Participatory sensing (PS) is a distributed data collection and analysis approach where individuals, acting alone or in groups, use their personal mobile devices to systematically explore interesting aspects of their lives and communities [Burke et al. 2006]. These mobile devices can be used to capture diverse spatiotemporal data through both intermittent self-report and continuous recording from on-board sensors and applications. Ohmage (http://ohmage.org) is a modular and extensible open-source, mobile to Web PS platform that records, stores, analyzes, and visualizes data from both prompted self-report and continuous data streams. These data streams are authorable and can dynamically be deployed in diverse settings. Feedback from hundreds of behavioral and technology researchers, focus group participants, and end users has been integrated into ohmage through an iterative participatory design process. Ohmage has been used as an enabling platform in more than 20 independent projects in many disciplines. We summarize the PS requirements, challenges and key design objectives learned through our design process, and ohmage system architecture to achieve those objectives. The flexibility, modularity, and extensibility of ohmage in supporting diverse deployment settings are presented through three distinct case studies in education, health, and clinical research.

Smartphones can capture diverse spatio-temporal data about an individual; including both intermittent self-report, and continuous passive data collection from onboard sensors and applications. The resulting personal data streams can support powerful inference about the user's state, behavior, well-being and environment. However making sense and acting on these multi-dimensional, heterogeneous data streams requires iterative and intensive exploration of the datasets, and development of customized analysis techniques that are appropriate for a particular health domain.

Soil contains vast ecosystems that play a key role in the Earth's water and nutrient cycles, but scientists cannot currently collect the high-resolution data required to fully understand them. In this paper, we present Suelo, an embedded networked sensing system designed for soil monitoring. An important challenge for Suelo is that many soil sensors are inherently fragile and often produce invalid or uncalibrated data. Therefore Suelo is an assisted sensing system: it actively requests the help of a human when necessary to validate, calibrate, repair, or replace sensors. This approach allows us to use available sensors without sacrificing data integrity, while minimizing the human resources required. We tested our system in multiple real soil monitoring deployments and demonstrate that, using human assistance, Suelo produced 91% fewer false negatives and false positives than common fault detection solutions on these datasets.

This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scientific deployments to develop a set of commonly used features useful in detecting and diagnosing sensor faults. We use this feature set to systematically define commonly observed faults, and provide examples of each of these faults from sensor data collected at recent deployments.

Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporating a mixture of elements from extremely constrained 8- or 16-bit “Motes” to less resource-constrained 32-bit embedded “Microservers.” Emstar is a software environment for developing and deploying complex applications on such heterogeneous networks. Emstar is designed to leverage the additional resources of Microservers by trading off some performance for system robustness in sensor network applications. It enables fault isolation, fault tolerance, system visiblity, in-field debugging, and resource sharing across multiple applications. In order to accomplish these objectives, Emstar is designed to run as a multiprocess system and consists of libraries that implement message-passing IPC primitives, services that support networking, sensing, and time synchronization, and tools that support simulation, emulation, and visualization of live systems, both real and simulated. We evaluate this work by discussing the Acoustic ENSBox, a platform for distributed acoustic sensing that we built using Emstar. We show that by leveraging existing Emstar services, we are able to significantly reduce development time while achieving a high degree of robustness. We also show that a sample application was developed much more quickly on this platform than it would have been otherwise.

Wireless sensor networks (WSNs) are a relatively new and rapidly developing technology; they have a wide range of applications including environmental monitoring, agriculture, and public health. Shared technology is a common usage model for technology adoption in developing countries. WSNs have great potential to be utilized as a shared resource due to their on-board processing and ad-hoc networking capabilities, however their deployment as a shared resource requires that the technical community first address several challenges. The main challenges include enabling sensor portability: (1) the frequent movement of sensors within and between deployments, and rapidly deployable systems; (2) systems that are quick and simple to deploy. We first discuss the feasibility of using sensor networks as a shared resource, and then describe our research in addressing the various technical challenges that arise in enabling such sensor portability and rapid deployment. We also outline our experiences in developing and deploying water quality monitoring wireless sensor networks in Bangladesh and California

Understanding how a sensor network system works requires running the system, extracting log files, and manually interpreting system metrics. When interpreting system metrics, we often try to correlate behavior over multiple modalities. For example, if a node is exhibiting strange behaviors, the cause may be due to weak battery, geographically bad placement, collision, interference, sensor failure, algorithmic faults, or a combination of the above. This approach of interpreting metrics is adequate for closed systems such as the ones run in simulations, with limited duration. However, for complex sensor network systems that have already been deployed for weeks or even months in the fields, this approach is difficult, laborious, and error-prone. Thus, a suite of tools to help analyze complex sensor network system is desirable. We have implemented Deployment Analysis System (DAS), a centralized data mining suite designed to better understand sensor networks. It supports visualization and deployment-related queries that allow the user to inspect historical system metrics, environmental data, geographical placements, and system status.

Being embedded in the physical world, sensor networks present a wide range of bugs and misbehavior qualitatively different from those in most distributed systems. Unfortunately, due to resource constraints, programmers must investigate these bugs with only limited visibility into the application. This paper presents the design and evaluation of Sympathy, a tool for detecting and debugging failures in sensor networks. Sympathy has selected metrics that enable efficient failure detection, and includes an algorithm that root-causes failures and localizes their sources in order to reduce overall failure notifications and point the user to a small number of probable causes. We describe Sympathy and evaluate its performance through fault injection and by debugging an active application, ESS, in simulation and deployment. We show that for a broad class of data gathering applications, it is possible to detect and diagnose failures by collecting and analyzing a minimal set of metrics at a centralized sink. We have found that there is a tradeoff between notification latency and detection accuracy; that additional metrics traffic does not always improve notification latency; and that Sympathy's process of failure localization reduces.

This work presents a preliminary design and evaluation of Sympathy, a debugging tool for pre-deployment sensor networks. Sympathy consists of mechanisms for collecting system performance metrics with minimal memory overhead; mechanisms for recognizing events based on these metrics; and a system for collecting events and their spatio-temporal context. Sympathy introduces the idea of correlating seemingly unrelated events, and providing context for these events, in order to track down bugs and find their root causes. Eventually, Sympathy will be part of a system that can aid in debugging sensor networks both pre- and post-deployment.

Recently deployed Wireless Sensor Network systems (WSNs) are increasingly following heterogeneous designs, incorporating a mixture of elements with widely varying capabilities. The development and deployment of WSNs rides heavily on the availability of simulation, emulation, visualization and analysis support. In this work, we develop tools specifically to support heterogeneous systems, as well as to support the measurement and visualization of operational systems that is critical to addressing the inevitable problems that crop up in deployment. Our system differs from related systems in three key ways: in its ability to simulate and emulate heterogeneous systems in their entirety, in its extensive support for integration and interoperability between motes and microservers, and in its unified set of tools that capture, view, and analyze real time debugging information from simulations, emulations, and deployments.