Ryoichi Shinkuma received the B.E., M.E., and Ph.D. degrees in communications engineering from Osaka University, Japan, in 2000, 2001, and 2003, respectively. In 2003, he joined the Faculty of Communications and Computer Engineering, Graduate School of Informatics, Kyoto University, Japan, where he is currently an Associate Professor. He was a Visiting Scholar with the Wireless Information Network Laboratory, Rutgers, The State University of New Jersey, USA, from 2008 to 2009. His research interest is mainly cooperation in heterogeneous networks. He received the Young Researchers’ Award from IEICE, in 2006, and the Young Scientist Award from Ericsson Japan, in 2007. He also received the TELECOM System Technology Award from the Telecommunications Advancement Foundation, in 2016. He has been the Chairperson of the Mobile Network and Applications Technical Committee of the IEICE Communications Society since 2017.

1. Cooperation in
heterogeneous networks
Ryoichi Shinkuma
Graduate School of Informatics
Kyoto University, Japan
April 2019
1

2. Drones (Micro UAVs)
Trend: diversification of connected `things'
2
Reserved by Ryoichi Shinkuma
Connected things
Before 2000, only
servers and PCs are
connected
After 2005, many kinds
of smart devices are
connected
In coming years,
everything will be
connected
My research
started in 2003
2000 Feature
phones
Servers PCs
2010 IoT
2005
2015
2020
My research started
Diversified
Smartphones
Wearable
devices
Connected cars

3. Benefits of heterogeneous networks
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Diversity of environment Diversity of connection
Diversity of resources Diversity of data
Many nodes for
cooperation,
congested
network
Few nodes for
cooperation,
no congestion
Powerful
computing
resource
High-speed
communication
resource
Wide-range but
low-speed
connection
High-speed but
short-range
connection
Collect and
provide bird's-
eye view and
wide-range data
Collect and
provide fine-
grained data in
dedicated area

4. Cooperation in homogenous network
One entity can control all
nodes
How to optimize?
e.g. Maximum flow problem
s t
b
a
e
g
f
c
d
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Maximize flow from s to t

5. Definition of `heterogeneous'
More than just variety of
parameters:
• Two or more kinds of
nodes
• Each node owned and
controlled by different
entities
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Much more difficult
to optimize
than homogeneous
networks

6. Research questions
Communications
Engineering
Mathematical
Informatics
Economics
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How to...
 define cooperative actions
 model cost caused by
cooperation
 model utility brought about
by cooperation
 model individual decision-
making and behavior
 design mechanism for
incentivizing nodes
 create means of giving
incentive rewards

7. How to solve the problem of cooperation in
heterogeneous networks
Conventional approach: simplify and introduce game theory [Han'12]
My approach: understand and create a new model [Shinkuma'12]
Incentive reward
allocated to
contribution, Ri
Utility brought
about by
cooperation, Ui
Cost caused by
cooperation, Ci
Ui + Ri > Ci ?
Yes
No
No motivation
Incentive
mechanism S Ui
S Ri
(< S Ui – Profit)
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[(Invitied) R. Shinkuma and K. Yamori, ``A Dynamic Traffic Control Technique in Communication Networks Using Incentive Engineering,''
IEICE Technical report, CQ2012-69, Nov. 2012 (in Japanese)

8. Actions for cooperation in heterogeneous
networks
Sharing
• Communication resources (bandwidth) and energy resources to
forward other's data
• Computing resources and energy resources to process other's task
Delaying
requests from peak time to off-peak time
Moving
to location where more efficient resource is available
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9. [H. Kubo, R. Shinkuma, and T. Takahashi, IEICE Trans. Inf. & Syst., vol. E93-D, no. 12, pp. 3260-3268, Dec. 2010]
[M. Yoshino, R. Shinkuma, and T. Takahashi, Proc. IEEE GLOBECOM 2008, vol. 12, pp. 5042-5046, Dec. 2008]
Modeling of cost caused by cooperative
forwarding
Modeling of cost
• Forwarding data for others causes
battery cost
• Psychological factor related to
social relationship?
Experimental evaluation
• Emulator developed by our lab
• 58 subjects
• Results of qR :
• Friends: 41.2%
• Friends of friends: 55.4%
• Strangers: 60.8%
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Relay network
Social network

10. Differentiate social relationships (1)
• Prior work: only friends,
friends of friends, and
strangers
• My work: more
differentiated social
relationship metrics for
cooperative forwarding
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[Y. Inagaki and R. Shinkuma, "Shared-Resource Management Using Online Social-Relationship Metric for Altruistic Device Sharing,''
IEEE Access, vol. 6, pp. 23191-23201, April 2018]
Adamic-Adar Index
Katz Index
( kz: no. of degree of z )
( Al
xy: number of paths of length l between x and y )
Extract from structure
of social networks,
validated by experiment
using Stanford
Brightkite dataset

11. Differentiate social relationship (2)
• Prior work: only pre-
determined social
relationships
• My work: more dynamically
changed social
relationships
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[R. Shinkuma, Y. Sugimoto, and Y. Inagaki, "Weighted network graph for interpersonal communication with temporal regularity,''
Springer Soft Computing, Nov. 2017]
Convert time
domain
to frequency
domain
per 1 day
per 10 minExtract from intercontact
logs like message
exchanges, Bluetooth
connections, validated by
experiment using MIT
Friends-and-Family dataset

12. Incentive mechanism for cooperative forwarding
Bandwidth Exchange (BE) in
wireless relay network:
Giving a portion of radio frequency
band when asking for forwarding
Nash Bargaining Solution (NBS)
based incentive mechanism
• Maximize product of utilities
• Ensure efficiency and fairness
Problem formulation
• ui: utility of node i
• Pij: relay-request probability from i to j
• lx: probability of choosing strategy x
• Pc
ij: cooperation probability of i for j
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13. Incentive mechanism for cooperative forwarding (2)
Geometricmeanofthroughput:
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 Decision-making model
• NBS: long-term profit
• Myopic: short-term profit
• Altruistic: always cooperate
 Numerical evaluation
• Rayleigh fading channel
• Orthogonal frequency division multiplexing
(OFDM) system
• Results: NBS achieves best fair throughput
NBS saves 4 times transmission
power

14. Means of giving incentive reward for cooperative
forwarding
• Monetary payment
• Payment by quality of service (QoS)
• Radio frequency band in physical layer [Zhang, 2010]
• TXOP in data link layer [Nishio, 2012]
• Packet queuing in network layer [Liu, 2015]
• Flow rate in transport layer [Nishio, 2011]
• Content delivery in application layer [Maki, 2012]
Physical
Data link
Network
Transport
Session
Presentation
Application
[D. Zhang, R. Shinkuma, and N. Mandayam, IEEE Trans. Wireless Communications, vol. 9, no. 6, pp. 2055-2065, Jun. 2010]
[T. Nishio, R. Shinkuma, T. Takahashi, and N. Mandayam, IEICE Trans. Commun., vol. E95-B, no. 6, pp. 1944-1952, Jun. 2012]
[W. Liu, R. Hu, R. Shinkuma, and T. Takahashi, IEICE Trans. Commun., vol. E98.B, no. 11, pp. 2141-2150, Nov. 2015]
[T. Nishio, R. Shinkuma, T. Takahashi, and G. Hasegawa, EURASIP Journal on Wireless Communications and Networking, vol. 2011,
no. 1, Aug. 2011]
[N. Maki, T. Nishio, R. Shinkuma, T. Mori, N. Kamiyama, R. Kawahara, and T. Takahashi, IEICE Trans. Inf. & Syst., vol. E95-D, no. 12,
pp. 2860-2869, Dec. 2012] 14
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15. Modeling of utility brought about by cooperative
computing-resource sharing
 Sharing computing resources via
networks
• Utility
• Problem formulation
Is it different from forwarding?
 Modeling of utility
• Service consists of multiple tasks;
task-oriented cooperation might
cause inefficient cooperation
=> Service-oriented cooperation is
proposed
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Computing-resource sharing network

16. Modeling of utility brought about by cooperative
computing-resource sharing (2)
Results
• Proposed (service-oriented)
cooperation reduced latency
up to upper-bound level
• Proposed model achieved best
fairness
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Problem formulation
• ti: service start time with cooperation
• t'i: service start time w/o cooperation
• Tl
i: processing time for task l of node i
• Ei: energy consumption with
cooperation
• E'i: energy consumption w/o
cooperation
(Reduced service latency)

17. Delaying as cooperative action
Delaying requests from peak time
to off-peak time
Delaying should not cause another
peak traffic
[R. Shinkuma, Y. Tanaka, Y. Yamada, E. Takahashi, and T. Onishi, Elsevier Computer Networks, vol. 137, pp. 17-26, Jun. 2018]
[Y. Yamada, R. Shinkuma, T. Iwai, T. Onishi, T. Nobukiyo, and K. Satoda, vol. 146, pp. 115-124, Dec. 2018] 17
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(Current traffic) (Predicted traffic
after delaying)

18. Delaying as cooperative action (2)
Modeling of decision making
and behavior
• Random utility model: statistic
model for making binary
decisions (delay or not)
Results
• Reproduced traffic, estimated from
signal quality measured in
Kawasaki-city, Kanagawa, Japan
• Off-peak is reduced by cooperation
instead of forcing nodes to delay
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(Expected utility by choosing `Yes')
(Expected utility by choosing `No')
(Probability of choosing `Yes')
TA: sensitivity
of nodes to
delay

19. Moving as cooperative action
Moving to location where more efficient resource is available:
[T. Kangawa, M. Yoshino, R. Shinkuna, and T. Takahashi, IEICE Trans. Communications, vol. J90-B, no. 12, pp. 1263-1273, Dec. 2007 (in Japanese)]
[M. Yoshino, K. Sato, R. Shinkuma, and T. Takahashi, IEICE Trans. Commun., vol. E91-B, no. 10, pp. 3132-3140, Oct. 2008]
[T. Kakehi, R. Shinkuma, T. Murase, G. Motoyoshi, K. Yamori, and T. Takahashi, IEICE Trans. Commun., vol. E95-B, no. 6, pp. 1965-1973, Jun. 2012] 19
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20. Conclusion
Recommended design of cooperation mechanism in heterogeneous
networks
1. Cooperative actions:
Sharing resources, delaying, moving
2. Cost model:
Psychological factor related to social relationships
3. Utility model:
Service-oriented
4. Incentive mechanism:
NBS-based
5. Decision-making:
stochastic models like random utility theory are ready-to-use but not
enough to reflect individual characteristics
=> further research is needed
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