Aiming at fast establishment of a wireless network around a multi-level building in a disaster area, we propose an efficient method to determine the locations of network nodes in the air. Nodes are attached to balloons outside a building and deployed in the air so that the network can be accessed from anywhere in the building. In this paper, we introduce an original radio propagation model for predicting path loss from an outdoor position to a position inside a building. In order to address the three-dimensional deployment problem, the proposed method optimizes an objective function for satisfying two goals: (1) guarantee the coverage: the target space needs to be covered by over a certain percentage by wireless network nodes, (2) minimize the number of network nodes. For solving this problem, we propose an algorithm based on a genetic algorithm. To evaluate the proposed method, we compared our method with three benchmark methods, and the results show that the proposed method requires fewer nodes than other methods.

1. 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. 2
Outline
• Research Background
• Related Research
• Problem Formulation
• Radio Propagation Model
• Proposed Method
• Simulation and Real Experiment
• Conclusion

3. 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. 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. 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. 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
Purpose
A few nodes
Small deployment cost

7. 7
Outline
• Research Background
• Related Research
• Problem Formulation
• Radio Propagation Model
• Proposed Method
• Simulation and Real Experiment
• Conclusion

8. 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. 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. 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. 11
Outline
• Research Background
• Related Research
• Problem Formulation
• Radio Propagation Model
• Proposed Method
• Simulation and Real Experiment
• Conclusion

12. 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. 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. 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. 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. 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. 17
Outline
• Research Background
• Related Research
• Problem Formulation
• Radio Propagation Model
• Proposed Method
• Simulation and Real Experiment
• Conclusion

18. 18
Approach of K-Coverage
• Calculate RSS using the
proposed model
• If RSS ≧ T then 1, else 0 AP
Cell’s RSS Higher than T
・・・0 0 0 1 1
AP
・・・1 1 1 1 0
+
・・・1 1 1 2 1
||

19. 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
Finish
No 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. 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. 21
Outline
• Research Background
• Related Research
• Problem Formulation
• Radio Propagation Model
• Proposed Method
• Simulation and Real Experiment
• Conclusion

22. 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. 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. 24
Simulation Results (k=1,3)
• The proposed method used fewer nodes
• Reduced nodes up to 50%
k=1 k=3
3.1 3.7
4.24.3
5.1
7.1
4.1
5.1
7.8
15 15
16
0
2
4
6
8
10
12
14
16
18
85 90 95
NumberofNodes
coverage[%]
Proposed method
Local Search
RS
BreadCrumb
10.1 11.6
13.9
16.8
19.8
24.3
11.9
19.1
25.2
45
48
52
0
10
20
30
40
50
60
85 90 95
NumberofNodes
coverage[%]
Proposed method
Local Search
RS
BreadCrumb

25. 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. 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. 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. 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. 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 ]