interdependencies in energy consumption between different subsystems. Through the use of our measurement tools we demonstrate that by carefully selecting the system operating points, energy savings of over 60% can be achieved while retaining system performance.

This paper presents a novel energy attribution and accounting architecture for multi-core systems that can provide accurate, per-process energy information of individual hardware components. We introduce a hardwareassisted direct energy measurement system that integrates seamlessly with the host platform and provides detailed energy information of multiple hardware elements at millisecond-scale time resolution. We also introduce a performance counter based behavioral model that provides indirect information on the proportional energy consumption of concurrently executing processes in the system. We fuse the direct and indirect measurement information into a low-overhead kernel-based energy apportion and accounting software system that provides unprecedented visibility of per-process CPU and RAM energy consumption information on multi-core systems. Through experimentation we show that our energy apportioning system achieves an accuracy of at least 96% while impacting CPU performance by less than 0:6%.

Tiered sensor networks are gaining currency. We propose a mathematical optimization based algorithm to compose data-fusion services in sensor networks with a decomposition technique to effectively load balance it among the microserver nodes in the network. We also provide a thorough evaluation of localization and a formulation for routing using this framework. In a network with LEAP2-like nodes which can switch functionality between that of a mote and that of a master, we intend to study dynamic reconfiguration algorithms to improve lifetime.

Dual-radio, dual-processor nodes are an emerging class of Wireless Sensor Network devices that provide both low- energy operation as well as substantially increased computational performance and communication bandwidth for applications. In such systems, the secondary radio and processor operates with sufficiently low power that it may remain always vigilant, while the the main processor and primary, high-bandwidth radio remain off until triggered by the application. By exploiting the high energy efficiency of the main processor and primary radio along with proper usage, net operating energy benefits are enabled for applications. The secondary radio provides a constantly available multi-hop network, while paths in the primary network exist only when required. This paper describes a topology control mechanism for establishing an end-to-end path in a network of dual-radio nodes using the secondary radios as a control channel to selectively wake up nodes along the required end-to-end path. Using numerical models as well as testbed experimentation, we show that our proposed mechanism provides significant energy savings of more than 60% compared to alternative approaches, and that it incurs only moderately greater application latency.

Recent advancement in microsensor technology permits miniaturization of conventional physiological sensors. Combined with low-power, energy-aware embedded systems and low power wireless interfaces, theses sensors now enable patient monitoring in home and workplace environments in addition to the clinic. Low energy operation is critical for meeting long operating lifetime requirement; an energy-aware wearable system is therefore particularly beneficial to adaptively profile and manage energy utilization. Furthermore, important challenges appear as some of these important physiological sensors, such as electrocardiographs (ECG), introduce large energy demand (because of the need for high sampling rate and resolution) and limitations (due to reduced convenience of user wearability). Energy usage of the distributed sensor systems may be reduced by activating and deactivating sensors according to real-time measurement demand as well as energy consumption characteristics. Our results show that with proper adaptive measurement scheduling, an ECG signal from a subject may be needed for analysis only at certain times, such as during or after an exercise activity. This demonstrates that autonomous systems may rely on low energy cost sensors combined with real time computation to determine patient context with high certainty diagnostics and apply this information to properly schedule use of high cost sensors (e.g. ECG sensor systems).

We have implemented a wearable system based on standard widely-used handheld computing hardware components. This system relies on a new software architecture and an embedded inference engine developed for theses standard platforms. The performance of the system is evaluated using experimental data sets acquired for subjects wearing this system during an exercise sequence. This same approach can be used in context-aware monitoring of diverse physiological signals in a patient’s daily life. Furthermore, a new energy-aware wearable system is introduced. It is capable of performing real-time energy profiling on major components through a convenient software interface. Exploring the techniques on how to utilize this energy information and optimize the existing context-aware algorithm is the focus of future work.

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. The LEAP2 platform is a second generation LEAP system with even higher resolution energy monitoring as well as the unique ability to do per process and per application energy profiling via a dedicated high performance ASIC. This poster will demonstrate the hardware platform capabilities as well as the energy-aware software currently available for LEAP2.

A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. The LEAP2 platform is a second generation LEAP system with even higher resolution energy monitoring as well as the unique ability to do per process and per application energy profiling via a dedicated high performance ASIC. This poster will demonstrate the hardware platform capabilities as well as the energy-aware software currently available for LEAP2.