WiEye

Since the Spring of 2011, we have deployed an Android application called WiEye, which is currently in use by over 10k ongoing users and has collected over 250 million measurements. There are multiple stages of the project that will be ongoing that will leverage this WiEye database for crowdsourcing research: 1. Step 1: We must first finalize our understanding of how a mobile phone approach the fidelity of more advanced, costly instrumentation specifically designed to measure wireless behavior in terms of course metrics such as signal strength and spectral activity. 2. Step 2: From there, channel modeling to the degree that primary (e.g., line-of-sight) paths or secondary (e.g., multiple non-direct) paths are understood for diverse types of environments and/or geographical factors must then be studied. For example, we will characterize a mountainous region (versus the flat DFW area) as I will teach a course at SMU in Taos this summer called Mobile Phone Embedded Design (EE 5378/7378), where spectral activity, wireless propagation, and structural health of unique infrastructure will be measured in the Taos region. 3. Step 3: More generally, we then will investigate what role does geographical information (such as LIDAR data) has on the performance of wireless networks by isolating each effect (e.g., tree canopy height and width, land use, amount of free space, and building height and area). The detailed understanding obtained here will help tremendously for the spatial dimensions that are emerging as problematic for such applications as dynamic spectrum access. 4. Step 4: Finally, we will focus on our works impact on the standardization efforts (e.g., License Assisted Access), dynamic spectrum access, and broader-reaching applications for the crowdsourced based understanding of wireless environments including continue to pursue commercialization that is a result of NSF I-Corps as well as the multi- disciplinary objectives discussed above.