The issue start with the different kinds of data. We got 1903 light data from sensor which was based on time, and got 107 GPS points which was based on distance.

So, the problem need be solved here is interpolate more GPS points for each light data. Here is the JAVA program.








With this program we got a new CSV files with 1903 data include light data and gps data. Here is the QGIS visualize the data. (The blue point is the original 107 GPS points we got, the red point between is the new point we interpolate.)




The Garbage Cans are Watching You

An advertising company, called Renew, is receiving backlash from inhabitants of London, for the sensors they have put in their “smart” trash bins. The bins, dubbed “smart” bins, measure the Wi-fi signals emitted by peoples cell phones and after multiple trash bins pick up the same wi-fi signal, the bins are able to determine the route that users took. Citizens of London have hit back and on August 12th officials demanded that all the bins be removed from the streets for personal privacy reasons.

Renew’s chief executive, Kaveh Memari, defends his product, explaining that the bins can track phone signals and recognize the same phones, but cannot determine who’s phone it is or any other personal message. The idea would be similar to web “cookies”. This is the tracking of files that follow internet users through the web, and with these trash cans using phone signals to identify users, Memari hopes that he can “cookie the street.”

Trash bins track peoples smart phones

Trash bins track peoples smart phones

This is something that could provide the opportunity to track people occupying public transport or track people at stops. This could provide the potential to know, for example, where a certain user enters a bus and where he gets off. This could potentially allow the buses to identify patterns between users to be able to identify more acceptable bus routes.

Monitoring People by detecting cell phone

The intent of this post is to give potential ways of tracking people on CTA buses. There is currently no way of tracking individuals, nor who gets off at what stations. With the hopes of providing a prototype for a system that will give the CTA a more comprehensive analysis of their riders, several factors will be necessary.

1. There needs to be an accurate way to detect when a person boards the bus as well as when they get off.

2. There needs to be a way to detect multiple people and keep track of them as they shift through the bus.

The wide spread use of cell phones would offer one way of tracking peoples movements. Cell phone detectors are common in prisons as cell phones have become one of the largest contraband items in prisons. These systems are relatively inexpensive and they are non intrusive, meaning they detect the radiation given off by phones and do not actually monitor phone usage. This second part is important because it allows us to track people without illegally monitoring their calls, texts, or data streaming. Here are links to the two most commonly used companies for cell phone detection.


This is geared more towards protecting a wifi network but it still is capable of detecting cell phone uses anywhere from 5-100 ft. If hacked and set with an aduino circuit board this would give the opportunity to monitor the radiation given off by cell phones with reference to location on the bus.

The second link is  to the far more common BVS systems cell phone detector


This system is as small as a deck of cards, which would be advantageous on a crowded bus. This detection is up to 75 ft which allows it to detect anywhere on a bus. If properly used this could allow for detection of peoples cell phones when they get on the bus and when they get off, essentially allowing to track multiple people and to better determine what are the busiest bus stops.

The suggested prototype for this will not give the information on what the best bus routes but it will give the potential prototype for collecting the information necessary to determine this in the future.

The last link is for a tiny homemade motion detector. This is an alternative to cell phone detection but it would be difficult to get a comprehensive understanding of the amount of people on the bus as it monitors motion and cannot determine individuals

Wi-Fi Motion Tracking Approaches X-Ray Vision Technology

New advances in motion tracking are using reflected wi-fi signals to track people, and the results are proving almost limitless. The idea of motion tracking has become very popular with the release of technologies such as the Xbox Kinect. The Xbox Kinect is a gesture recognition software that uses a photon-measuring method, known as “time of flight” sensing, to track movements of people. This technology is limited, because it requires the person being measured to be in the line of sight of the camera.

The “Wi-Vi” project at MIT and the “WiSee” project at the University of Washington have been using wi-fi to track motion of people and it has been shown to be able to track gestures in all line-of-sight, non-line-of-sight, and through-the-wall scenarios. This technology has been proposed to be able to track your movements in your household, being able to turn on lights, change the temperature, etc. with the wave of an arm. It has also been studied for its potential for security purpose. This technology could track occupants of a building before police or other security enter. See some of the potentials that the University of Washington thought of in the video below.

Collecting Data by sensors / The role of Infrared Cameras in Mold inspections

Mold Problem = Moisture Problem <= Environment Problem <Living condition>

Thermal Imaging has now been in place for a number of years within the building industry and has been used to find problems with building materials, such as: hidden water leaks, leaks within the HVAC system, general plumbing leaks and faulty electrical and mechanical systems. For example, thermal imaging has been successfully utilized to help determine whether or not there are any significant energy losses due the  incorrect amount of insulation or even missing insulation, thermal imaging has also been used successfully to help locate loose electrical connections or overheated breaker boxes by identifying “hot spots”.

While this equipment cannot readily detect mold, it does hold the “key” to finding mold by quickly and accurately identifying the conditions necessary for mold to be present. By identifying variances in surface temperature, thermal imaging has the ability to help us quickly find moisture incursions. The variances in the surface temperatures often mean that there has recently been a moisture incursion and that variance in the temperature of the moisture present behind the surface is affecting the surface temperature, which is easily detected by high quality infrared cameras. Thermal imaging cameras have quickly become the “must have tool” for IAQ Professionals as they can help to expedite the investigation process significantly, thereby providing investigators with opportunities to increase the number of investigation that can be completed on a daily basis.

This video tells us about the process of detecting by using the thermal camera


EPA Annual Air Quality Report Illinois 2011

Attached is the link to the 2011 Illinois Annual Air Quality Report from the Illinois EPA. In it, it quantifies the pollution from 2011 with respect to the major categories that are monitored. Several Sample bits of information are as follows


This is a map of where the air quality sensors are located within Cook County. One thing that is obvious is that there is not enough sensors within the actual City of Chicago. Most of these sensors are on the periphery of the city.



This is the breakdown of the number of sensors within all of Illinois. One problem is that there are only 1 CO2, 5 Particulate matter, and very few of the other important sensors throughout the state.


Here are the sampled days. The samples are taken every 3 days.

Copenhagen Urban Data Collection

Attached is the website for the Copenhagen Wheel. This program was a joint project between MIT and the city of Copenhagen. Attached to each wheel is sensors that detect CO2, NOx, traffic, and noise to give a geo spatial evaluation of all of these factors within Copenhagen. This information is accessible through your iPhone