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Current Projects

HealthOS: A Platform For Pervasive Health Applications [HOWTOS]

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With innovation in wireless sensing and communication technologies, we are now able to continuously monitor people's everyday conditions. Our goal is to design and develop a pervasive health software architecture on which developers can add new sensing devices, combine multiple information streams from the devices, and develop an innovative health application (e.g., Web or mobile). In this article, we showcase how to use HealthOS and to develop HealthOS applications and adapters.

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Life Under Your Feet

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serc

LUYF is a joint project with Katalin Szlavecz from JHU Department of Earth and Planetary Sciences and Alex Szalay from JHU Department of Physics & Astronomy. The purpose of the project is to use WSNs to study the environmental conditions which affect soil respiration with fine spatial and temporal resolution.

Our deployments use the Koala data collection system to get data from the field to our visualization tools.

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Target Localization Using Radar Sensor Networks

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Target localization in WSNs (Wireless Sensor Networks) has been an active research area due to its importance and wide range of applications. Most of the proposals so far, however, have focused on cooperative targets that in one way or another work with the localization infrastructure. For example, mobile targets can emit RF or acoustic signals either on purpose (ActiveBadges) or by their nature (e.g., bird chirps). Instead, we focus on localizing non-cooperative targets using active sensors. Specifically, we use a network of mono-static low-power, pulsed, Dopper radars that independently estimate the target's Doppler velocity with respect to each radar. These measurements are then used to estimate the target's location and true velocity using an Extended Kalman filter. We have built a prototype of this system using TelosB motes and BumbleBee radars.

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DC Genome

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Servers and GenomotesThe Data Center (DC) Genome project is a collaborative effort with Microsoft Research. The project goal is to use data-driven and feedback control approaches to monitor, analyze, and improve the efficiency of data center operations and thus minimize their environmental impact.

 

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MEDiSN

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Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide olwghigh-quality care. Additionally, there is a growing concern about the ability of hospitals and EMS responders to provide effective care during disaster events. To automate the patient monitoring process and improve efficiency, quality of care, and the volume of patients treated, we have developed MEDiSN, a wireless sensor network for monitoring patients’ vital signs in hospitals and disaster events. MEDiSN has been deployed at the Emergency Department at the Johns Hopkins Hospital, at the University of Maryland's Trauma Center, and at the Washington Hospital Center. Recently, MEDiSN has been featured in the Discovery Channel Tech.


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Characteristics of Low-power Wireless Links and Radios

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cc2430

Understanding the characteristics of low-power wireless links and radios is an essential step towards building robust, efficient and reliable wireless sensor networks. In this project we study and evaluate the fidelity of the Received Signal Strength Indication (RSSI), which the low-power radios use to measure the power of the wireless signal. This value is heavily utilized in many wireless sensor network protocols and applications, such as localization, topology control, link scheduling, and link quality estimation. With extensive experiments, we show that inaccuracies in the RSSI values reported by widely used 802.15.4 radios, such as the CC2420 and the AT86RF230, have profound impact on these protocols and applications. Therefore, we also developed a calibration scheme to effectively minize the adverse effects associated with inaccurate RSSI values.

Stepping up to the link layer, we note that packet loss and energy consumption in sensor networks depend critically on the quality of the network's wireless links. Experimental results have shown that a low-power wireless link can be in one of three states or 'regions', as the inter-node distance increases: connected, transitional (gray), and disconnected. Moreover, the transitional region spans a sigficant portion and is likely to be even larger than the connected region. Therefore, in this project we explore the characteristics of the transitional region and study the possibility of picking reliable links within this region.

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Phoenix: An Epidemic Approach to Time Reconstruction

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phoenix-small

Harsh deployment environments and uncertain run-time conditions create numerous challenges for postmortem time reconstruction methods. Motes reboot and lose their clock state, considering that the majority of mote platforms lack a real-time clock. While existing time reconstruction methods for long-term data gathering networks rely on a persistent basestation for assigning global timestamps to measurements, the basestation may be unavailable due to hardware and software faults. We present Phoenix, a novel offline algorithm for reconstructing global timestamps that is robust to frequent mote reboots and does not require a persistent global time source.

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Time Synchronization Using Pervasive Time Signals

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rx-board-scaledConsidering its central importance to sensor networks, time synchronization has received extensive attention by the research community. Nevertheless, we note that existing approaches introduce undesirable trade-offs. For example, while GPS offers excellent accuracy for outdoor deployments, the high cost and power consumption of GPS receivers make them prohibitive to many applications. Message-passing protocols, such as FTSP, introduce different sets of compromises and constraints. In this project, we build an inexpensive and ultra-low power (< 100 uA) mote peripheral, we term the Universal Time Signal Receiver, that leverages the availability of time signals transmitted by dedicated radio stations around the globe to provide access to UTC time with millisecond-level accuracy. The proposed universal time signal receiver achieves global time synchronization and for applications where millisecond-level precision is sufficient, it consumes up to 1,000 times less energy than GPS or FTSP.

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