Sensors and Live Maps

April 2012, Sytze de Bruin

Mobile mapping

Overview

Sparse sample of critical environmental variable

● No complete image

● Joint effort involving multiple observers/sensors

Explicitly support decision making

Objectives:

● Sample to obtain maximum information content

● Share data | information among observers

● Near real time mapping

Findings from cases

● Methodological development

Example: Mobile measuring devices: γ-dose

Where to optimally locate mobile

measuring devices given an

imminent emergency?

Simulated

disaster using

NPK-PUFF

atmospheric

dispersion

model

No hypothetical exercise

“the decision to evacuate people in a set

radius from the Fukushima Daiichi plant

is flawed”

http://maps.safecast.org/drive/4

Relevance

Mistakes are costly

Examples:

● Site selection (playground)

● Soil remediation

● Evacuate area (safe route)

● Precautionary measures (iodine)

Needed methods:

● Smart sampling

● Minimise the aggregated expected costs of making wrong

decisions (e.g. misclassification)

Synthetic case study, automated sensors

Threshold on 100 x 100 Gaussian Random Field

Initial sample: 16 sensors on regular grid

Measurement error considered

Scenarios:

● Add 1 obs./time; move sensor having lowest cost

● Add 2 obs./time; scan restricted neighbourhood (short-sighted ~ autonomous sensors)

● Add 2 obs./time; scan whole area, move sensors having lowest costs (~ centralized control)

Scenario 1: single sensor moving at a time P

robabili

ty m

ap

Scenario 2: two short-sighted sensors P

robabili

ty m

ap

Bigger problem: • 4 E(cost) maps

• 4 P(map)

• n(n-1) pot. solutions

Confine it?

Scenario 3: two sensors & heuristic optimiser P

robabili

ty m

ap

Cost plot: 2 moving sensors; 2 strategies

4500

5000

5500

6000

6500

7000

7500

8000

8500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Iteration

expected costs

real costs

expected costs

real costs

Aggre

gate

d m

iscla

ssific

ation c

osts

[-]

Exhaustive search (GA)

Restricted neighbourhood

Method: aggregated costs over whole map

Point carries information for neighbourhood (not just single location)

Integrate data from all sensors

1) Compute probability + en - signal at new sample site

2) Compute aggregated costs for both situations (geostatistical model)

3) Multiply [1] and [2] expected aggregated costs E(C)

4) Choose site having lowest E(C), highest Expected Value Of Information

Single moving sensor | small problem: exhaustive search

Otherwise: heuristic method, e.g. Genetic Algorithm

Note: EVOI is data dependent!

Photo: Jeroen Bosman

Simulated toxic plume over Wageningen campus

Student fieldwork on campus (Feb. 23)

Context: evacuate campus

Measurement = location, time

● using smartphone

Results

● sample from simulated plume

● map showing recent results of all

observers

Decide where to go next

Next day: present evacuation plan

100 most recent “measurements” at 15.00 Students almost immediately identified the source Find “decision boundary”

Spatio-temporal interpolation with hotspots

Near real time mapping

Optimally weigh data in space and time (model)

Deal with hot spots

Not solved yet

Some students complained

about lack of correspondence

with wind direction

Field experiment (learning map)

Mapping invasive species (real world)

Live updating of probability map

● Data of all students

● Remaining uncertainty

Monitoring EVOI

Other examples

Use EVOI:

● Translate geometric accuracy in economic loss precision

agriculture

● Soil remediation

● Starting project with IMAZON (NGO promoting sustainable

development in the Amazon) on carbon credits

Conclusions

Multiple sensors sampling critical environmental variable

Global data | information improves mobile mapping

● Automated: Expected Value Of Information (EVOI)

● Human: interpolated maps, EVOI, observations (raw data)

Autonomous decision making by individual sensors may fail

● Sensors get trapped in local optima

Need for fast solutions for optimising sample

We need fast interpolators for a variety of data types

● Spatio-temporal data are yet a challenge

● So are hot spots & other non-stationarity issues

Enabling technology