The proliferation of sensors in consumer mobile phones has made them a powerful data collection platform. We have become more interested in collecting data to document and learn about our own lives, our relationships with the community, and the environment we live in. However, current research is mostly focused on analysis of quantitative data, and a lot of important information that can be extracted from qualitative data such as images and text annotations is being ignored. The focus of this dissertation was to research and build techniques based on narratology to help users analyze and make sense of the qualitative data they have collected and to communicate the information and knowledge they extract from the data though narratives. More specifically, our main contribution was in the creation and evaluation of two techniques that were fundamental to narration tools: clustering and conflict discovery. We used these techniques to build the Narreme toolset that enables user-assisted creation of compelling narratives with participatory sensing data. The toolset consists of three tools: (a) Narreme clustering tool which helps a user organize her thoughts and identify supporting qualitative data consisting of images and text for her narratives, (b) Narreme conflict discovery tool helps the user better understand where conflict could be introduced in her narratives, and, (c) Narreme video editing tool which is used to create a video slideshow narrative and adjust the order based on a narrative ten- sion curve. We evaluated the effectiveness of our techniques and toolset through user studies.

Advances in microsensor and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. The low per-node Cost will allow these wireless networks of sensors and actuators to be densely distributed. The nodes in these dense networks will coordinate to perform the distributed sensing and actuation tasks. Moreover, as described in this paper, the nodes can also coordinate to exploit the redundancy provided by high density so as to extend overall system lifetime. The large number of nodes deployed in these systems will preclude manual configuration, and the environmental dynamics will preclude design-time preconfiguration. Therefore, nodes will have to self-configure to establish a topology that provides communication under stringent energy constraints. ASCENT builds on the notion that, as density increases, only a subset of the nodes are necessary to establish a routing forwarding backbone. In ASCENT, each node assesses its connectivity and adapts its participation in the multihop network topology based on the measured operating region. This paper motivates and describes the ASCENT algorithm and presents analysis, simulation, and experimental measurements. We show that the system achieves linear increase in energy savings as a function of the density and the convergence time required in case of node failures while still providing adequate connectivity.

Existing adaptive sampling methods for mobile sensors are poorly suited to scenarios in which the sensor continuously samples as it moves. We discuss why this is the case and suggest some techniques that may help. Next we propose an entirely different approach that uses a colored planar triangulation model and how it can be analysed by using Bayesian inference and MCMC simulation, which we believe better utilizes the path sampling capabilities of some sensors.

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.

Personal mobile devices such as smartphones are powerful tools for people to collect data about their lives. Not only are smartphones capable and connected computing and sensing devices, but their portability makes them proxies for their users in terms of data as they become an increasing part of users’ routines. People do many tasks for communication and information consumption on their smartphones, and the phone can automatically track user context like location and motion. This enables them to help users to learn about themselves and their routines, and also provides their applications in domains like health and productivity with useful data to make services more relevant, adaptable, and intelligent.

Useful data from phones includes both immediate context information and higher level analyses about user patterns. This research includes investigations into both of these context types. In the first phase we developed techniques for detecting and classifying users’ current transport mode. Our solution, MyClassifier, addresses the difficulty of creating a classifier that performs accurately for a variety of users with different gait. We used an online, semi-supervised learning method to adapt a generic transport mode classifier to users’ own data, increasing the accuracy when the default classifier was initially inaccurate.

The second phase of the research explored the use of transport mode and other immediate context streams available on smartphones to learn higher-level aspects of personal context. Our solution, RoutineSense, predicts the consistency of individuals’ daily routines from the data that smartphones can passively collect. Routine regularity is an important metric both for its intrinsic value as it relates to psychological health factors, and as an input for other personalized applications. Researchers in the health sciences measure lifestyle consistency with the Social Rhythm Metric (SRM), a score based on the consistency of when certain routine daily events, such as dinner time, occur.

The events traditionally used to calculate SRM were designed to be significant recurring events in everyone’s routines. RoutineSense uses a novel method of identifying passively-detectable recurring events (called “landmark” events) which, along with their expected time offsets with events of interest, can be used to predict the times of these daily events. Unlike other methods tailored to detecting a specific event, this method can work on any recurring event of interest in a user’s routine. This general approach, while potentially less accurate than targeting specific EOIs with a separate method, allows it to scale to more events, and could support user-defined events in future applications. The events traditionally used to calculate the SRM were chosen in advance to be appropriate for self-report manual data entry scenarios. These specific measurements can be difficult to capture accurately without daily user input, even with our landmark event method. However, the landmark events mobile phones can detect in users routines from their smartphones’ passive context data streams provide an alternative measure of daily rhythms. Rather than recreate the SRM-5 by predicting the times of the traditional set of 5 events designed for human recall, RoutineSense chooses the most representative landmark event corresponding to each traditional event, and uses these landmarks directly as a surrogate set of events to create the “SRM-P” (passive SRM), allowing users to track their routine regularity without the continuous requirement of daily surveys.

The widespread use of smartphones and proliferation of mobile applications are reshaping many other areas ranging from social networking to health care Today's smartphones are much more capable than before, but mobile application are still restricted by limited resources on smartphones. The key hypothesis of this dissertation is that resource management on smartphones can be improved by adapting to usage patterns of users. We extensively studied users in the wild to characterize smartphone usage. We discovered significant diversity in smartphone usage. Along all aspects that we studied, users differ by one or more orders of magnitude. This finding suggests that resource management policies and algorithms on smartphones can become more effective if they learn and adapt to user behavior.

We developed the prototype of a system that adaptively manages battery, one of the most strained resources on smarthpones, and evaluated its performance. PowerLeash is a system that gives users control over their smartphones' battery lifetime when running background applications. With PowerLeash a user who is running power consuming background applications on her smartphone can decide how long her battery should last. PowerLeash continuously monitors the phone's battery level, the user's interactions with the phone, and progress of background applications. It builds a personalized model to estimate battery consumption based on usage and background applications progress. Using the on-line model and other information, PowerLeash dynamically adjusts the power consumption of background applications to meet the user's desired battery lifetime. We have designed PowerLeash to be easy to deploy, easy to use, and easy to incorporate in background applications. PowerLeash can run on any Android smartphone as a user level application, and relies only on information that is available to user-level processes. We present the design of PowerLeash and a detailed performance evaluation based on user studies. We use the lessons from deploying PowerLeash on volunteers smartphones to inform future iterations.

Without understanding the higher level science goal of a deployment, a networked sensing system appears to be nothing more than an ad hoc wireless network forwarding packets to a common sink. From this limited viewpoint all failures seem equally detrimental to the science application. However, all faults are not equal. Being able to prioritize faults based upon their impact to the science application is important because faults are common in sensing systems. Here, we present work on Vigilance, a system that incorporates a scientific model of the sensed phenomenon to enable a system administrator to quantify the impact of failures on the science application.

Scientific applications can be impacted by failures because when faults occur they result in unusable or missing data. Vigilance incorporates statistical techniques to fill in (i.e. impute) faulty and missing data, and quantify the resulting model output uncertainty. Since the imputation model is trained from historical data it has a limited lifetime. Vigilance also predicts when the imputation model will expire, and runs online to determine when the model actually does expire. Using real soil data collected from an ongoing deployment at James Reserve we present preliminary results on sensor data imputation, as well as the resulting impact on scientific model certainty.

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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.