How do we address the key challenges of IoMT? Where does computing take place? Where do we place the sensors? This presentation explores those issues. Presented at the 2017 D-STOP Symposium.
Data Analytics: Challenges and the Internet of Moving Things
1. Data Analytics: Challenges and the
Internet of Moving Things
Constantine Caramanis
The University of Texas at Austin
Electrical and Computer Engineering Department
1
2. Infrastructure-based sensing
Sensing includes radar, LIDAR,
cameras, and weather
Coordinate traffic through
intersections, support automated
driving
Collect data about collisions
and near-misses for planning
Effective with non-connected cars,
bicycles, and pedestrians
Sensing includes radar, LIDAR,
cameras, and weather
Coordinate traffic through
intersections, support automated
driving
Collect data about collisions
and near-misses for planning
Effective with non-connected cars,
bicycles, and pedestrians
3. Radar-aided millimeter wave communication
mmWave BS
supporting
V2X+radar
antennas
Radar beam
Millimeter wave is used for both radar sensing and high bandwidth
communication
communication beams
Radar can be used to configure
communication link more efficiently
4. Key Challenges: Noise + Noise + Noise
• Different Noise Characteristics in Data.
• Raw data – measurements from other cars, from
infrastructure, and from local sensors.
• Partial occlusions: objects may disappear/reappear
• Maps can be wrong: sparse but arbitrary corruption in
the data.
• Inconsistent measurements from different sensing
modalities
6. Key Challenges: Mixed Populations
• Higher level data abstractions
• Inferring behavior:
• Ex: deviations from trajectory
7. Mixtures and Non-Linearities in Large Scale Data Analysis
Linear, Logistic and Non-linear regression are fundamental for prediction and
planning
Examples: transit time vs. daily flows, flow vs. speed, responses to network
stressors or diversions or to future demand and flow patterns
Mixtures: Populations are mixed, and may require simultaneous clustering and
regression/classification, when clustering-as-data-preprocessing is impossible
Nonlinearities: Discover structure without expensive/intractable non-parametric
models
New algorithms for:
1. Solving the simultaneous clustering-regression
problem (tensor methods)
2. Structure recovery through unknown non-linear
transforms (second-moment methods)
Northfield
Windsor Park
RidgetopHyde Park/
Northfield
Delwood II
Hancock
North University
Cherrywood/WilshireWood / Delwood I
Mueller
Barbara Jordan Blvd
38 ½th St
Manor Rd
Other
Ramps used by neighborhood traffic, Source: Dr. J. Duthie
8. Dynamics, Transportation and Data Science
• The two themes above tightly interrelated
• Inference-of-dynamics becomes a sensing
modality
• And different sensing capabilities require/impose
different inference needs
9. Upshot
• Basic Statistical and Algorithmic Research,
Models and Computation still a fundamental
bottleneck
• Computing Infrastructure:
• Where does computing take place?
• Where are the sensors?
• Cost – Speed – Communication challenges and
tradeoffs
Notes de l'éditeur
Notes from Constantine Caramanis: Mixed regression is the problem where you see (y,x) and they are related via: y = z*x*beta_1 + (1-z)*x*beta_2 + noise
Instead of having all data (y,x) being related by one (noisy) linear relationship, y = x*beta + noise, each data point has one of 2 (or many) possible linear relationships. The interesting setting is when if you look at all the x's by themselves, they are not clusterable, and hence you cannot pre-process. Note that if they were clusterable, you would just cluster the data, and then solve individual regressions on each cluster, as usual.
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For the nonlinear part, think of logistic regression. It says: P(y = 1 | x) = f(x*beta), where f is the logit function.
Suppose that you do not know f. Must you learn it, if what you care about is learning beta? Our results say that you do not. if all you want is to learn beta (say, its support if you have a sparse problem) then no need to learn f.