The Energy Endoscope: Real-Time Detailed Energy Accounting for Wireless Sensor Nodes

This paper describes a new embedded networked sen-sor platform architecture that combines hardware and soft-ware tools providing detailed, fine-grained real-time en-ergy usage information. We introduce the LEAP2 plat-form, a qualitative step forward over the previously devel-oped LEAP [13] and other similar platforms. LEAP2 is based on a new low power ASIC system and generally appli-cable supporting architecture that provides unprecedented capabilities for directly observing energy usage of multi-ple subsystems in real-time. Real-time observation with microsecond-scale time resolution enables direct account-ing of energy dissipation for each computing task as well as for each hardware subsystem. The new hardware archi-tecture 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 results obtained in real-time we show that for a large class of wireless sensor network nodes, there exist several interdependencies in energy con-sumption between different subsystems. Through the use of our measurement tools we demonstrate that by carefully se-lecting the system operating points, energy savings of over 60 % can be achieved while retaining system performance.

Energy Efficient Computing with the Low Power, Energy Aware Processing (LEAP) Architecture

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

The Energy Endoscope: Real-time Detailed Energy Accounting for Wireless Sensor Nodes

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

Accurate Energy Attribution and Accounting for Multi-core Systems

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%

Node synchronization in a wireless sensor network using unreliable GPS signals

© 2014 IEEE. This paper presents our findings in using pulse measurements from a jittery one pulse per second (pps) global positioning system (GPS) clock, to synchronize the real-time clock (RTC) in each node of a wireless sensor network, when the timing jitter is subject to a empirically determined bimodal non-Gaussian distribution. Specifically, we 1) estimate the RTC phase and align it with an estimate of the true time phase, 2) calibrate the frequency of a 19.2 MHz low-cost temperature compensated crystal oscillator (TCXO) that drives the one pps RTC, and 3) track and compensate TCXO frequency variations due to environmental and aging effects. In our GPS driven synchronization methodology we adopt a statistical signal processing framework to estimate the 2% percentile in the bimodal distribution, perform a long-term frequency calibration with fractional frequency adjustment, and track the changes in the TCXO frequency to within three tick per second over a nominal 19.2 MHz frequency with an adjustment made every four hours

Context-aware, Energy-aware Sensing of Physiological Signals

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

Context-aware, Energy-aware Sensing of Physiological Signals

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

Design and Deployment of Services in Tiered Sensor Networks

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