This fall, San Francisco will implement the largest mesh network for monitoring parking to date. Around 6,000 wireless sensors from the San Francisco company Streetline will be fixed alongside as many parking spots, monitoring both parking availability and the volume and speed of passing traffic. The city hopes that displaying information from the sensors on Web maps, smart phones, and signs on the street will reduce the traffic and pollution caused by circling cars.
A mesh network differs from a typical wireless network in that there’s no central transmitter: every node can transmit to every other node. Mesh networks have generally been used for environmental monitoring, or to grant wireless devices Internet access.
When sensor networks have been deployed roadside, it’s usually been to monitor traffic, not parking. In urban areas, traffic-monitoring systems have been used for congestion pricing: during business hours in downtown London, for instance, the license plates of cars are photographed, and the drivers are sent a bill. Some parking garages also have signs that tell drivers where the available spaces are, but such systems generally rely on manual car counting, not sensors.
In San Francisco, however, clusters of plastic-encased, networked sensors are embedded in the surface of the street. The main sensor in the cluster, which is commonly used to detect cars, is a magnetic one, says Jim Reich, the vice president of engineering at Streetline. Magnetic sensors detect when a large metal object locally disrupts Earth’s magnetic field. One challenge with magnetic sensors is avoiding false positives. “We rely on the magnetometer the most, but in order to fix errors, we use other types of sensors [that] give you much higher reliability,” says Reich. He won’t elaborate on the supporting sensors, but he says that the Streetline system has accuracy in the high nineties in recognizing parked cars.
To relay information, the Streetline sensors use Dust Networks’ SmartMesh system, a spinoff of the Smart Dust project at the University of California, Berkeley, funded by the U.S. Department of Defense. Dust Networks CEO Joy Weiss says that SmartMesh networks are more than 99.99 percent reliable. SmartMesh and Streetline’s technology combined gives the nodes an average lifespan of 5 to 10 years on only two AA batteries. “We were really the first ones able to build an entire network where every node in the network is able to run on batteries for years, and at the same time deliver very high reliability,” says Weiss. “In most [other networks], these are a trade-off.”
Dust Networks uses several techniques to combine efficiency and reliability. The first is redundant routing: if a signal doesn’t go through the first time, the sending node tries other nearby nodes, or tries the same node after a period of time. A technique called channel hopping circumvents interference by assuming that changing channels every few seconds is more efficient than trying to find a good or bad channel, says Weiss. To save power, she adds, the nodes go to sleep in between transmissions.
The sensors in Streetline’s monitoring system don’t have any wires, which makes installation cheaper and easier than tearing up roads to put down cables. “The vehicle sensors look like pavement reflectors, and cities can simply glue them down to the street and have a working system almost instantly,” says Reich. Every four to six blocks is a wired receiver–usually on a lamppost–that relays the sensor data to a central server, says Reich.
Another aspect of the network is that each additional node–such as the ones that the city plans to add to parking meters to allow for remote meter paying–improves the system. “Every new application essentially strengthens the network,” says Reich. “When you put sensors in parking meters, they improve the quality connection of those in the ground, so the system gets [better] quality.”
Streetline plans to offer a wide range of services using the same network, including sensors to measure air pollution and ambient noise levels and monitors for street lighting and water systems. “We intend to build an operating system for the city,” says Reich.
Yossi Sheffi, director of the MIT Center for Transportation and Logistics, is skeptical that the parking sensors will be helpful, however. “The aim of the system is to make driving downtown easier and less time consuming,” he says. “What we know from economics is, when we reduce the cost of any good, the use of that good goes up.” More convenient parking will entice more cars downtown, Sheffi suggests: “I’m not sure that they’ll get less pollution and less traffic. I think they’ll achieve the exact opposite.” He adds, “Cities should not make it easier to drive. They should make it easier to use alternative modes of transportation.” He suggests that congestion pricing, like London’s, might be better at reducing traffic.
Reich, on the other hand, suggests that the increased information will help people make better decisions, based on projections of “how much of their time is actually going to be wasted driving around.” He says that the system can suggest transit alternatives at the same time that it displays parking availability, and that it will eventually be able to predict whether parking spots will be available in a particular location by the time you get there. “Our goal with parking management is to help the city set the right prices and policies for parking based on actual demand, to smooth usage, and [to] improve overall efficiency,” says Reich.
It will soon be easy for self-driving cars to hide in plain sight. We shouldn’t let them.
If they ever hit our roads for real, other drivers need to know exactly what they are.
Maximize business value with data-driven strategies
Every organization is now collecting data, but few are truly data driven. Here are five ways data can transform your business.
Cryptocurrency fuels new business opportunities
As adoption of digital assets accelerates, companies are investing in innovative products and services.
Yann LeCun has a bold new vision for the future of AI
One of the godfathers of deep learning pulls together old ideas to sketch out a fresh path for AI, but raises as many questions as he answers.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.