1. (2) mobility (1) sensing ability meth-amphetamine lab
(N-methyl-1-phenylpropan-2-amine)

2. Crowd-Sensing (urban) Sensing Platform Social engineering
mobility
(urban) Sensing Platform
Key questions: System engineering
What data to collect? How to collect it? How often? How to aggregate? How to disseminate?
sensing ability
(participatory deployment)
Crowd-Sensing
Additional questions:
Social engineering
How to create participation incentives? Deal with freeriders? Deal with malicious users? How to manipulate participation? What’s an efficient interface?

3. Crowd-Sensing for On-street Smart Parking
(Shawn) Xiao Chen,
Elizeu Santos-Neto,
Matei Ripeanu
Electrical and Computer Engineering Department
University of British Columbia

4. Overview What How Why What is smart parking and its objectives?
What are the current solutions and their problems?
What is our proposed solution and its advantages?
How can the organizer guide the data collection?
How should participants respond to contribute data?
How should we deal with free riders?
Why should we prefer coordinated crowdsourcing?
Why can we simplify users’ manual operation?
Why we should not always exclude free riders?
In this study, we try to investigate whether and how crowdsourcing can be applied to realize smart parking. Although some applications have already attempted to provide the service of parking guidance by crowdsourced data, none of them have been proven effective yet. We start with the concept of smart parking and its objectives, the comparison of existing approaches and our proposed solution. Then we explain the different possible design options. Finally we introduce our findings by simulations.

5. Parking problem / Smart parking
Searching for free parking spots costs billions:
congested traffic (30%)
pollution,
wasted time and fuel
Smart Parking:
collect real-time data on parking availability,
guide drivers to find free spots efficiently.
The parking problem has puzzled most big cities for decades. According to studies, an average of 30% of the traffic congestion is caused by cars cruising for open parking slots.
Smart parking denotes the application of information and communication technology to collect and distribute information about parking availability in order to help drivers park efficiently.

6. Compared with ordinary drivers
Objectives
Cruising
Time
Walking
Distance
The primary objectives of smart parking is to reduce cruising time while maintain a low walking distance from the parking spot to the driver’s final destination.
The bottom line is that smart parkers shouldn’t have a longer walking distance compared with ordinary drivers.
Compared with ordinary drivers

7. Data collection: Infrastructure-based approaches
Infrastructure to detect status of parking slots (sensor or RSU)
Collect and distribute data
High initial investment and maintenance cost
Suitable for indoor garage or large parking lots
$20/month/spot
Example: SFParking
Deployed in San Francisco
The infrastructure-based approach is straightforward. A single entity installed sensors and wireless infrastructure around parking lots to detect and communicate the parking availability information. This approach can easily guarantee the data accuracy but is too costly for most cities to adopt.

8. Data-collection: CrowdSensing approaches
Collect relevant data from the public through their mobile phones
(almost) no initial investment,
but dependent on users’ manual input
Example: Google’s Open Spot
Applications like Google open spot try to realize smart parking by crowdsourcing. They ask users to contribute information about parking availability through mobile devices. This approach attracted great attention quickly because it eliminates the huge cost required by the infrastructure-based solution. Anyone with a cell phone is able to contribute to the system.

9. Problems with current approach
Difficult to use
apart from navigation, too much info to read/enter
Limited info
only from previous contributors, no info about occupied streets
Uncoordinated
race for the same spot, users not willing/guided to explore unknown areas
Although they have gained a lot of attention(according to the download statistics), they are not proven success yet(for the low app market rating). It is still not clear whether or not crowdsourcing is a feasible solution to provide parking guidance service.

10. System Components
We propose to collect data from drivers through their smartphones when they are driving. The mobile device can not only accept drivers’ manual input before or after their trips but also collect sensor data automatically. These data can be useful to infer information about parking availability.

11. Potential Advantages Easy to use Guided parking Higher adoption
Integrated with road navigation system
Guided parking
By coordinating drivers
Higher adoption
mutual assistance, resilient to free riders
The most natural way to realize smart parking is to make it a part of a road navigation system. Since crowdsourcing is proven to be an effective way to collect data for road navigation by applications like Waze, it would be easier to extend it to support smart parking. We try to do crowdsourcing in a way much similar to Waze.

12. Design alternatives What How Why
What is smart parking and its objectives?
What are the current solutions and their problems?
What is our proposed solution and its advantages?
How can the organizer guide the crowd-sourced data collection?
How should participants respond to contribute data?
How should we deal with free riders?
Why should we prefer coordinated crowdsourcing?
Why can we simplify users’ manual operation?
Why we should not always exclude free riders?
To design a feasible crowdsourcing system for smart parking, we need to make two important decisions. First, what should we do as the organizer when people give feedback. Second, what kind of data should be collected and how can these data be collected.

13. Uncoordinated vs. coordinated guidance
By coordinated crowdsourcing, we mean the organizer makes decisions for participants and these decisions will in turn affect the crowdsourced data in the future. In contrast, uncoordinated crowdsoursing just shares data among participants. The latter approach is used in google’s open spot.

14. Data acquisition Types of questions to ask smart parkers
Inference from sensed data #
Question
Answers
Capacity Q1
How many parking spots on street?
0,1,2,3…
As the answer Q2
Any parking spots on the street?
Yes/No
1(Yes)/0(No) Q3
No question / inference
No answer
Always 1
In crowdsourcing, data can be acquired in two ways: manual input or sensors. Manual input provides direct information but could make the app inconvenient to use. On the other side, sensor data can be used to infer important information but may add to the complexity of the system. #
Observed behavior
Inference
Capacity I1
Reach the assigned street and continue at low speed
The assigned street is occupied I2
Move at low speed after I1
The past street is occupied I3
Launch the application and drive away
New vacancy in the street +1

15. Simulation results What How Why
What is smart parking and its objectives?
What are the current solutions and their problems?
What is our proposed solution and its advantages?
How can the organizer steer the crowd-sourced data collection?
How should participants respond to contribute data?
How should we deal with free riders?
Why should we prefer coordinated crowdsourcing?
Why can we simplify users’ manual operation?
Why we should not always exclude free riders?
We compare different design alternatives through simulations and we find why applications like google open spot fail to provide effective support for smart parking. We also discovered some design principles for these kind of applications

16. Coordination is necessary
Uncoordinated
Coordination is necessary
When uncoordinated, smart parkers fail to find parking slots closer to their destination than ordinary drivers
Coordinated
When smart parkers are not coordinated as they participate in the crowdsourcing, their aggregate data is not sufficient to support the service. They are more likely to follow previous users’ suggestion and fail to discover better parking spots in unknown areas.

17. Coordinated smart parking works!
When coordinated, a majority of smart parkers don’t need to cruise for the parking slots.
Even those who need to cruise spend far less time than ordinary drivers.
In contrast, when smart parkers are coordinated, they will spend less cruising time and park closer to their destination than ordinary drivers. When more drivers become smart parkers, their advantage become more obvious.

18. Manual operation can be simplified
With high adoption the service is functional with only answering simple questions
When the percentage of smart parkers is low, inference by sensor data becomes useful
We also discover that when there are enough smart parkers, they can use simple manual input to maintain the quality of the service. It means that an increasing number of participants can make the application more user-friendly and more acceptable to the society.

19. Accept freeriders!
Here free riders refer to those smart parkers who use the service but don’t want to contribute. As our studies show, if there are small amount of smart parkers who can keep contributing to the system, the existence of free riders doesn’t deteriorate the service level seriously. On the contrary, accepting free riders may increase social benefits because it reduces fuel consumption and traffic congestion.
As the number of free-riders grows, the quality of service deteriorates only slowly.
When there are sufficient contributors, social benefits grow as more people freeride.

20. Summary Coordination is key to effective parking guidance
CrowdSensing: Simplified input is enough if there are enough participants
Accepting free riders increases social benefits (if there are some contributors)
In summary, we find that crowdsourcing is a prospective mechanism to realize smart parking but the organizer should take the effort to coordinate all participants during the process. We also find that a high degree of participation could make such applications easy to use because the manual input can be simplified. In addition, our study shows that a small amount of contributors can help a large amount of free riders in order to increase the social benefits.