Derr, Kurt | Search NASA

DOE OSTI2016

Data Acquisition in Wireless Router Link Testbed using GNU Radio Companion

An IEEE 802.11g WLAN wireless-router link test-bed is under development at the Idaho National Laboratory-Wireless Research Center. The project objective is to generate repeatable, high-quality, controlled-noise wireless transmission for software-defined radio (SDR) analysis and research. Signal acquisition for the data link will include both an Ettus X310 (with GNU Radio Companion) in parallel with a Tektronix RSA 306a spectrum analyzer. The integration and validation of the SDR is described. Preliminary results indicate the router test-bed comprised of readily-available WiFi and RF hardware will provide low-noise and repeatable signal captures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

Wireless Sensor Networks - Node Localization for Various Industry Problems

Fast, effective monitoring following airborne releases of toxic substances is critical to mitigate risks to threatened population areas. Wireless sensor nodes at fixed predetermined locations may monitor such airborne releases and provide early warnings to the public. A challenging algorithmic problem is determining the locations to place these sensor nodes while meeting several criteria: 1) provide complete coverage of the domain, and 2) create a topology with problem dependent node densities, while 3) minimizing the number of sensor nodes. This manuscript presents a novel approach to determining optimal sensor placement, Advancing Front mEsh generation with Constrained dElaunay Triangulation and Smoothing (AFECETS) that addresses these criteria. A unique aspect of AFECETS is the ability to determine wireless sensor node locations for areas of high interest (hospitals, schools, high population density areas) that require higher density of nodes for monitoring environmental conditions, a feature that is difficult to find in other research work. The AFECETS algorithm was tested on several arbitrary shaped domains. AFECETS simulation results show that the algorithm 1) provides significant reduction in the number of nodes, in some cases over 40%, compared to an advancing front mesh generation algorithm, 2) maintains and improves optimal spacing between nodes, and 3) produces simulation run times suitable for real-time applications.

42 ENGINEERING↗