An Air Quality Sensor Web Made From iPhones
Considering the increasing ubiquitousness of the iPhone not everyone has one, but every area is bound to have a few it was really only a matter of time before someone hit on the idea of turning them into a sensor web. The primary problem however, being how do you convince thousands or tens of thousands of iPhone owners to let you use their phones for your web?
The answer: Have your sensor web monitor something that is of direct benefit to those owners. Namely, a continuous check on the surrounding air quality.
This was the task undertaken by a group of researchers at the University of California, San Diego. They constructed many dozens of sensors that could be interfaced easily with an iPhone, turning it from an intelligent phone into a mobile surveillance system. Each sensor is the size of the iPhone itself, and connects onto the back of the phone, essentially doubling its size and weight which keeps the entire apparatus well within the constraints for a pre-iPhone cellphone.
The aim is to radically increase the availability of sensors to monitor the spread of pollutants in the atmosphere, and relay those results in real-time to interested individuals.
Just 100 of the sensors deployed in a fairly large area could generate a wealth of datawell beyond what a small number of mandated air-quality monitoring stations can provide. For example, San Diego County has 3.1 million residents, 4,000 square milesand only about 10 stations.
We want to get more data and better data, which we can provide to the public, said William Griswold, a computer science professor at the Jacobs School of Engineering at UC San Diego and the lead investigator on the project. We are making the invisible visible.
The CitiSense sensors detect ozone, nitrogen dioxide and carbon monoxide, the most common pollutants emitted by cars and trucks. The user interface displays the sensors readings on a smart phone by using a colour-coded scale for air quality based on the EPAs air quality ratings, from green (good) to purple (hazardous).
Researchers provided the sensors for four weeks to a total of 30 users, including commuters at UC San Diego and faculty, students and staff members in the computer science department at the Jacobs School of Engineering. Computer scientists presented findings from these field tests at the Wireless Health 2012 conference in San Diego earlier this year.
The sensors turned out to be great educational tools for their users. Many people assume that pollution diffuses equally in the air. But thats not true. It actually remains concentrated in hot spots, along main roads, at intersections and so on. The sensors made this clear for users. Wendy Chapman, an associate professor at the UC San Diego School of Medicine, was one of them. She often bikes to work and discovered that pollution on her route varies widely. She was exposed to the most pollution when she used the bike path along State Route 56. But when she drove home on that same road, she had virtually no exposure.
The people who are doing the most to reduce emissions, by biking or taking the bus, were the people who experienced the highest levels of exposure to pollutants, said Griswold.
Users discovered that pollution varied not only based on location, but also on the time of the day. When Charles Elkan, a professor in the Department of Computer Science and Engineering, drove into work in mid-morning, the readings on his sensor were low. But when he drove back home in rush hour in the afternoon, readings were sometimes very high. Elkan said being part of the study allowed him to gauge how worried about pollution he should actually be. Air quality in San Diego is fairly good, he added.
Its a valuable study, Elkan said. I think its going to have a big impact in the future.
Elkan added that he could envision a day in the near future when the sensors used by CitiSense would be built into smart phones, allowing virtually everyone to keep tabs on the levels of pollution they encounter every day. Of course, that means people might start worrying more about pollution as something they can see and measure. No bad thing, certainly.
The system will always rely on volunteers who own a smart phone and are willing to carry one of the sensors attached to it. The app has to be running all the time of course, but the phone can still be used to send and receive calls as normal.
Some of the sensors are currently on loan to researchers at San Diego State University who are gauging air quality in San Ysidro, a community right on the border between the United States and Mexico, and one of the most polluted areas in San Diego County. Researchers hope to secure a grant from the National Institutes of Health to monitor air quality for school-age asthmatic children in that area and to determine what can be done to limit their exposure to pollutants.
The ultimate goal of CitiSense is to build and deploy a wireless network in which hundreds of small environmental sensors carried by the public rely on cell phones to shuttle information to central computers where it will be analysed, anonymized and delivered to individuals, public health agencies and the community at large. The sensors currently cost $1,000 per unit, but could easily be mass-produced at a rather lower cost, and result in a much smaller unit than the currently hand-constructed models.
The researchers believe they will find enough volunteers, as those most directly exposed to atmospheric pollutants cyclists, walkers, those with lung conditions tend to be eager to test the devices, and have kept them running constantly throughout the study. Many have even reported that it let them change their habits for a healthier commute.
For example, bicyclists found out that they could avoid a great deal of exposure by simply biking one block away from a busy street. Commuters who took the bus avoided waiting near the vehicles tail pipe, where the air quality was poor. One user convinced his supervisor to install new air filters in the office after registering poor air quality readings on his sensor.
The sensor network itself was quite a challenge. Aside from the hardware, the system that ties it all together is actually running a weak artificial intelligence to oversee the network and any problems that occur. In the end, it was constructed using Latent Variable Gaussian Regression, to cope with unacceptable variances in normal daily use.
Sensors will differ. Sensors will fail, Griswold explained. People will breathe on them. We wanted to make sure we got good data in these conditions.
Technical challenges remain. The data exchanges between smart phones and sensors use up a great deal of the phones batteries. During field tests, researchers provided users with two chargersone for home and one for workto ensure that their phones were not going to run out of power.
To extend battery life, researchers are experimenting with uploading data from the sensors to the phones every 15 minutes or only when the user wants to retrieve the information. A software toggle to deactivate the inbuilt GPS is also in the works, to minimise power consumption when the phone is immobile. If it is not moving, there is no need to query its location every few seconds. It can be switched back on when the phone's accelerometer standard in every iPhone detects significant movement again. In other words, jostling it around won't trigger a GPS update, but a brisk walk down the street will.
A further extension may come from implementing weak AI methods into the app software itself, to detect intelligently when to check the sensor, and when to check location, based off of experience of its individual user's habits. Getting to know the user's routine, would enable the device to minimise its power consumption by anticipating when it needs to check air quality and position, and when it does not. That again, is an avenue for further exploration, pending successful receipt of further grants.
Ultimately, we should end up with a mirror-world sensor web carried by most of the fitter, healthier commuters around the world, but whose results are available to everyone, not just the individuals who are using them. It has already radically increased our knowledge of the grouping patterns of surface-level pollution, and how to avoid it in our daily lives. Enough data may have been gathered already, to create an actual mirror world environment, to showcase the worst culprits for pollution, and the likely levels to be found even if it is just limited to the study area, for now.
CitiSense: Mobile Air Quality Sensing for Individuals and Communities (Paper, download link)
Quality Index (AQI) - A Guide to Air Quality and Your Health