CELLULAR AUTOMATA SIMULATION APPROACH IN … fileComments on the Workflow •From images to cellular...
Transcript of CELLULAR AUTOMATA SIMULATION APPROACH IN … fileComments on the Workflow •From images to cellular...
CELLULAR AUTOMATA SIMULATION APPROACH
IN ENVIRONMENTAL MONITORING APPLICATIONS
Tuyen Phong Truong, PhD
Can Tho University, Vietnam
Email: [email protected]
Anglet , June 27, 2019
ResCom Summer School Methods and models for network analysis
Contents
• Part 1: GPU Computing
UBO tools: QuickMap, PickCell, NetGen
Grid-cell data preparation
From cells to cellular automata
Graphics Processing Unit (GPU) execution
• Part 2: Cellular Automata and Terrain Complexity
Geography analysis in relation to physical phenomena
Cellular automata: principles, execution models and workflow
• Part 3: Physical Modeling
Flash flood: simulation and validation
Long-range radio coverage: computation and experimental validation
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PART 1
GPU Computing• Massively Parallel Processing with CUDA
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Cellular Systems: Workflow Organization
1. QuickMap
2. PickCell
3. Cell classifier 5. Process architecture
4. NetGen
• UBO toolset: QuickMap, PickCell and NetGen• Zone selection
• Cell segmentation
• Cell classification
• Binding cell together
• Generating data
• Adding behavior
• Executing on target
• architecture
Comments on the Workflow
•From images to cellular systems
Including external data such as
elevations, weather, sensing data
should be done before classification.
It can be done at (A) and/or (B)
Including external data at step (A) is
far better than (B)
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Segmentation
Classification
Cell Networks
Synchronous
simulation
(A)
(B)
GPU Computing with CUDA
• Simulation on GPUs Processes described by CUDA threads
Channels are interpreted by read/write
on buffers in shared memory.
A controller can access the status of
process and buffers
Inter placement control and presentation
• Behavior CUDA code expressing a program synchronous
• Objectives of simulation Network architectures: design and evaluation
Automatic synthesis of concurrent simulators
Distributed behavior libraries
Qualification of solution under required constraints: processing time, data
presentation, etc.
Metrics: data rate, latency, energy consumption
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Developing Simulations
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• Architecture (automatic):
One process per node
Channel representing node
communication capabilities
• Behavior files (reusable):
Distributed algorithms
Networking activities
Trace production
MIMD (Multiple Instruction, Multiple Data): Occam
SIMD (Singe Instruction, Multiple Data): CUDA
Compiler
Trace
• Cellular automata* A self-reproducing machine abstraction.
Be described, and specified as a discrete space which associates cells.
• Synchronous Systems Cellular space: an assembly of similar cells, regular or irregular.
Evolution of cell: Statet Statet+1
Neighborhood: physical dependencies.
Transition rule: behavior under influence of its neighborhood and local
sensed influences.
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Cellular Automata: Principles
* S. Wolfram, “Cellular automata: a model of complexity,” Nature 31, pp. 419–424, 1984.
From Cells to Cellular Automata
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• Cells embed the local state of
some physical system (1D, 2D
or 3 D), etc.
• The evolution is produced by
exchanging values with a
neighborhood.
• A connectivity represents
possible communications
between cells.
• The system progresses
synchronously, step by step,
by computing next cell state
from current state plus
neighbor communications.
Neighborhood
VN Neumann
Neighborhood
Moore
A cell with
(w x h) pixels
GPU Execution Diagram
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NVIDIA GPUs: up to
thousands of processors
on one graphic board.
Processing Flow: An Example
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Memory location
(a) A simple net with 3 nodes.
(b) Data management
(c) Data transaction between GPU and CPU.
• Variability in Large Systems and Asynchronism
Large systems: synchronism and massive data parallel execution
issues due to sparse data spaces.
Asynchronous cellular automata: being proposed to represent
reactive systems where events are propagated in a way similar to
Communicating Sequential Processes (CSP).
High Level Architecture (HLA): sequencing cellular sub-system at a
different speed and allowing data exchange.
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Cellular Automata: Implementations
Federation of two complementary cellular sub-system, a sensor network, and a display for the observer.
PART 2
Cellular Automata and Terrain Complexity
Sensing and Communication Principle• State of the art
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Communication between two sensing nodes via RF links
Wireless Sensor Network Fundamental
• Current metrics: Sensor coverages are discus representing perception or
perception accuracy.
Combining them defines perception surface.
Radio coverage are discus representing signal availability
with possibly accuracy.
Combining radio coverages defines possible network
connectivity.
15Reference:[1] Chuan Zhu, Chunlin Zheng, Lei Shu, and Guangjie Han. 2012. “A survey on coverage and connectivity issues in wireless sensor networks” J. Network Computing. Appl. 35, 2 (March 2012), 619-632.
[2] A. Tripathi, H. P. Gupta, T. Dutta, R. Mishra, K. K. Shukla and S. Jit, "Coverage and Connectivity in WSNs: A Survey, Research Issues and Challenges," in IEEE Access, vol. 6, pp. 26971-26992, 2018.
Wireless Communication Technologies
Technology Year Communication range
Bluetooth/IEEE 802.15.1 1994 <50 m
Zigbee/IEEE 802.15.4e 2003 <100 m
SixFox 2009 <40 Km
LoRa 2012 <20 Km
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• Prediction on long-distance point-to-point connections needs
deterministic computation to obtain more precise radio
coverage definition.
• Sensing accuracy prediction needs elaborated physical
simulations.
• The algorithms and CAD tools can fill both challenges.
Sensing and Long Range Communication Illustrations
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A Characterization of Terrain Complexity
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• Terrain complexity metrics: measuring ground
irregularity.
• Topographic Position Index (TPI): difference
in elevation value of a center cell and the mean
of adjacent neighbor cells.
Terrain
complexity
analysis
Extract sub-system
using TPI threshold
TPI terrain complexity for the river Soummam in Algeria (30 x 25 km)
Histogram of TPI analysis.
A subsystem of low terrain
(A)
(B)
(C)
Threshold
Designing for Long-distance Radio Coverage
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• Communication
coverage, from an
emitter (red point), is
shown in dark yellow.
• The irregular yellow
shape reflects the
challenges in
deployment of wireless
sensor networks,
especially on long
distances.
Radio coverage for an emitter (red point), located above the river
Soummam (36.622141028, 4.799995422), elev:165.0 m.
(Base map: OpenStreetMap)
Observing Physical Phenomena
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• A physical
simulation produced
positions with the
risk of flash flooding
(dark blue color).
• Selecting reachable
sensor positions
from a network sink
according to the
flooding result
simulation.
• The control can
connect on radio
coverage zones and
obtain measures
from critical
positions.
Communication coverage of a base station with a star network
PART 3
Physical Modeling• Flash Flooding Simulation on a Complex Terrain
Principle of Water Distribution in Cell System
• Rainfall modeling reveals several interactions
Water falling on the ground.
Water losses locally for reasons such as absorption or evaporation.
Water passes locally from cell to cell according to elevation differences.
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Physical exchange during a rain episode
Water Distribution Based on Cellular Approach: Transition Rule*
𝑟𝑒𝑚𝑎𝑖𝑛𝑖𝑛𝑔 = 𝑄𝑡 × 𝛽 (1)
𝑟𝑎𝑖𝑛 = 𝛼𝑡 (2)
𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 = 𝑖=1𝑛 𝑖𝑛𝐹𝑖 (3)
𝛿𝑖 = 𝑐𝑒𝑙𝑙𝑐. 𝑒𝑙𝑒𝑣𝑎𝑡𝑖𝑜𝑛 − 𝑐𝑒𝑙𝑙𝑖 . 𝑒𝑙𝑒𝑣𝑎𝑡𝑖𝑜𝑛 (4)
𝑒𝑙𝑒𝑣𝑎𝑡𝑖𝑜𝑛𝐴𝑏𝑜𝑣𝑒 = ∆ = 𝛿𝑖 , ∀𝛿𝑖 (5)
𝑠𝑒𝑛𝑡𝑖 = 𝑜𝑢𝑡𝐹𝑖 = 𝑄𝑡 × 𝛿𝑖/∆ , ∀𝛿𝑖 > 0 (6)
𝑸𝒕+𝟏 ≔ 𝒓𝒆𝒎𝒂𝒊𝒏𝒊𝒏𝒈 + 𝒓𝒂𝒊𝒏 + 𝒓𝒆𝒄𝒆𝒊𝒗𝒆𝒅 − 𝒊=𝟏𝒏 𝒔𝒆𝒏𝒕𝒊 (7)
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Pro
ce
ss
ing
se
qu
en
ce
A Study Case: Flash Flooding in Morlaix
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Heavy flooding in the center of Morlaix,
France due to a storm on 3 June 2018,
(photo: Le Telégramme). Flash flood simulation results for Morlaix The zone is composed of
58275 cells corresponding to actual area 3 × 3 km.
Rue de Brest near the river Queffleuth(long: -3.8329839706421, lat: 48.573767695437),
elev: 10.1 m.
.
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Flash Flooding Simulation in Morlaix: Analysis
A chart of rainfall values and water levels in Morlaix
during the heavy rain episode in June 3, 2018.
Flooding happens several hours after
the heavy rain.
There are 50 places where water
levels are from 30 to 70 cm above
ground level.
Performances of Flooding Simulations
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• The simulation was executed in 32 steps, equivalent to 32 hours.
PART 3
Physical Modeling• Radio Coverage Computation
Vertical Interpretation of Signal Routes
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• Allow to produce a power profile as a function of distance.
Power
A profile obtained along a route. Elevations (y) vs. Distances (x): 3 km real case
Horizontal Model and Directed Breadth-First Search
• Cell propagation is
guided according to
geometric positions
inside the routes.
• Objectives are to
reduce errors in
discrete segments
computation and avoid
useless forwarding.
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Part 2: Physical Modeling – Radio Coverage Computation
Segmented lines laid on a map to represent how cells forward incoming
signals from a root cell taking into account terrain complexity.
Terrain Complexity Analysis
• Figure shows the topographic complexity of a mountain area, the Arrée mountains in central Brittany (France), TPI metric.
• Remarkable landform splits in the North–South direction.
Critical references for network deployment.
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Parallel Algorithm for Cellular Long-range Coverage Computation
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1. Data Preparation• Geographic zone selection
• Cell segmentation
• Adding external data
• Process system production
according to neighborhood
2. Computation• Cellular automata model
• Locked steps
• Transition function
• Communication
• GPUs or multicore execution
Line-of-Sight (LoS): A central emitter passes a signal to neighbors that
propagate routes according to a directed spanning tree to cover the space in
concentric circles.
Radio signals propagate in concentric squares step by
step. Reachable cells are represented in colored stripes.
Directed Breadth-First Search Algorithm (1)
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Initializing local values
Preparing for the first
communication
session
Directed Breadth-First Search Algorithm (2)
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Initializing local values
Preparing for the first
communication
session
Sending data to
neighbors
Receiving data from
neighbors
Main loop
Directed Breadth-First Search Algorithm (3)
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Initializing local values
Preparing for the first
communication
session
Sending data to
neighbors
Receiving data from
neighbors
Updating local
values
Producing data for
next step
Radio Signal Propagation Models
• Difference propagation models allow
representing the median of the expected path loss.
Free space path loss
Single knife-edge diffraction
Okumura-Hata, etc.
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Topology of geography
seriously impacts on the
quality of radio links.
Radio propagation models describe
the qualifications and reliability of links
using radio frequency.
Experimental Measurement of Radio Coverage (1)
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• RECoco (Radio Estimation Coverage) board: Arduino Mega 2560 (micro-controller), inAir9b LoRa module, Venus GPS, and antenna (1 dBi).
• Hardware Development
Experimental Measurement of Radio Coverage (2)
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• Equipment Preparation
Mobile node
Base station
Experimental Measurement of Radio Coverage (3)
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Mobile node (receiver):RECoco + GPS mounted on a
car: receiving a “Hello”
message from an emitter,
sending its current position
back to the emitter.
Base station (emitter): Macbook (running QuickMap,
PickCell) to collect data (with
RECoco connected via an USB
port) and then show positions of
the mobile node.
• Experiment Arrangement
Simulator Validation Principle
• The base station
broadcast packets
periodically.
• The mobile node
answers received
packets with time,
Received Signal
Strength Indicator
(RSSI), and GPS
positions.
• Simulation estimation
is compared with real
behavior.
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The experiment at the Arrée mountains.
Analysis: RSSI vs Distance
• Received Signal Strength Indicator (RSSI) values decay as a negative exponential law of
distance.
• Obstacles seriously impact on the quality of radio waves.
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Performances of Computations (GPUs)
• Depending on cell size in pixel, zone size (up to 150K cells).
• Parallel algorithm with log(n) complexity.
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Execution time (NVIDIA GTX1070)
Validation of Simulations• Experimental measurements for validation of radio coverage prediction in different complexity terrains.
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Abert 1er
Plougastel
The Arrée mountains
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Thank you for your attention!
SAMES project
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MICAS project
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Lacuna project
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