Some people think of terminator when talking about AI or other dystopia scenarios. Others think that AI is overrated, but what is it?
Some people think of terminator when talking about AI or other dystopia scenarios. Others think that AI is overrated, but what is it?
If you live in the UK, you’ve travelled at least once with N. E. Well… If you haven’t, N.E. is a British multinational public transport company. I used to take their coaches to travel across the country. I still do. They connect all the places together by having frequent journeys to hundreds of destinations within at least the UK. So, what is this post about? Well, as I said, I am using their services mainly because of their prices. It is usually much cheaper than taking the train. However, there is one frustrating thing. Delays!! Waiting for the coaches seems never-ending. Other times you expect to go to your destination within a couple of hours and it takes four or more. It happened to me. I know, traffic. We can’t do anything about it. But, yes we can. We can at least know the schedule. We can know that the coach will actually take four hours to go to its destination and thus be prepared of the long journey.
The timetable is actually given along with the expected time to the destination…and, to my experience, it is usually wrong! So, here, I propose a simple solution that could be beneficial for both customers but also for the company.
Machine learning is a fast evolving branch lying between computer science and statistics and it could come handy. We can train intelligent algorithms to find patterns in the schedule of coaches. Specifically, we can learn their departing and arriving times and provide better estimates about each journey’s duration. So, we can know in advance that the trip is going to take more than expected or that is going to be departing late!
To the practical bit now. I believe that Gaussian Processes are ideal for this task. A periodic kernel could be used since we already know that duration depends on the day and the time of the day. Departure and arrival times can be noted down by the drivers and added to the system. Thus, a history of journeys’ times and durations can be created. Next, for any journey requested, an accurate estimation of the duration and departure time can be provided as well as the risk or the confidence interval or the uncertainty about that prediction.
NoiseTube is a project that tackles the noise pollution problem in many large cities in Europe. In particular, the deployment is focused on Brussels, Paris and London. It proposes a participative approach of monitoring noise pollution by involving the general public. Part of this project is the use of the NoiseTube app, a smartphone application which turns smartphones into noise sensors, enabling citizens to measure the sound exposure in their everyday environment. Each participant is able to share their geolocalized measurement data in an attempt to create a collective map of noise pollution, which will be available to NoiseTube community members. The main motivation for participation is the social interest. In other words, people contribute in order to understand their noise exposure, to build a collective map, to help local governments in tackling noise pollution by understanding noise statistics and to assist researchers by providing real data to analyse.
On the other hand, this project enables system designers to assess the potential of the participatory sensing approach in the context of environmental monitoring. In particular, developing a smartphone application, which is a widely adopted technology, can potentially reach thousands people that could cover large cities. Thus, provide a complete and accurate, in terms of noise exposure of individuals, noise pollution map to interested parties in order to take action.
The authors argue that although noise pollution is a major problem in cities around the world, current air pollution monitoring approaches fail to assess the actual exposure experienced by citizens. In particular, static sensors are located away from streets and emission sources in order to reflect the average pollution over an area. Consequently, it might underestimate the true exposure of people to air pollution. Thus, participatory sensing provides a low-cost solution for the citizens to measure their personal exposure and contribute to the community by taking measurements at the sources of the air pollution. This approach seems to work well, achieving the same accuracy as standard noise mapping techniques but at a significantly lower cost, as no expertise nor expensive sound level meter equipment is required.
GasMobile is a low-power and low-cost mobile sensing system for participatory air quality monitoring. Instead of relying on the expensive static measurement stations operated by official authorities for highly reliable and accurate measurements, GasMobile relies on the participatory sensing paradigm. In particular, GasMobile is a system developed from the combination of a small-sized, low-cost ozone sensor and an off-the-shelf smartphone. This system, besides taking ozone measurements to calculate air quality, can also exploit nearby static measurement stations to improve calibration and consequently the system’s accuracy. This system was used in a two-month campaign in an urban area. Specifically, the system was attached to a single bicycle and took measurements from several rides all around the city. The sampling interval was pre-set to five seconds, collecting a total of 2815 spatially distributed data points. Data collected were aggregated based on the area excerpt selected by a user interested in the results. To produce the map they divided this area into rectangular regions of 35×35 pixels and took the average ozone concentration of the observations in that region. Then, each region was classified into one of three categories: green, yellow, red depending on the average concentration value.
The system is currently at a prototyping stage but has great potential as it shows that air pollution monitoring can be achieved in a cost-effective manner. The results also show participatory sensing can produce results of high accuracy as the mean error for 2815 measurements was 2.74ppb which is only slightly higher than in static setting.
Another important participatory sensing application is Citisense, which its purpose is to monitor air pollution in large regions such as San Diego, California, US. Citisense consists of three components: A wearable pollution sensor, a mobile phone application and a web interface. Users carry the pollution sensor and the mobile phone with them throughout the day in order to learn their air pollution exposure. The web interface provides more detailed reflection on the air pollution exposure as well as air pollution maps built with historic user’s air pollution data. The sensor is connected via Bluetooth to the mobile phone and it is able to take measurements for five days in a single charge. The mobile phone app is responsible for collecting readings from the sensor and presenting them to the user. Each reading is time-stamped and geo-tagged by utilizing mobile phone’s GPS and network-based localization services. Citisense was conducted in the field for one month, involving 16 participants. The results show that the users exposure differ from the average measurements displayed by static sensors scattered in cities. In particular, participatory sensing approach is able to identify pollution hot spots in the micro-environment that have been developed due to busy roads, buildings and natural topology. Also, Citisense made an impact on the awareness of people. Specifically, participants understood better the properties of air pollution and in particular, they realized that being near busy streets or buses, air pollution spikes. However, as the authors admit, power management is an important challenge.
ExposureSense is a participatory sensing project that attempts to monitor air pollution in cities. It exploits the increasingly number of sensors that smartphones tend to have to convert them in to powerful mobile sensor devices. ExposureSense has a different approach than other participatory sensing applications for air pollution. It attempts to correlate humans’ daily activities and air quality monitoring in order to estimate user’s daily pollution exposure. To do so, smartphone’s accelerometer is used to infer the activities of users and external mobile sensor is used for air quality monitoring. In particular, machine learning techniques are applied on accelerometer data to infer users’ daily activities. In order to gather data from mobile devices they connect smartphones to air quality sensors via a USB cable. Data are also collected from external sensor networks which are combined with data collected from the users and interpolation is performed. Data is spatio-temporally correlated in order to estimate people’s daily pollutant exposure. Exposure intensity is scaled based on activity type, burned calories and movement speed.
HazeWatch is another low-cost participatory sensing system for urban air pollution monitoring in Sydney. HazeWatch uses several low-cost sensor units attached to vehicles to measure air pollution concentrations, and users’ mobile phones to tag and upload data in real-time. This project identifies the disadvantages of current approaches, i.e., using static sensors to monitor air pollution in cities. In particular, typically, there are only a few statics sensors scattered in cities and air pollution is inferred with the use of mathematical models which require complex input, such as land topography, meteorological variables and chemical compositions. This leads in to potentially inaccurate inferences as well as underestimation of the true exposure of the public to air pollution. HazeWatch aims to crowdsource fine-grained spatial measurements of air pollution in Sydney and to engage users in managing their pollution exposure via personalized tools. Specifically, HazeWatch among others, suggest low pollution routes to users.
P-sense is a work in progress that utilizes the concept of participatory sensing to monitor air pollution. The ultimate goal of this project is to allow government officials, international organizations, communities, and individuals to access the pollution data to address their particular problems and needs. P-sense enables air pollution measurements in a finer granularity rather than what is currently achieved by having static sensors in cities. It also enables users to assess their exposure to pollution according to the places visited during their daily activities. P-sense is easily extensible to allow the integration of existing data acquisition systems that could enrich the air quality dataset. P-sense consists of four main components: the sensing devices, the first-level integrator, the data transport network, and the servers. The environmental data are collected by a number of sensors such as gas, temperature, humidity, carbon monoxide, carbon dioxide and air quality sensors integrated to mobile phones via Bluetooth. All environmental data acquired from those sensors are transmitted to first-level integrator device, i.e., mobile phones. The phone is able for real-time analysis of data, providing visual feedback to users. The first level integrators transmit environmental data over the Internet (data transport network) to a dedicated server where they are stored and processed. Users are able to connect to the server and get visual feedback for the data. However, there are several important research challenges to address before this system is deployed in the real-world. These are related to data validity, incentives, visualization, privacy and security. Moreover, as in other applications, the Bluetooth connection drains mobile phone’s battery.
CommonSense is a participatory sensing project that aims to design a mobile air quality monitoring system. They conducted interviews with citizens, scientists and regulators in order to derive the principles and the framework for data collection and citizen participation. Unlike other applications, they break analysis into discrete mini-applications designed to scaffold and facilitate novice contributions. This approach allows the community members impacted by poor air quality to engage in the process of locating pollution sources and exploring local variations in air quality. Based on the fieldwork, a set of personas was developed to characterize relevant stakeholders. Specifically, `Activists’ are responsible for orchestrating actions and publicizing environmental issues. `Browsers’ are interested in environmental quality but not directly involved in sensing. `Data collectors’ are novice community members which are likely to be affected by air pollution. Also, the main principles extracted from the interviews with people are: Goal-oriented, i.e., what is the personal exposure of individuals and what are the hot spots in the city? Local and relevant, i.e., participants are interested mostly about their neighborhood and areas that frequently visit. Elicit latent explanations and expectation as well as prompt realizations are about taking into consideration the local knowledge of people and expertise, such as beliefs about the sources of air pollution in their area. Language barriers, i.e, users could be benefited by be introducing them to the scientific language where possible. This analysis lead to the development of a framework that is divided to six phases: Collect, Annotate, Question/Observe, Predict/Infer, Validate, Synthesize. Collect is the phase where the actual sensing takes place. Annotating is the step after collecting data where data collectors provide addition insights that contextualize and supplement it. Question/Observe is the step where data collectors begin to ask basic questions such as what is the personal exposure of each one or whether air quality is bad in their home based on their own and other collectors data. Infer/Predict builds on these questions and predictions are made for the unobserved locations. Validation is the stage where data collectors’ data are compared against with data from organizers and activists and check whether there is enough coverage of the interested area. Finally, Synthesize is the highest level where data are integrated and documentation, reports and other deliverables are produced.
Besides relying on citizens to take measurements, CommonSense attempts to monitor air pollution by other means. In particular, in one study they run trials with air quality monitoring devices attached to the rooftops of street cleaners in the city of San Francisco. These devices are associated with mobile phones that send data into CommonSense servers. This way, a systematic coverage of a large city can be achieved as well as test, refine and calibrate the system for future deployments.
OpenSense is a project that aims to monitor air pollution in large cities like Zurich, Switzerland. More than 25 million measurements were collected in over a year from sensors attached to the top of public transport vehicles. Based on these data, land-use regression models were built to create pollution maps with spatial resolution 100m X 100m. One of the challenges that this approach aims to tackle is the lack of fine-grained spatio-temporal air quality data. Static sensors are expensive to acquire and to maintain and thus only a few are placed in every city. The proposed system consists of 10 nodes installed on top of public transport vehicles that cover a large urban area on a regular schedule. The collected data are processed and predictions about the unobserved locations are made using the regression models. Although this is a good approach for providing fine grained spatio-temporal information about air pollution, nothing is said about the battery consumption of the sensors that are used to send the measurements in real-time over GSM and use GPS satellites to get their location. Also, measurements are only taken in roads where there are buses routes and since sensors are placed on top of them they endure vibrations, heat, humidity and long operating times which might lead into inaccurate measurements.
The Next Big One
The Next Big One is a participatory sensing application for the early detection of earthquakes. These events are difficult to model and characterize a priori. Thus, it utilizes the accelerometer sensors available on smartphones in a way to detect rare events and earthquakes. The focus of this project is to harness the power of the crowd, i.e., the wide availability of accelerometer sensors, for early earthquake detection. In shake table experiments, it is found that it is possible to distinguish seismic motion from accelerations due to normal daily use. However, for this application to be robust thousands of phones must be utilized. It is estimated that a million phones would produce 30 Terabytes of accelerometer data per day.
TrafficSense is a participatory sensing application for the monitoring of road and traffic conditions. In particular, this application relies on people carrying their smartphones with them while traveling and utilizing their sensors like accelerometer, microphone, GSM radio, and/or GPS sensors to detect potholes, bumps braking and honking. The effectiveness of the sensing functions were tested in the roads of Bangalore and it is shown that is it possible to monitor the roads using a variety of sensors built in the smartphones that users carry with them. In particular, the accelerometer was used for braking detection and to distinguish pedestrians from users stuck in traffic. Also, it is used to detect spikes that would suggest bumps in the roads. Audio was recorded using phones’ microphone in order to detect noisy and chaotic traffic conditions. Finally, GPS and GSM cell triangulation are used to localize users’ positions.