|Someday, smart cities could use crowdsourced data to alleviate parking woes. (Image source: Omer Rana on Unsplash)|
You're running late for an appointment. By some miracle, you manage to catch all the green lights on the way...and you still end up late because you spent 15 minutes driving in circles looking for parking! If you're lucky enough to find street parking in a major city, you're probably rolling the dice as to whether you'll get a parking ticket or not. Even the newer parking garages, which show you the number of available spaces, are prone to sending you on wild goose chases.
A group of researchers from Singapore-based Nanyang Technological University (NTU Singapore) think the solution for our parking woes lies in a mobile data-gathering technology called crowdsensing. But parking is just the first step toward using our mobile data to make our day-to-day lives more efficient.
In 2016, Jim Cherian—a senior research engineer at NTU Singapore—conducted a study around ParkGauge, a method of leveraging crowdsensing to capture mobile phone data to track the states of cars in parking garages. ParkGauge uses 3G, GPS, and Wi-Fi to collect data from a smart phone's sensors—including its gyroscope, accelerometer, and barometer—to determine driving states (i.e., turning or braking). Based on the driving state, and with enough data from other vehicles in the garage, ParkGauge's machine learning algorithm can infer which cars are parked, where they are parked and likely to park, and also where parking spaces are most likely to be available. This can all be delivered in real time—not only in a parking area, but also online so users can understand the parking situation before they even get there.
What Is Crowdsensing?
Most have probably heard of crowdsourcing, and may even have funded a few projects on sites like Kickstarter or Indiegogo. Crowdsensing is somewhat similar in that it seeks to leverage large groups of people. But what it wants is your data, not your money. “Crowdsensing refers to the process of collecting sensor data using smart devices from a crowd of contributing users. This is akin to a crowd of connected sensors on the move. In other words, it simply refers to the crowdsourcing of sensor data to enable and empower specialized applications and services,” NTU's Cherian told Design News. “Most of the crowdsensing efforts are done using commodity mobile devices (such as smartphones or smartwatches) and hence it also is known as 'Mobile Crowdsensing.'”
Cherian explained that crowdsensing typically comes in one of two forms. There's participatory crowdsensing, in which each user manually contributes data. Opportunistic crowdsensing, in contrast, refers to automatic, non-intrusive, “behind the scenes” data collection—usually with no or minimal user intervention.
”Participatory crowdsensing has been around for a decade or even longer, but it often fails due to insufficient user motivation and incentives,” Cherian said. Some may remember an app called Open Spot released by Google back in 2011. The app's heart was in the right place, but it only worked if users manually notified the app that they were leaving a parking space. It was the digital equivalent of flagging someone down to let them know you're leaving a space. But politeness only goes so far. Without the immediacy of seeing someone searching for a spot, there was no real incentive for users to interact with the app.
Opportunistic schemes have their own challenges. Because the data collected is generally not verified or contextualized by humans, it can create sets of bad or low-fidelity data and/or noise. The idea of having data collected in the background also sparks understandable privacy concerns from users.
The key challenges for crowdsensing, Cherian said, are to incentivize human users, discriminate trust levels and data quality, and ensure data privacy. If this is done, Cherian imagines that crowdsensing could be applied to a variety of applications beyond parking. “There are several problem areas where such methods could be applied,” he said. “This includes traffic congestion estimation, prediction of stopped duration at signalized intersections, and, in general, almost any problem that involves estimating a super-state that can be represented as the temporal evolution of sub-states in a hierarchical fashion.”
The data privacy hurdle could be a major one, however—particularly in today's post-Cambridge Analytica climate. There are major risks associated with any sort of data that deals with location and activity. Is it worth risking exposing your real-time location to strangers in exchange for a smoother commute?
“Overcoming these concerns while ensuring a good quality of service (i.e., differential privacy) is an active area of ongoing research,” Cherian said. “Typical ways of addressing this include data anonymization, data obfuscation, and stochastic (randomized) data sampling. But achieving practical, efficient, privacy-preserving crowdsensing schemes still remains a challenging subject.” The ParkGauge study explains that a major goal is to get the most out of as little data as possible in any particular application:
“...Unlike existing crowdsensing-based parking systems that directly count the available parking lots, ParkGauge does not require a “crowd” (hence high penetration of the application) for sensing individual parking garages. Instead, a minimal amount of sensing data acquired from a small number of users for each parking garage would be sufficient for ParkGauge to deliver useful information, whereas the “crowd” is needed only for covering many parking garages across a large urban area.”
Because of this, technologies like ParkGauge are more likely to roll out in places such as shopping malls before a wider, smart city deployment.
Connecting Crowds of Devices
Cherian and his colleagues were able to conduct their initial study with ParkGauge using only a low-energy 3G connection. Surely, a larger deployment will require better and faster connectivity? When thinking about city-wide deployments, crowdsensing as a whole looks like an emerging use case for 5G.