Center for Embedded Network Sensing

Participatory Sensing is a process whereby individuals and communities use mobile phones and web services to observe, analyze, and present personal and environmental artifacts, events and experiences. In this technical report we describe a community data campaign that made use of smartphone based participatory sensing for environmental needs assessment. Community organizers defined the content of the participatory sensing campaign. 68 individuals participated over the course of 6 weeks, uploading over 450 mini-surveys, including over 700 images.

This Technical Report provides a review of a new embedded computing platform enabling research, education and training, and product development based on the Intel Atom processor architecture. This introduces a dramatic advance in the capability for direct characterization of energy and power dissipation in embedded computing platforms and the associated capabilities for optimization of performance and energy. This report includes development, usage, and example operation and results with platform applications in mobile computing, distributed sensing, network routing, and wireless access point implementation. In each case, Atom LEAP is intended to provide both a reference design and a high throughput, easily implemented solution with an unprecedented advance in the capability for characterizing energy usage at a level of computing task and operating system detail substantially superior to prior methods.

Large and bright screens on today's mobile phones account for significant energy demand on phones' batteries. In this paper we propose an algorithm that, given the energy profile of the screen, finds the optimal schedule to minimize screen energy dissipation when the phone is idle. We profile the screen energy consumption of two popular smartphones, Nokia N95 and E71, through carefully designed micro-benchmarks. Our energy measurement results suggest that the default screen schedules on these phones are far from optimal - on average our algorithm performs 50% better than default. We also find that on the E71 not using the dim state of the screen and directly turning it off is more energy-efficient. We improve the performance of our screen scheduling algorithm by considering the history of each user's interaction with his/her phone. We study the interaction patterns of six volunteers with their smartphones. The results suggest that the distribution of the length of idle times for each user does not change over time. Therefore, the screen scheduler can learn this distribution during a learning phase and use it to improve screen management. We show that the screen energy consumption can be further reduced by up to 60% using this technique.

An important tool for evaluating the health of patients who suffer from mobility-affecting chronic diseases such as MS, Parkinson’s, and Muscular Dystrophy is assessment of how much they walk. Ambulation is a mobility monitoring system that uses Android and Nokia N95 mobile phones to automatically detect the user’s mobility mode. The user’s only required interaction with the phone is turning it on and keeping it with him/her throughout the day, with the intention that it could be used as his/her everyday mobile phone for voice, data, and other applications, while Ambulation runs in the background. The phone uploads the collected mobility and location information to a server and a secure, intuitive web-based visualization of the data is available to the user and any family, friends or caregivers whom they authorize, allowing them to identify trends in their mobility and measure progress over time and in response to varying treatments.

This work demonstrates the application of real-time adaptive management principles to the problem of controlling the salinity levels in, and/or protecting groundwater quality beneath, soils undergoing irrigation with relatively saline water (e.g., reclaimed wastewater) under arid/semi- arid conditions. Here, optimal feedback-control scheme known as Receding Horizon Control (RHC) previously applied offline to control soil moisture levels during irrigation (Park et al., 2009) is applied inline during a pilot-scale field test aimed at balancing reclaimed water reuse and soil/groundwater quality in real-time. RHC is supported by sensor measurements, physically-based state prediction models, and optimization algorithms to drive field conditions to a desired environmental state. A simulation model including a one-dimensional (vertical) form of the Richards equation coupled to energy and solute transport equations is employed as a state estimator to provide predicted soil moisture, temperature, and salinity data. Vertical multi-sensor arrays installed in the soil provide initial conditions and continuous feedback to the control scheme. An optimization algorithm determines the optimal irrigation rate and frequency based on the imposed salinity constraints while forced by the requirement to maximize water reuse. The small-scale field test demonstrated that the RHC scheme was capable of maintaining specified salt levels at a prescribed soil depth autonomously. This finding suggests that, given an adequately structured and trained simulation model, sensor networks, prediction models, and optimization algorithms can be incorporated in the context of RHC to achieve water reuse and agricultural objectives while minimizing negative impacts on environmental quality autonomously.

Many robotic path planning applications, such as search and rescue, involve uncertain environments with complex dynamics that can be only partially observed. When selecting the best subset of observation locations subject to constrained resources (such as limited time or battery capacity) it is an important problem to trade off exploration (gathering information about the environment) and exploitation (using the current knowledge about the environment most effectively) for efficiently observing these environments. Even the nonadaptive setting, where paths are planned before observations are made, is NP-hard, and has been subject to much research.

In this paper, we present a novel approach to adaptive informative path planning that addresses this exploration-exploitation tradeoff. Our approach is nonmyopic, i.e. it plans ahead for possible observations that can be made in the future. We quantify the benefit of exploration through the ``adaptivity gap'' between an adaptive and a nonadaptive algorithm in terms of the uncertainty in the environment. Exploiting the submodularity (a diminishing returns property) and locality properties of the objective function, we develop an algorithm that performs provably near-optimally in settings where the adaptivity gap is small. In case of large gap, we use an objective function that simultaneously optimizes paths for exploration and exploitation. We also provide an algorithm to extend any single robot algorithm for adaptive informative path planning to the multi-robot setting while approximately preserving the theoretical guarantee of the single robot algorithm. We extensively evaluate our approach on a \emph{search and rescue} domain and a scientific monitoring problem using a real robotic system.

The capacity to adaptively manage irrigation and associated contaminant transport is desirable from the perspectives of water conservation, groundwater quality protection, and other concerns. This paper introduces the application of a feedback-control strategy known as Receding Horizon Control (RHC) to the problem of irrigation management. The RHC method incorporates sensor measurements, predictive models, and optimization algorithms to maintain soil moisture at certain levels or prevent contaminant propagation beyond desirable thresholds. Theoretical test cases are first presented to examine the RHC scheme performance for the control of soil moisture and nitrate levels in a soil irrigation problem. Then, soil moisture control is successfully demonstrated for a center-pivot system in Palmdale, CA where reclaimed water is used for agricultural irrigation. Real-time soil moisture, temperature, and meteorological data are streamed wirelessly to a field computer to enable autonomous execution of the RHC algorithm. The RHC scheme is demonstrated to be a viable strategy for achieving water reuse and agricultural objectives while minimizing negative impacts on environmental quality.

Data faults in sensor networks must be marked to ensure accurate inferences. We introduce a two phase semi-realtime end-to-end Bayesian fault detection system for sensor networks. The first phase selects a subset of agreeing sensors from which a model of expected behavior is derived. The second phase uses this subset to derive and tag questionable sensor data. To accurately model the data, we use a hierarchical Bayesian space-time (HBST) model, as compared to the linear autoregressive modeling used in previous works. Applying this system to simulated and real world data, results are excellent when the phenomenon is well modeled by the HBST model. It achieves high detection rates and almost zero false detection rates. Results also indicate that in cases of critically low spatial sampling density a more accurate model is required.

We present a new application of hierarchical Bayesian space-time (HBST) modeling: data fault detection in sensor networks primarily used in environmental monitoring situations. To show the effectiveness of HBST modeling, we develop a rudimentary tagging system to mark data that does not fit with given models. Using this, we compare HBST modeling against first order linear autoregressive (AR) modeling, which is a commonly used alternative due to its simplicity. We show that while HBST is more complex, it is much more accurate than linear AR modeling as evidenced in greatly reduced false detection rates while maintaining similar, if not better detection rates. HBST modeling reduces false detection rates 41.5% to 96.5% when paired with our simple fault detection method. We also see that HBST modeling is more robust to model mismatches and unmodeled dynamics than linear AR modeling.

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%.