Recent cens_techrep items

Participatory Sensing for Community Data Campaigns: A case study

Thu, 28 Jul 2011 00:00:00 +0000

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.

The Atom LEAP Platform For Energy-Efficient Embedded Computing

Wed, 26 May 2010 00:00:00 +0000

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.

Smart Screen Management on Mobile Phones

Wed, 24 Jun 2009 00:00:00 +0000

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...

Ambulation: a tool for monitoring mobility patterns over time using mobile phones

Thu, 28 May 2009 00:00:00 +0000

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.

A Receding Horizon Control Algorithm for Adaptive Management of Soil Moisture and Chemical Levels during Irrigation

Wed, 6 May 2009 00:00:00 +0000

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...

Real-Time Adaptive Management of Soil Salinity Using a Receding Horizon Control Algorithm: A Pilot-Scale Demonstration

Wed, 6 May 2009 00:00:00 +0000

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,...

Nonmyopic Adaptive Informative Path Planning for Multiple Robots

Thu, 9 Apr 2009 00:00:00 +0000

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...

Sensor Network Data Fault Detection using Hierarchical Bayesian Space-Time Modeling

Tue, 20 Jan 2009 00:00:00 +0000

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.

Sensor Network Data Fault Detection Using Bayesian Maximum a Posterior Sensor Selection and Hierarchical Bayesian Space-Time Models

Tue, 20 Jan 2009 00:00:00 +0000

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.

Accurate Energy Attribution and Accounting for Multi-core Systems

Tue, 13 Jan 2009 00:00:00 +0000

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...

Fixing Faults in Wireless Sensing Systems with Confidence

Thu, 25 Dec 2008 00:00:00 +0000

This paper presents Confidence, a tool for identifying and addressing faults in wireless sensing systems. Confidence pinpoints potential sensor and network faults in real time, allowing users to validate unexpected data and address any failures in the field. By introducing a well defined, low-dimension feature space, and functions to map sensor data into this space, we are able to achieve fault detection and diagnosis with relatively simple mechanisms such as outlier detection. Users can directly modify system outcomes by altering a classification label in instances when Confidence's automated algorithm draws the wrong inference. This label is applied to all similar points in the feature space, enabling Confidence to learn from user interaction in the field. This abstraction for incorporating user knowledge provides a lightweight and easy- to-understand interface for the user, while limiting user bur- den and reducing the required a priori environmental knowledge. Confidence...

Optimal Spectrum Management in Multiuser Interference Channels

Fri, 21 Nov 2008 00:00:00 +0000

In this paper, we investigate the optimal spectrum management problem in multiuser frequency selective interference channels. First, a simple pairwise condition for FDMA to be optimal is discovered: for any two among all the users, as long as the normalized cross couplings between them two are both larger than or equal to 1/2, orthogonalization between these two users is optimal for every existing user. Therefore, this single condition applies to achieving all Pareto optimal points of the rate region. Furthermore, not only is this condition sufficient, but in symmetric channels, it is also necessary for FDMA to be always optimal. When the normalized cross couplings are less than 1/2, the optimal spectrum management strategy can be a mixture of frequency sharing and FDMA, depending on users’ power constraints. We first explicitly solve the sum-rate maximization problem in two user symmetric flat channels by solving a closed form equation, providing the optimal spectrum management...

Achieving Participatory Privacy Regulation: Guidelines for CENS Urban Sensing

Wed, 25 Jun 2008 00:00:00 +0000

Achieving Participatory Privacy Regulation: Guidelines for CENS Urban Sensing

Participatory Design of Sensing Networks: Strengths and Challenges

Mon, 16 Jun 2008 00:00:00 +0000

Participatory design (PD) involves users in all phases of design to build systems that fit user needs while simultaneously helping users understand complex systems. We argue that traditional PD techniques can benefit participatory sensing: community-based participatory research (CBPR) projects in which complex technologies, such as sensing networks using mobile phones, are the research instruments. Based on our pilot work on CycleSense, a community-based data gathering system for bicycle commuters, we discuss the benefits and challenges of PD in participatory sensing settings, and outline a method to integrate PD into the research process.

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

Fri, 16 Nov 2007 00:00:00 +0000

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...

Heartbeat of a Nest: Using Imagers as Biological Sensors

Wed, 7 Nov 2007 00:00:00 +0000

We present a scalable end-to-end system for vision-based monitoring of a biological phenomenon. Our system enables automated analysis of thousands of images, where manual processing would be infeasible. We automate the analysis of raw imaging data using statistics that are tailored to the task of interest, the study of avian behavior during nesting cycles. The system uses simple image statistics (features) as the low-level representation to be fed to generic classifiers and final inferences exploit the temporal and spatial consistencies. Our testbed achieves bird detection accuracy of 82%, and egg counting accuracy of 84%, allowing inference of avian nesting stage with accuracy within a day. Our results demonstrate the challenges and potential of using imagers as biological sensors. An exploration of system performance under varying image resolution and frame rate suggest that an in situ adaptive vision system is technically feasible.

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