Joshua Kiepert

A Portable Wireless Particulate Sensor System for Continuous Real-Time Environmental Monitoring

Airborne particulate matter has been shown to be associated with morbidity and mortality, and may interfere with certain sensitive experiments. Understanding the levels and movements of particulate matter in an enclosed space can lead to a reduction in the impact of this material on health and experimental results. A system of environmental sensors including particulate matter, selected gases, humidity, temperature, and pressure can be used to assist in tracking air movement, providing real-time mapping of potential contaminants as they move through a space. In this paper we present a system that is capable of sensing these environmental factors, collecting data from multiple dispersed nodes and presenting the aggregated information in real-time. The highly modular system is based on a flexible and scalable framework developed for use in aircraft cabin environments. Use of this framework enables the deployment of a custom suite of sensors with minimal development effort. Individual nodes communicate using a self-organizing mesh network and can be powered from a variety of sources, bringing a high level of flexibility in the arrangement and distribution of the sensor array. Sensor data is transmitted to a coordinator node, which then passes the time-correlated information to a server-hosted database through a choice of wired or wireless networks. Presentation software is used to either monitor the real-time data stream, or to extract records of interest from the database. A reference implementation has been created for the National Institutes of Health consisting of a custom optical particle counter and off-the-shelf sensors for CO2, CO, temperature, humidity, pressure, and acoustic noise. The total environmental sensing system provides continuous, real-time data in a readable format that can be used to analyze ambient air for events of interest.

Monitoring of the Aircraft Cabin Environment via a Wireless Sensor Network

Wireless sensor networks consist of physically distributed autonomous sensor nodes that cooperatively monitor physical or environmental conditions. The key benefit of wireless sensor networks is that they are capable of generating a more complete view of the sensed environment by acquiring larger quantities of correlated data than independent sensor monitors. This makes them ideally suited for applications where a complex environment with many interdependent factors must be monitored. The aircraft cabin is one such example of a highly dynamic environment which necessitates the use of an advanced sensing system. Thus, in order to gain a better understanding of the aircraft cabin environment, a wireless sensor network was designed and prototyped. The network is comprised of a variable number of nodes, and each node is capable of adapting to monitor a wide variety of environmental parameters. The system, as described in previous publications, has now entered the testing phase. The current configuration includes twelve nodes sensing temperature, humidity, carbon dioxide, and barometric pressure. This paper discusses the results from a series of tests conducted with the prototype hardware/software in a mockup of the 767 cabin environment. Tests involved the use of humidifiers, heaters, and carbon dioxide to simulate changes in the cabin environment.

A wireless sensor data fusion framework for contaminant detection

In recent years, much research has been done on wireless sensor networks and sensor data fusion, however there has been limited work regarding implementation of real systems that are capable of providing a highly connected sensor network for data logging and data fusion applications. This paper describes the design and implementation of a wireless, portable, and reconfigurable sensor network framework. This sensor node design has proven to be effective for monitoring environmental conditions of aircraft cabins and is well suited to environmental monitoring and detection of contaminants in large areas when utilizing sensor data fusion features.