1

BalloonNet : A Deploying Method for

a Three-Dimensional Wireless Network

Surrounding a Building

Shinya Matsuo(1 Weihua Sun(2 Naoki Shibata(1

Tomoya Kitani(3 Minoru Ito(1

(1 -- Nara Institute of Science and Technology

(2 -- Osaka University

(3 -- Shizuoka University

2

Outline

• Research Background

• Related Research

• Problem Formulation

• Radio Propagation Model

• Proposed Method

• Simulation and Real Experiment

• Conclusion

3

Research Background

• Large Scale disasters occurs frequently

– Earthquake, Tsunami, Extreme Weather

• Advanced Health and Disaster Aid Network

– Electric Triage System

– Wireless network is necessary

– Restoration of Communications infrastructure is an urgent task

• Ad-hoc networks are attracting attention

– Building a wireless network using only network nodes without a

base station

[1]Tia Gao, Dan Greenspan, Matt Welsh, Radford R. Juang,Alex Alm, “Vital Signs Monitoring and

Patient Tracking Over a Wireless Network”, the 27th Annual International Conference of the IEEE EMBS, Shanghai, Sep 2005.

4

Related Research (Sensor Deployment)

• Bread Crumb [2]

– Network nodes are deployed one by one at equidistant

intervals inside a building

– Add nodes until the coverage requirement is met

• Problems

– Need many network nodes

– Deployment cost

[2] M.Souryal, J. Geissbuehler, L. Miller, N. Moayeri, “RealTime Deployment of Multihop Relays

for Range Extension”, the 5th International Conference on Mobile Systems, Application and Services, 2007, pp.85-98.

5

Related Research (System in Air)

• Launch network nodes and antennas using balloons in the sky[4]

– Balloons are launched at intervals of several kilometers

– Provide wide range coverage

– Additional functions available

• video cameras, atmosphere sensors

• Problems

– Specialized devices

– Enormous time for installation

– Cannot cover the inside of building

[4] Yoshitaka Shibata, Yosuke Sato, Naoki Ogasawara, Go Chiba, ”Ballooned Wireless Mesh Network for Emergency

Information System”, Advanced Information Networking and Applications Workshops,2008 AINAW

6

Purpose of Our Research

Idea

• Launch network nodes to balloons

• Surrounding the target building

– Easy to install and adjust the balloons

– Wide radio coverage

• Covers multiple floors through windows

• Consider the deployment algorithm

– Minimize the number of network nodes

PurposeA few nodes

Small deployment cost

7

Outline

• Research Background

• Related Research

• Problem Formulation

• Radio Propagation Model

• Proposed Method

• Simulation and Real Experiment

• Conclusion

8

Assumptions

• Balloon

– Moored by strings from 3 ground points

• Network nodes

– Battery-powered ad-hoc router

• Building is shown by a set of cubic cells

– A unique ID that corresponds to its position

– Has attribute of material

– Cell is the smallest unit of coverage

• Radio attenuation can be predicted by

propagation model

– If the received signal strength (RSS)≧T

then communication is available

Node-50dBmPossible

-90dBm

Impossible

If T=-80dBm

9

Problem Formulation

• Input

– Floor plans of the target building

– Locations where balloons can be placed

– Radio propagation prediction model

– Requirement of number/ratio of coverage

• Output

– Location and the number of network nodes

• Objective

– Minimize the number of network nodes

10

Constraints of Deploying

• K-Coverage

– The target field must be covered by no less than k nodes’ radio

• Improve the network stability and Provide additional functions

– Ex: Positioning

1-Coverage 2-Coverage

Node

11

Outline

• Research Background

• Related Research

• Problem Formulation

• Radio Propagation Model

• Proposed Method

• Simulation and Real Experiment

• Conclusion

12

Radio Propagation Model

• Existing radio attenuation model

– ITU[5], COST 231[6]

– Most are Outdoor-to-Outdoor, Indoor-to-Indoor

– Few of Outdoor-to-Indoor model

• Existing Outdoor-to-Indoor model

– The error is too big to fit to the survey data

• We constructed an original radio propagation model

– Based on survey data

[5] ”Propagation data and prediction methods for the planning of indoor radio communication systems and the radio

local area networks in the frequency range 900MHz to 100 GHz”, ITU-R Recommendations, Geneva, 2001

[6] J.E.Berg,”4.6 building penetration in Digital Mobile Radio Toward Future Generation Systems,” COST Telecom

Secretariat, Commission of the European Communities, Brussels, Belgium, pp. 167-174, COST231 Final Rep., 1999. sec.

4.6

13

Para. of Attenuation Prediction Model

• Parameters

– d1 : The shortest distance

– d2 : The detour distance

– Oi : The number of obstacles

• ρ: Attenuation of obstacles

– Outer wall

– Inner wall

– Floor/Ceiling

Node

Survey Spot

d1

d2

Inner walls: 2

Outer walls: 3

14

Overview of Measurement

• Place:

– Information science graduate school, NAIST, Japan

• Network nodes is placed outside

• Measure RSS at multiple spots in the building

– Staff holds a laptop PC to measure RSS

– Towards to node, stops for 15 seconds, uses the average value

– Measurement was performed on multiple floors

• Band: 2.4GHz

• About 150 results

15

Fitting through Measured Result

• We received the bellowing attenuation model

• Compared with Okamoto model [7][8]

Obstacles Coefficient

Outer wall:-6.44, Inner wall: -0.72, Floor:-3.41 [dBm/each]

[7] Hideaki Okamoto, Koshiro Kitao and Shinichi Ichitsubo, “Outdoor-to-Indoor Propagation Loss Prediction in 800-MHz

to 8-GHz Band for an Urban Area”, IEEE TRANSACTION ON VEHICULAR TECHNOLOGY, vol.58, No.3, pp.1059-1067,

Mar. 2009.

[8] K. Kitao and S. Ichitsubo, “Path loss prediction formula for microcell in 400MHz to 8GHz band”, Electronics Letters,

vol. 40, no. 11, pp.685-687,May. 2004.

16

Error Comparison (vs. Okamoto)

• The proposed model

– When d1=d2

• Error in Okamoto model

grows larger over distance

Ave. Error [dBm] Standard Deviation

Proposed model 4.06 3.51

Okamoto model 13.27 10.07

17

Outline

• Research Background

• Related Research

• Problem Formulation

• Radio Propagation Model

• Proposed Method

• Simulation and Real Experiment

• Conclusion

18

Approach of K-Coverage

• Calculate RSS using the

proposed model

• If RSS ≧ T then 1, else 0AP

Cell’s RSS Higher than T

・・・0 0 0 1 1AP

・・・1 1 1 1 0

+

・・・1 1 1 2 1

||

19

Mechanism of Calculation

• 3D nodes deployment is a NP-hard class problem

– We use a heuristic method based on genetic algorithm

Search by GA

Create a new solution

N-1 based on the received

solution set

FinishNo solution

Found solutions

satisfied all

constraints

Use new

solution N-1 as

next initial

solution

Create an Initial solution N for

the number of network nodes

20

Details of Genetic Algorithm

• Encoding

– Chromosome (A solution candidate):

• a vector including 3 integers representing coordinates of a node

– Population (A list of chromosomes)

• Evaluate function

– K-coverage rate

• Finish

– If the evaluation score is not updated for 10 generations

21

Outline

• Research Background

• Related Research

• Problem Formulation

• Radio Propagation Model

• Proposed Method

• Simulation and Real Experiment

• Conclusion

22

Simulation Configuration

• Simulation input

– Building 1F-7F of Graduate school of Information Science,

NAIST

• Size of a cell

– Width: 0.96m

• Radio Strength threshold T= -86 [dBm]

• Required number of coverage: k= 1--3

• Required Ratio of k-coverage: 85--95%

23

Benchmark Methods

• Repetitive Random Search (RS)

– Repetitively and randomly search better deployment plan

• Local Search (LS)

– Randomly create an initial solution

– Repetitively run local search based on the initial solution

• Bread Crumb (BC)

– The first one is set to the entrance of building

– Then set other nodes by same interval

– If the coverage does not meet requirement, then add node

– All nodes are set in the building

• Run above methods by same time to the proposed method

• Compared the average results of 50 trials

24

Simulation Results (k=1,3)

• The proposed method used fewer nodes

• Reduced nodes up to 50%

k=1 k=3

3.1 3.74.24.3

5.1

7.1

4.15.1

7.8

15 1516

0

2

4

6

8

10

12

14

16

18

85 90 95

Nu

mb

er

of

No

de

s

coverage[%]

Proposed method

Local Search

RS

BreadCrumb

10.1 11.613.9

16.819.8

24.3

11.9

19.1

25.2

4548

52

0

10

20

30

40

50

60

85 90 95

Nu

mb

er

of

No

de

s

coverage[%]

Proposed method

Local Search

RS

BreadCrumb

25

Real Experiments

• We conducted a real world experiment to investigate

the result of simulation

– Used the solution from simulation

– Measured radio attenuation

• We measured more than 300 spots on 1F-2F of the

target building

• Deployment requirement

– 1-cover, node number 3, coverage rate 92.6%

26

Experiment Result

• Average error: 11.08[dBm]

• Coverage rate: 86.6% （error 6%）

1F 2F

Building A

Building B

Building A

Building B

×

××

27

Indoor Positioning based on RSS

• We conducted a range-based positioning trial by a 3-coverage

deployment

• Measure RSSs from the 3 nodes

• Use the proposed propagation

model to estimate the position

• Conducted for 70 spots

• Average error 3.86[m]

– The accuracy is enough to detect the target in which room

28

Conclusion

• We proposed an efficient deploying method to install

network nodes surrounding buildings in disaster area

– Proposed an original radio attenuation model

– Used a heuristic algorithm based on GA

• Future work

– Improve the algorithm to reduce computing complexity

– Improve the propagation model to apply to general

environment

29

Shinya Matsuo, Weihua Sun, Naoki Shibata,

Tomoya Kitani and Minoru Ito : "BalloonNet: A

Deploying Method for a Three-Dimensional

Wireless Network Surrounding a Building," in

Proc. of the Eighth International Conference on

Broadband and Wireless Computing,

Communication and Applications (BWCCA-2013),

pp.120-127.

DOI:10.1109/BWCCA.2013.28, [ PDF ]