Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an event-driven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded programming model with the efficiency of an event-based kernel. TOSThreads is backwards compatible with existing TinyOS code, supports an evolvable, thread-safe kernel API, and enables flexible application development through dynamic linking and loading. In TOS-Threads, TinyOS code runs at a higher priority than application threads and all kernel operations are invoked only via message passing, never directly, ensuring thread-safety while enabling maximal concurrency. The TOSThreads package is non-invasive; it does not require any large-scale changes to existing TinyOS code.

We present Koala, a reliable data retrieval system designed to operate at permille (.1%) duty cycles, essential for long term environmental monitoring networks. Koala achieves these low duty cycles by letting the network's nodes sleep most of the time and reviving them through an efficient wake-up strategy whenever the gateway performs a bulk data download. Unlike other systems which consume energy to maintain consistent network state (e.g. routes, sleep schedules, etc.) across the network's nodes, Koala maintains no persistent routing state on the motes. Instead, a basestation calculates the network paths using reachability information collected by the motes. The flexible control protocol (FCP), a protocol we developed, is then used to install this routing information on the network's nodes. This paradigm of operation not only eliminates the overhead of maintaining routing state, but also significantly reduces the complexity of the networking code running on the motes. Results from simulation and an actual implementation on TinyOS 2 indicate that Koala can achieve very low duty cycles under a wide range of download and network sizes.

Innovation in gateways for data gathering sensor networks has lagged compared to advances in mote-class devices, leaving us with a limited set of options for deploying such systems. In this paper we outline the system design of a gateway that meets the hardware and software demands of data gathering networks and present a prototype implementation of this design using the popular Stargate platform. The gateway communicates with the back-end servers through a periodic query-response protocol which is robust to connectivity outages. Commands from the back-end servers instruct the gateway to wake up the sensor network, perform bulk data downloads, and finally upload the collected measurements to a remote server. We investigate mechanisms to minimize energy consumption at the gateway including completely powering it off in between query intervals and batching data uploads. While we ground our design using the Stargate device and evaluate its power consumption using different long-haul radios, we present several platforms which could become viable alternatives in terms of their cost/performance ratio.

Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide high-quality care. Moreover, there is a growing concern about the ability of hospitals to provide effective care during disaster events. Tools that automate patient monitoring would greatly improve efficiency, quality of care, and the volume of patients treated. Towards this goal, we have developed MEDiSN, a wireless sensor network for monitoring patients' vital signs in hospitals and disaster events. MEDiSN consists of Patient Monitors which are custom-built, patient-worn motes that sample, compress and secure medical data, and Relay Points that form a static multi-hop wireless backbone for carrying patient data. Moreover, MEDiSN includes a back-end server that persistently stores medical data and presents them to multiple GUI clients. MEDiSN's heterogeneous architecture enables it to address the compound challenge of reliably delivering large volumes of data while meeting the application's QoS requirements.

Science is increasingly driven by data collected automatically from arrays of inexpensive sensors. The collected data volumes require a different approach from the scientists' current Excel spreadsheet storage and analysis model. Spreadsheets work well for small data sets; but scientists want high level summaries of their data for various statistical analyses without sacrificing the ability to drill down to every bit of the raw data. This demonstration describes our prototype data analysis system that is suitable for browsing and visualization - like a spreadsheet - but scalable to much larger data sets.