CELLULAR AUTOMATA SIMULATION APPROACH

IN ENVIRONMENTAL MONITORING APPLICATIONS

Tuyen Phong Truong, PhD

Can Tho University, Vietnam

Email: tptuyen@ctu.edu.vn

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

2

PART 1

GPU Computing• Massively Parallel Processing with CUDA

4

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)

5

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

6

Developing Simulations

7

• 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.

8

Cellular Automata: Principles

* S. Wolfram, “Cellular automata: a model of complexity,” Nature 31, pp. 419–424, 1984.

From Cells to Cellular Automata

9

• 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

10

NVIDIA GPUs: up to

thousands of processors

on one graphic board.

Processing Flow: An Example

11

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.

12

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

14

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

16

• 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

17

A Characterization of Terrain Complexity

18

• 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

19

• 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

20

• 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.

22

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)

23

Pro

ce

ss

ing

se

qu

en

ce

A Study Case: Flash Flooding in Morlaix

24

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.

.

25

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

26

• The simulation was executed in 32 steps, equivalent to 32 hours.

PART 3

Physical Modeling• Radio Coverage Computation

Vertical Interpretation of Signal Routes

28

• 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.

29

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.

30

Parallel Algorithm for Cellular Long-range Coverage Computation

31

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)

32

Initializing local values

Preparing for the first

communication

session

Directed Breadth-First Search Algorithm (2)

33

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)

34

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.

35

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)

36

• 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)

37

• Equipment Preparation

Mobile node

Base station

Experimental Measurement of Radio Coverage (3)

38

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.

39

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.

40

Performances of Computations (GPUs)

• Depending on cell size in pixel, zone size (up to 150K cells).

• Parallel algorithm with log(n) complexity.

41

Execution time (NVIDIA GTX1070)

Validation of Simulations• Experimental measurements for validation of radio coverage prediction in different complexity terrains.

42

Abert 1er

Plougastel

The Arrée mountains

43

Thank you for your attention!

SAMES project

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MICAS project

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Lacuna project

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