Team presentation on PerAda 2009 Summer school... http://www.perada.eu

1. Nature Knows Best ?
How can biological systems be exploited in engineered systems ?

2. People
Cathy Scott c.scott@napier.ac.uk
Husna Osman ho12@hw.ac.uk
Ioannis Polyzos i.polyzos@gmail.com
Michael Matscheko mm@pervasive.jku.at
Petros Papadopoulos petros.papadopoulos@acm.org
Sarah Clayton s.clayton@napier.ac.uk

3. Outline
Introduction
Aim
Scenarios
Network Settings
Results
Fungal Colonies
Conclusion
Questions ?

4. Introduction
Routing strategies are highly important for computer
networks performance.
The emergence of mobile/sensor networks raised several
novel problems mainly due to resource constrained
devices.
Among others biological systems with the same
characteristics have been exploited in order to solve
several discrete optimization problems such as :
Ant Colonies
Fungal Colonies

5. Aim
This case study focus in :
Comparing “Ant Colony Optimization”(ACO) with other
discrete optimization algorithms like Nearest Neighborhood
Tree(NNT).
Exploring a recently introduced systems inspired by Fungal
Colonies
3 scenarios have been used in order to measure :
Efficiency , Scalability and Robustness

6. Scenarios
Scenario 1 : Static Network
Focus on the performance of NNT and ACO in a static
network where there is no update frequency/proactive ants.
Scenario 2 :
Focus on the performance of NNT(no update) and ACO(with
proactive ants) in a static network.
Scenario 3:
Focus on the performance of NNT(with update) and
ACO(with proactive ants) in a dynamic environment (possible
failures).

7. Network Settings
Typical Network1 :
Population : 50
Distance : 0.09
Futuristic Network 2 :
Population: 400
Distance : 0.04

8. Routing Overhead
10
• bug ?
8
• configuration ?
• more tests ?
6
4
ACO FA –
ACO FA 1
2
ACO FA 10
NNT UR --
NNT UR 5
0
0 5 10 15 20 25 30 35 40
NNT UR 10

9. Delivery Ratio
1
0.8
0.6
NNT UR --
NNT UR 5
NNT UR 10
0.4
ACO FA 1
ACO FA 10
0.2
corresponds to ACO FA –
routing overhead
0
0 5 10 15discrepancy 25
20 30 35 40

10. Delivery Ratio (Failure t=20)
1
low delivery rates:
0.8
• congestion
• reduce data load?
0.6
NNT UR 5
NNT UR 10
0.4
0.2
ACO FA 1
ACO FA 10
0
0 5 10 15 20 25 30 35 40

11. Delivery Ratio (400 nodes)
1
• far too much data
0.8
for large network
• rerun with
decreased data load
or increased radio
0.6
data rate
0.4
0.2 NNT UR 5
NNT UR 10
ACO FA 1
0 ACO FA 10
0 5 10 15 20 25 30 35 40

12. Conclusions
Ant algorithm struggles with (almost) static network
scenarious
Network congestion leads to low delivery ratio
Future Work:
Investigate situation in more dynamic networks
Volatile environment (high node failure rates)
Mobile nodes
Optimization of parameters
Longer simulation timespan

13. Fungi Colonies

14. Fungal Colonies
Processes Transactions
•Explore (sparse branching)
•Exploit (dense branching)
•Degenerate

15. Processes Triggers
Explore Fitness function
(sparse branching)
Exploit Data message
(dense branching)
Degenerate Energy < pdegenerate

16. Mappings
SpeckSim Fungal Colony
Energy Nutrients
Data Mobile biomass
Established route Structural biomass

17. Message Type Transmission type
Data message Broadcast
Explore message Broadcast
Establish route message Unicast

18. Network Flow

19. Thank you …
Questions ?