CENS Technical Seminar Series

Powering Personal Choice for Global Impact through PEIR

powerpoint slides (PDF)

Invited Speaker: Eric Howard and Vinayak Naik
Date: July 18, 2008
Time: 1:00 PM - 2:00 PM
Venue: Boelter Hall 4760

Abstract

Our daily patterns and behaviors shape our relationship with the surrounding environment. This relationship is reciprocal with our actions having an impact on the health of our neighbors, communities, and the rest of the natural world and our exposure to anthropogenic pollutants. But we are still lacking tools that will provide us with data to measure our impact and exposure, making this largely unseen and misunderstood relationship visible. We present PEIR, the Personal Environmental Impact Report, which is a new kind of online tool that allows you to use your mobile phone to explore and share how you impact the environment and how the environment impacts you. Taking a step beyond a "footprint calculator" that relies strictly on your demographic profile, PEIR uses location data that is regularly and securely uploaded from your mobile phone to create a dynamic and personalized report about your environmental impacts and exposures. It provides users with information about their (a) carbon impact in kilograms when they travel, (b) impact of particulate matter on sensitive sites such as hospitals and schools in kilograms when they travel, (c) duration of exposure to particulate matter, and (d) duration of exposure to fastfood restaurants. Users get a summary of their impact/exposure values per trip and also per day, week, month, and year. They can share and compare their values among their social network in Facebook.

PEIR is an extensively distributed system built on cellphone application, databases, GIS, and data visualizing utilities. In our talk, we will describe the system-architecture of PEIR. One of the objectives for PEIR is to present data to the users in a near-real time manner. However, the complex models to compute the impact and unreliable nature of underlying services, such as reporting of the weather data, make real-time presenting of data a challenge. We will give an overview of the statistical methods use to handle the afore-mentioned challenge.

Biography

Eric J. Howard completed the Masters of Urban and Regional Planning program at Virginia Tech in 2007 and holds a B.S. in Environmental Studies and a Certification in Geographic nformation Science from the University of West Florida. As a member of the research staff at the Center for Embedded Networked Sensing (CENS) at UCLA, Eric has worked on a variety of urban sensing projects with a primary focus on the Personal Environmental Impact Report (PEIR) project. Prior to his work at CENS, Eric was a U.S. Department of Housing and Urban Development Community Development Fellow at Virginia Tech working on project to promote community and economic growth in the southern Virginia. Also, Eric worked with the Shenandoah Valley Air Quality Initiative to model the risk of exposure to air pollution to explore environmental injustice claims using remote sensing techniques. His research interests include sustainable transportation, environmental justice, community renewable energy systems, urban form / settlement patterns, community-based planning, and the development of GIS and Remote Sensing applications for use in an Urban Planning context.

Vinayak Naik received his PhD in Computer Science and Engineering from Ohio State University in 2006 and B. Eng. from VJTI, India in 1999. Since 2006, he is a member of the research staff at Center for Embedded Networked Sensing (CENS) in UCLA. His research focuses on reliable systems for large-scale networks of embedded devices. He has worked on the ExScal project that deployed the largest number of wireless sensor nodes, MASE project that deployed the wireless sensor nodes over the largest distance, and Kansei testbed that houses the largest number of wireless sensor nodes. He has contributed to Emstar and Tenet, which are two of the major software distributions coming out of CENS for the wireless sensor networks. Currently, he is working on the urban sensing project called PEIR and PeruNet which is a deployment of seismic wireless sensor network in Peru.