The document discusses Gopika Premsankar's doctoral research on analyzing and optimizing scalable networked systems. The research aims to enable reliable communications for heterogeneous devices through algorithmic approaches. Key areas of research include using secondary networks to improve scalability, optimally configuring LoRa network parameters for dense IoT deployments, and optimally placing edge computing devices. The research has proposed solutions that can efficiently solve large network instances while improving reliability even in dense network scenarios.

1. Scalable networked systems:
analysis and optimization
Gopika Premsankar
Doctoral candidate
Department of Computer Science
February 28, 2020

2. Future networked systems
Image source: Solvieg Been/Shutterstock

3. Different connectivity requirements
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Image source: Solvieg Been/Shutterstock

4. Different connectivity requirements
Battery-powered
Small infrequent
data (bytes)
Delay in hours
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Image source: Solvieg Been, Jarp2/Shutterstock

5. Different connectivity requirements
Battery-powered
Small infrequent
data (bytes)
Delay in hours
Mains-powered
Large video data
(MBs)
Delay in seconds
to minutes
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Image source: Solvieg Been, Jarp2, NinaM/Shutterstock

6. Different connectivity requirements
Battery-powered
Small infrequent
data (bytes)
Delay in hours
Mains-powered
Large video data
(MBs)
Delay in seconds
to minutes
Battery-powered
Large sensor-rich
data (MBs)
Delay in
milliseconds
33333333333333333
Image source: Solvieg Been, Jarp2, NinaM, Herr Loeffer/Shutterstock

7. A single network cannot
satisfy all requirements
Challenges in current networks
Image source: Solvieg Been/Shutterstock

8. Increasing interference with
high density of devices
A single network cannot
satisfy all requirements
Challenges in current networks
Image source: Solvieg Been/Shutterstock

9. Increasing interference with
high density of devices
A single network cannot
satisfy all requirements
Large latency of communication
to distant cloud data centers
Challenges in current networks
Image source: Solvieg Been/Shutterstock

10. Research goal
How to enable scalable and reliable communications
for large number of heterogeneous devices
through algorithmic approaches?

11. Research question 1
What is the role of secondary access networks
in enabling scalable communications?
Image source: Solvieg Been/Shutterstock

12. Secondary access networks
Utilize free spectrum in TV white space and Industrial, Scientific
and Medical (ISM) bands through opportunistic contacts between
mobile users

13. Distributor selection algorithm
Select distributors to maximize offloaded traffic
WS offloading enables 47% more capacity even with inaccurate
WSDB information
Publication I. Suzan Bayhan, Gopika Premsankar, Mario Di Francesco, Jussi Kan-
gasharju, “Mobile Content Offloading in Database-Assisted White Space Net-
works”, CROWNCOM ‘16, May 2016

14. Research question 2
How to achieve scalable and reliable communications
in dense urban deployments of LoRa networks?
Image source: Solvieg Been/Shutterstock

15. Adaptive configuration of LoRa parameters
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SF 8
SF 9
SF 10
SF 11
SF 12
Lower SF: higher data rate, less energy
Higher SF: lower data rate, more energy
Control parameters: spreading factor (SF), transmission power
to increase scalablity
Issues identified with Adaptive Data Rate (ADR) algorithm to
control parameters
Publication II. Mariusz Slabicki, Gopika Premsankar, Mario Di Francesco, “Adaptive
Configuration of LoRa Networks for Dense IoT Deployments”, IEEE/IFIP NOMS ‘18,
Apr 2018

16. Optimal assignment of LoRa parameters
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High transmission power Low transmission power
SF 7
SF 9
SF 10
SF 8
SF 7
SF 9
SF 10
SF 8
A A
B B
C C
D D
Integer Linear Programming model to assign parameters
to nodes in dense networks with multiple gateways
Improved reliability with up to 8% increase in successful delivery
of messages
Publication III. Gopika Premsankar, Bissan Ghaddar, Mariusz Slabicki, Mario Di
Francesco, “Optimal configuration of LoRa networks in smart cities”, IEEE Trans-
actions on Industrial Informatics, to appear

17. FLoRa
Open source framework for LoRa simulations (FLoRa)
https://flora.aalto.fi https://github.com/mariuszslabicki/flora

18. Research question 3
How can edge computing
provide scalable processing
for data-intensive applications?
Image source: Solvieg Been/Shutterstock

19. Properties of edge computing
Mobile
network
edge
CSC cloud
Kajaani
Finland
EC2
Frankfurt
datacenter
EC2
Ireland
datacenter
0
20
40
60
80
100
Networkdelay(ms)
WiFi
LTE
800 x 600 1280 x 720 1920 x 10800
10
20
30
40
50
60
Processingdelay(ms)
g2.2xlarge
g2.8xlarge
More processing power in the cloud does not compensate for the
network delay to reach distant data centers
Publication IV. Gopika Premsankar, Mario Di Francesco, Tarik Taleb, “Edge Com-
puting for the Internet of Things: A Case Study”, IEEE Internet of Things Journal
5(2):1275–1284, Feb 2018

20. Optimal placement of edge devices
Candidate edge device location
Mixed Integer Linear Programming model to optimally place
edge devices in a city
Improved delivery of messages and reduced overload
at edge servers when traffic volume is high
Publication V. Gopika Premsankar, Bissan Ghaddar, Mario Di Francesco, Rudi Ver-
ago, “Efficient Placement of Edge Computing Devices for Vehicular Applications
in Smart Cities”, IEEE/IFIP NOMS ‘18, Apr 2018. Best student paper award

21. Summary and future work
Proposed solutions that can solve large network instances
within a short time
Improved reliability of networks even in dense networks
Data-centric approach to optimizing the network