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Outline1. What is SenMinCom ?2. Past Works & Why SenMinCom ?3. How SenMinCom ?4. SenMinCom’s Contributions 5. Current Methods v/s SenMinCom
6. SenMinCom’s Simulations1. Shopping Model2. Mobile Device Usage Model
7. Conclusion8. References9. Acknowledgements
What is SenMinCom [24] ? Independent units
that receive and respond to signals
Unobtrusive Cheaply available
computer
Sensing
contd… Process of sorting
through heap of data and picking out relevant gems
Mostly on data that have not been previously discovered
Mining
contd… Mobile commerce or
U commerce is the ability to conduct commerce using a cellular device
U-commerce because of its Ubiquitous-ness
mobile Comme
rce
Past Works & Why SenMinCom [24] ?
Sensors restricted to defense, environmental tracking, etc.Cellular phones limited to entertainment, commerce, etc.
contd…
Environmental monitoring [1-7]
• Enormous potential over the traditional invasive methods
Smart environments [8]
• Self-organizing, adaptive systems
Mobile Commerce [9-10]
• Discusses frameworks, applications
contd…
GypSii [11-12]
• Location service management
Mobile social networking [12-13]
• Share photos, send invitations, etc.
contd…
NTT DoCoMo’s Wellness Phone [14-15]
• Keeps track of runs, heart pulse, mini body fat calculator
Tsunami warning system [16]
• Register an earthquake & relays warning messages to the people in affected area
HealthGear [17]
• Set of non-invasive physiological sensors that monitors a patient’s health
contd…
Schwan’s for route sale drivers [18]
• Wirelessly record sales, issue receipts and track inventory
contd…Shopping Scenario
•No method to get the real time shopping pattern
•No way to know about shoppers’ preference for products
•No effective way to lure shoppers’ before they leave store
•No system that benefits retail and consumer
contd…Mobile Usage Scenario
• Companies rely on quarterly surveys
• No method to get real-time mobile usage
contd…
Centralized static data mining
Off-line data mining
Time & resource intensive
Sink processes & analyzes
Sensor tracks & forwards
contd…
Mobile agent•Formed by code & data•Clone & migrate•Reduce network load•Overcome network latency
Data mining•Reveal patterns•Easy to perceive, interpret, manipulate •Mining accomplished on real time data rather than on a snapshot
Sensor •Achieves unobtrusive sensing•Gathers +Processes +Communicates
contd…
Distributed Dynamic
Mining System (DDMS)
Reduces communication effort [19]
Dimensionality reduction of data mining
Mining on fresh data
Less resource intensive
contd…
A DDMS is a set of transactions <T, t> where 'T' is a purchase or product information event and 't' represents the time and date of the occurrence of T.
DDMSM = { <T1 ,t1>...<Tn, tn>} where M is the recorded Mac Id of the customer's cell phone.
System will have pre-defined rule base from which the distinction of customers is achieved.
contd…
i. Define Rules and corresponding parameters
for each Rule
ii. for shopper (Mac Id) m=1 to M
i. Identify DDMSm from set of DDMS
ii. for segment r=1 to Ri. Select Ruler from the set of Rules, R
ii. If(Ruler ⊆ DDMSm ) add shopper m to group r
iii. The R groups of shoppers are the segments
contd…If(thisNode = = firstAggregator)
MA migrates toward firstAggregatorElse if( (thisNode = = nextAggregator) &&
(nextAggregator != lastAggregator) )MA collects sensed raw data and does local miningSet nextAggregator in the MA packetMA migrates towards next aggregator
Else if(thisNode = = lastAggregator)MA collects sensed dataMA migrates back to sink
Current Methods v/s SenMinCom [24]
Communicating messages consume far more energy than processing it [21]
• Mining done at aggregator
Mined results don’t affect real-world situation [22]
• Mining takes place on fresh data
Link bandwidth of wireless sensor network very less [20]
• Agent carries only the result set
contd…
Central mining costly in terms of communication and storage [23]
• Task of mining distributed on all aggregators
Sensor nodes passive
• Sensor made an active device
Shopping Model [24]
Random shoppers have no strong intention to purchase something, and just wander among aisles a.k.a. window shoppers
Rational shoppers visiting a store, know clearly what they need a.k.a prompt shoppers
Recurrent or regular customers are customers who visit the store often. They can be further divided into Customers with higher purchasing power Customers with lower purchasing power
contd…
Example Book store company e.g. Barnes & Nobles Store modeled on SenMinCom architecture
Result Customers shopping & checkout patterns
dynamically tracked
contd…
Features
1. Aisle wise real time products distribution
2. Reveals aisle popularity
Consequences
3. Restacking products
4. Maximize selling
Ais
le I
Ais
le II
Ais
le II
I
Ais
le IV
Ais
le V
Ais
le V
I
Ais
le V
II
Ais
le V
III
Ais
le IX
Ais
le X
0
10
20
30
40
50
60
Ais
le I
Ais
le II
Ais
le II
I
Ais
le IV
Ais
le V
Ais
le V
I
Ais
le V
II
Ais
le V
III
Ais
le IX
Ais
le X
Aisles
Prod
ucts
Aisle
contd…
Features
1. Aisle wise real time products distribution at separate time intervals
2. Aisle popularity
Consequences
3. Restacking products according to different hours of a day, days in a week, etc.
Ais
le I
Ais
le II
Ais
le II
I
Ais
le IV
Ais
le V
Ais
le V
I
Ais
le V
II
Ais
le V
III A
isle
IX
Ais
le X
Ais
le I
Ais
le II
Ais
le II
I
Ais
le IV
Ais
le V
Ais
le V
I
Ais
le V
II
Ais
le V
III
Ais
le IX
0
10
20
30
40
50
60
70
80
Aisle I Aisle II Aisle III Aisle IV Aisle V Aisle VI AisleVII
AisleVIII
Aisle IX Aisle X
Aisles
Prod
ucts
Time 1
Time 2
contd…
Features
1. Reveals customers purchasing power
2. Categorize customers
Consequences
3. Directed products promotion
30
34 34
38
43
46
49 49 50
42
0
10
20
30
40
50
60
Ali
ce
Che
tan
Chi
rayu
Sarj
i
Sara
h
Mar
k
Mon
ica
Muk
und
Sund
ar
Mou
nica
Customers
Prod
ucts
Shopping Share
contd…
Feature
1. Products lifted to checked out
Consequences
2. With shopping history product promotion offers
3. Customer
Ali
ce
Che
tan
Chi
rayu
Mar
k
Mon
ica M
ouni
ca
Muk
und
Sara
h Sund
ar
Ali
ce Che
tan
Chi
rayu
Mar
k Mon
ica
Mou
nica
Muk
und
Sara
h
Sarj
i
Sund
ar
Sarj
i0
10
20
30
40
50
60
Ali
ce
Che
tan
Chi
rayu
Mar
k
Mon
ica
Mou
nica
Muk
und
Sara
h
Sarj
i
Sund
ar
Customers
Prod
ucts
Checkout Share
Shopping Share
contd…
Feature
1. Products lifted to checked out customer level
Consequences
2. Shopping history leads product promotion offers
3. Products picked to checked out share
4. Aisle movement pattern
3
3
3
5
4
4
11
1
4
2
0
3
5
4
5
3
10
1
4
0
0 1 2 3 4 5 6 7 8 9 10 11 12
Aisle I
Aisle II
Aisle III
Aisle IV
Aisle V
Aisle VI
Aisle VII
Aisle VIII
Aisle IX
Aisle X
Prod
ucts
Aisles
Checkout
Shopping
Mobile Device Usage Model Popular cellular phone cravings
Brand popularity where the people are attracted or loyal towards a company
For a cell phone company, popularity of a given model or total volume of their models
Cellular phone usage among an age group Educational period is a stage among the age
group of 18-28, generally students attending schools, colleges, and universities.
Working period, among the age group 28-60
contd…
Example Georgia State University Campus Area modeled on SenMinCom architecture
Result Students real time device usage scenario Manual device survey avoided
contd…M
otor
ola
LG
Sam
sung
Nok
ia App
le
Bla
ckB
erry
0
10
20
Mot
orol
a
LG
Sam
sung
Nok
ia
App
le
Bla
ckB
erry
Mobile Device
Mobile Devices GSU Plaza
contd…M
otor
ola LG
Sam
sung Nok
ia
App
le
Bla
ckB
erry
0
10
20
30
Mot
orol
a
LG
Sam
sung
Nok
ia
App
le
Bla
ckB
erry
Mobile Device
Mobile Devices GSU Student Center
contd…M
otor
ola
LG
Sam
sung
Nok
ia
App
le
Bla
ckB
erry
0
10
20
30
40
Mot
orol
a
LG
Sam
sung
Nok
ia
App
le
Bla
ckB
erry
Mobile Device
Popular Mobile Devices @ GSU
contd…
Features
1. Area wide popular mobile models
2. Total mobile device usage scenario
Consequences
3. Real time mobile popularity
4. Brand consideration leads to streamlining promotions
contd…
Feature
1. Various mobile models of a brand
Consequences
2. Popularity of models
3. Reasons like cost, intriguing features, etc. revealed
0
7
0 0
9
6
0
10L
G S
hine
LG
Ax3
90
LG
Sco
op
LG
env
LG
Vx8
500
LG
Vx8
550
LG Models
Vol
ume
LG
contd…3
0
5
4
1
0
0
10
Mot
o R
azr V
3m
Mot
o R
azr V
9
Mot
o K
rzr k
1m
Mot
o R
azr v
8
Mot
o Q
9c
Mot
o V
365
Motorola Models
Vol
ume
Motorola
Mot
orol
a
LG
Sam
sung
Nok
ia
App
le
Bla
ckB
erry
0
10
20
30
40
Mot
orol
a
LG
Sam
sung
Nok
ia
App
le
Bla
ckB
erry
Mobile Device
Motorola Volume Usage Popular Mobile Devices
contd…
Feature
1. Market share of cell phone models
Consequences
2. Timeline based share of model
3. Provide insight for a newly released model
0
2
8
19
17
11
0
10
20
Tim
e I
Tim
e II
Tim
e II
I
Apple Models
Vol
ume
iPhone 3
iPhone 2
contd…
Feature
1. Mobile usage of new cell phone models
Consequences
2. Crosscheck their marketing campaign
3. Peoples’ current mobile preferences
2
8
1
3
4
0
10M
oto
Kra
zrK
1m
iPho
ne 3
G
LG
Vx8
500
LG
Vx8
550
LG
Ax3
90New Sightings
Vol
ume
New
Conclusion
SenMinCom [24]
Sensors extended to retail
Real time pervasive system
Data centric
Real time analysis of business
References1. Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J.,“Wireless
Sensor Networks for Habitat Monitoring”, Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, 2002, pp.88-97.
2. Warrior, J., “Smart Sensor Networks of the Future”, Sensors Magazine, March 1997.
3. Pottie, G.J., Kaiser, W.J., “Wireless Integrated Network Sensors”, Communications of the ACM, vol. 43, no. 5, pp.551-55 8, May 2000.
4. Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton M., Zhao, J., “Habitat monitoring: Application driver for wireless communications technology”, 2001 ACM SIGCOMM Workshop on data Communications in Latin America and the Caribbean, Costa Rica, April 2001.
5. Werner-Allen, G., Johnson, J., Ruiz, M., Lees, J., Welsh, M., “Monitoring volcanic eruptions with a wireless sensor network”, Wireless Sensor Networks, 2005. Proceedings of the second European Workshop, 2005, pp.108-120.
6. Intel Research Sensor Network Operation, http://intel.com/research/exploratory/wireless_sensors.htm .
7. Shih, E., Cho, S., Ickes, N., Min, R., Sinha, A., Wang, A., Chandrakasan, A., “Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks”, Proceedings of ACM MobiCom’01, Rome, Italy, July 2001, pp.271-286.
contd…8. Herring, C., Kaplan, S., “Component-based software systems for smart
environments, IEEE Personal Communications, October 2000, pp. 60-61.
9. Varshney, U., Vetter, R., “Framework, Applications, and Networking Support for M-commerce”, ACM/Kluwer Journal on Mobile Network and Applications (MONET), June 2002.
10. Varshney, U., Vetter, R., Kalakota, R.,”Mobile Commerce: A New Frontier”, IEEE Computer, 2000, 22(10), pp.32-38.
11. GyPSii Webtop, http://www.gypsii.com/
12. Social Networking moves to the cell phone, http://www.nytimes.com/2008/03/06/technology/06wireless.html?_r=1&oref=slogin
13. Social Network Zingku, http://www.infoworld.com/article/07/09/28/Google-buys-Zingku-mobile-social-networking-service_1.html
14. NTT DoCoMo Newsletter, Mobility, Adding the Human Touch to Communication, http://www.nttdocomo.com/binary/about/mobility_doc_15.pdf
15. New Cell phone doubles as personal trainer and shrink, http://tech.yahoo.com/blogs/null/50133
contd…16. MedDay has Breakthrough solution for Tsunami Warning System
based on disease detection and management system, http://www.hoise.com/vmw/05/articles/vmw/LV-VM-02-05-16.html
17. Oliver, N., Flores-Mangas, F., HealthGear: A Real-time Wearable System for Monitoring and Analyzing Physiological Signals, Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks (BSN ’06), 2006
18. Schwan’s, http://www.bluetooth.com/NR/rdonlyres/826F390E-C82E-43A4-9810-B1D7291A275D/0/schwans.pdf
19. Goel, S., and Imielinski, T., “Prediction-based monitoring in sensor networks: Taking lessons from mpeg”, ACM Computer Communication Review, 31(5), 2001.
20. Chen M., Kwon, T., Choi, Y., “Data Dissemination based on Mobile Agent in Wireless Sensor Networks”, Proceedings of the IEEE Conference on Local Computer Networks 30th anniversary (LCN '05).
21. Chen M., Kwon, T., Yuan, Y., Leung V.C.M., “Mobile Agent Based Wireless Sensor Networks”, Journal of Computers, Vol 1, No. 1., April 2006
contd…22. Ong, K., Zhang, Z., Ng, W., Lim, E., “Agents and Stream Data
Mining: A New Perspective”, IEEE Intelligent Systems, June 2005.
23. Bontempi, G., Borgne, Y., “An Adaptive Modular Approach to the mining of Sensor Network Data”, 2005 SIAM International Conference on Data Mining, April 2005.
24. Hiremath, N., Zhang, Y., “SenMinCom: Pervasive Distributed Dynamic Sensor Data Mining for Effective Commerce,” Proceedings of 2008 IEEE International Conference on Granular Computing (GrC 2008), Aug 2008