A leading cause of mortality of hospitalized patients are hospital acquired infections (HAI). Unclean hands of healthcare personnel (HCP) are the most common factor contributing to HAI, but their strict compliance to hand hygiene protocols is difficult to supervise. In this work, we propose CleanHands: a simple, low-cost and scalable monitoring and alerting system to ensure adequate thoroughness of disinfection. CleanHands uses a combination of low-cost Bluetooth low energy (BLE) beacon tags and mobile phones for HCP tracking. It integrates infection control models and state-following algorithms for alarming in the event of noncompliance to hand hygiene. Our preliminary experiments in a mockup, small scale intensive care unit (ICU) facility shows promising results with less than 5% false positives.

The primal problem with the Internet of Things is the lack of interoperability at various levels, and more predominately at the device level. While there exists multitude of platforms from multiple manufacturers, the existing ecosystem still remains highly closed. In this work, we propose SNaaS or Sensor/Network as a Service: a service layer that enables the creation of the plug-n-play infrastructure, across platforms from multiple vendors, necessary for interoperability and successful deployment of large-scale systems. We present the design and implementation of SNaaS, along with preliminary microbenchmarks.

Clock synchronization is highly desirable in distributed systems, including many applications in the Internet of Things and Humans (IoTH). For IoTH, Bluetooth Low Energy (BLE) - a subset of the recent Bluetooth v4:0 stack - provides a low-power and loosely coupled mechanism for sensor data collection with ubiquitous units (e.g., smartphones and tablets) carried by humans. This fundamental design paradigm of BLE is enabled by a range of broadcast advertising modes. While its operational benefits are numerous, the lack of a common time reference in the broadcast mode of BLE has been a fundamental limitation. This work presents and describes CheepSync: a time synchronization service architecture for BLE advertisers. We implement CheepSync on custom designed nRF24Cheep beacon platforms (as broadcasters) and commercial off-the-shelf Android ported smartphones (as passive listeners); and show that the average (single hop) time synchronization accuracy is in the 10 μs range.

In this work, an algorithm is proposed for self-localization of a target node using power measurements from beacon nodes transmitting from known locations. The geographical area is overlaid with a virtual grid, and the problem is treated as one of testing overlapping subsets of grid cells for the presence of the target node. The proposed algorithm is validated both by Monte Carlo simulations as well as using experimental data collected from commercially-off-the-shelf bluetooth low energy (BLE) beacon nodes.

Despite their increasing sophistication, wireless sensor networks still do not exploit the most powerful of the human senses: vision. Indeed, vision provides humans with unmatched capabilities to distinguish objects and identify their importance. Our work seeks to provide sensor networks with similar capabilities by exploiting emerging, cheap, low-power and small form factor CMOS imaging technology. In fact, we can go beyond the stereo capabilities of human vision, and exploit the large scale of sensor networks to provide multiple, widely different perspectives of the physical phenomena.To this end, we have developed a small camera device called Cyclops that bridges the gap between the computationally constrained wireless sensor nodes such as Motes, and CMOS imagers which, while low power and inexpensive, are nevertheless designed to mate with resource-rich hosts. Cyclops enables development of new class of vision applications that span across wireless sensor network. We describe our hardware and software architecture, its temporal and power characteristics and present some representative applications.

Technological progress in integrated, low power CMOS imaging [1] and maturing low power wireless sensor network platforms, motivate a new and rich design space exploiting in network dense imaging. In particular, by combining comparably low power CMOS cameras and low power wireless sensor nodes, and implementing on board compression and image analysis techniques, we can greatly enhance the application of dense wireless sensor networks to phenomena that are most readily observed in the optical domain.