trafficview: a scalable traffic monitoring system tamer nadeem, sasan dashtinezhad, chunyuan liao,...
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TrafficView: A Scalable Traffic Monitoring System
Tamer Nadeem, Sasan Dashtinezhad, Chunyuan Liao, Liviu Iftode*
Department of Computer Science
University of Maryland, College Park*Now with Rutgers University
2
TrafficView
• Enable drivers to see vehicles in front of their cars, farther than they can see, in real-time
• Use vehicle-to-vehicle ad hoc networks
3
How TrafficView Works
• Each vehicle has an embedded system– GPS receiver (location, speed, time)– Short-range wireless NIC– On-Board Diagnostics interface (optional)
Receive data fromremote vehicle
Non-validateddataset
Validate
Validateddataset
Local data
Display
Broadcast data
4
Need for Data Aggregation
• Ad hoc networks of vehicles are dynamic
• Data propagation must be simple
• Send all data in one packet (up to MTU)
• Use data aggregation to put as much
information as possible in one packet
5
How Far Can You See?
• Problem
– How to aggregate data to see vehicles as far as
possible with “acceptable” accuracy loss
• Natural Solution
– Aggregate data for vehicles that are close to
each other
– Perform more aggregation as distance
increases
6
Outline
• Motivation and Problem Definition
• Data Representation
• Aggregation Algorithms
• Evaluation
• Conclusions and Future Work
7
Data Representation
• Vehicles store records:
– Vehicle ID (ID), position (POS), speed (SPD),
broadcast time (BT)
• Broadcast time: the time at which the originating
vehicle sent out the record
• An aggregated record contains more
than one ID
8
Aggregated Records
• Having n records
• Calculate the aggregated record’s fields:
},,,}...{,,,{ 1111 nnnn BTSPDPOSIDBTSPDPOSID
}...min{ 1 na BTBTBT
nodecurrent the to vehicleof distance idi
n
i i
ia d
POSPOS
1
n
i i
ii d
SPDSPD
1POS and SPD are weighted averages.
9
Aggregation Algorithms
• Ratio-based
• Cost-based
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Ratio-based Aggregation
Current Vehicle
Aggregation ratio: inverse of the number of records that would be aggregated in one record
Portion value: amount of the remaining space in the broadcast message
3. In each region, each two consecutive records that are closer than the merge threshold, are merged
1. Calculate region boundaries
2. Calculate merge thresholds
11
Cost-based Algorithm• The Ratio-based algorithm selects the records to be
aggregated blindly!
• Assign a cost to merging two records, select
records corresponding to lowest cost
• Cost function:
– High cost to close vehicles
– Minimize error due to merging records
– Minimize number of cars in merged records
a
aa
d
sddsdd 2211 ||||cost
12
Information Aging• Problem
– Vehicles move and change speed– Records can be out-of-date– Received information might be invalid
• Solution– Delete record if no information about that vehicle
is received in a while– Compute expected delay for each record received– Store record only if
|actual delay – expected delay| < threshold
13
Evaluation
• Metrics
• Road Scenarios
• Simulation Results
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Metrics• Visibility
– Average distance ahead about which a vehicle has
information
• Accuracy
– Average position error introduced due to aggregation
• Knowledge Percentage
– Average percentage of vehicles in each region ahead
about which a vehicle has information
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• Evenly distributed entries and exits• Random constant speed during
time intervals• Changing lanes randomly
Traffic Model
0
5
10
15
20
25
30
35
40
45
50
0 1 2 3 4Average Number of Lane Changes
Perc
en
tag
e o
f C
ars
(%
)
0
5
10
15
20
25
30
20 25 30 35 40
Average Speed (m/s)
Per
cen
tag
e o
f C
ars
(%)
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Simulations• NS-2 simulations
– 802.11b with 11Mbps bandwidth
– transmission range of 250m
– MTU = 2312 bytes
• 15,000m road, 4 lanes
• 300s duration of simulation
• Algorithms:– Ratio-based, Cost-based, Non-aggregation, and Brute-force Cost-
based
• Selected parameters using preliminary simulations
17
Scenarios
Name # of nodes Avg. speed (m/s) Avg. gap (m)
Rush-hour 690 10 100
City 780 20 100
High-density highway 870 30 100
Low-density highway 548 40 175
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Effect of Road Parameterson Visibility (1)
Ratio-basedAggregation
Cost-basedAggregation
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Effect of Road Parameterson Visibility (2)
Non-aggregation Brute-forceCost-based
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Visibility (High-density Highway)
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Accuracy (High-density Highway)
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Knowledge Percentage(High-density Highway)
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What We Learned
• Intuitively, cost-based algorithm appeared
to be a better choice
• Cost-based algorithm is only marginally
better for relatively closer distances
• Ratio-based algorithm is better for farther
away distances and is more flexible
24
Conclusions
• TrafficeView provides drivers with real-
time view of vehicles in front of their cars
• Designed and evaluated two aggregation
algorithms using realistic road scenarios
• Ratio-based algorithm is a good algorithm
– Good visibility and small position error
25
Future Work
• Working on prototype implementation
• Linear programming model to
automatically calculate the aggregation
parameters
• Privacy and Trust
26
Thank You!
http://www.cs.umd.edu/~nadeem/projects/trafficview