Energy Aware Self Organized Communication in Complex Networks

Jakob Salzmann, Dirk Timmermann

SPP 1183 Third ColloquiumOrganic Computing,

14.-15.09.2006, Stuttgart

Institute of Applied Microelectronics

and Computer Engineering

University of

Rostock

DFG 1183 Organic Computing 2

Outline

• Project introduction

• OC principles in research

• Current work

• Future work

• Conclusion

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Sensor network = paradigm of a complex network

Task:

• Collection of sensor data at many locations

• Transmit collected data to sink

Applications:

• Forest fire surveillance

• Movement of cars

• Detection of volcanic activity

• Intelligent house

Project introduction (1)

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Active nodeSink

Properties of a sensor network:– High node count– Random node distribution– Wireless communication

Project introduction (2)

Properties of a node:

Typical problems:– Energy limits lifetime– Node failure rate high– Centralized control infeasible

– Limited energy per node

– Transmission range

– Sensing range

Transmission range

?

Sensing range

!

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Our goal: Increase lifetime and robustness of sensor

networks using self-organized communication and organic

principles

Lifetime and robustness of a sensor network

A network „lives“ completely: – iff phenomens still can be detected in each observed location– iff messages from acquiring nodes can reach the sink

A structure of a sensor network is robust:– iff deliberate and random node failures up to a given extent do not

impact lifetime

Project introduction (3)

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OC principles in research

• Role assignment Less communication

• Graceful degradation / Controlled shutdown Less communication Less computation

• Scale free network More robustness

• Stigmergy Energy balancing

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Role assignment

• Clusterhead:– Distributes necessary data to his

cluster (i.e. sensoring cycle)– Collects and aggregates data– Communicates outside cluster

• Sensor nodes (Active nodes):– Measure data– Communicate with their

clusterhead only

Active nodeSinkClusterhead

• In Nature:– Concentration on specialized work– Data aggregation– Improvement by learning

• Introducing two roles

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Graceful degradation / Controlled shutdown (1)

• In nature: Hibernation of animals

• In sensor networks: Detection and temporary shutdown of redundant nodes

Detection:

• Redundant, if transmitting and sensing function can be adopted by adjacent nodes

• Inside a cell, only one node is necessary for coverage

• High effort for redundancy detection

• Our approach: define a grid Active nodeSensing rangeRedundant node

Max. Cellsize

Active nodeSink

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Controlled shutdown• Nodes inside a cell establish a

cluster

Graceful degradation / Controlled shutdown (2)

• Clusterhead can shutdown all nodes in its cell until specified time

Active nodeSinkClusterheadSwitched off node

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Scale free network (1)

• Network results from preferred connection

• US airline system

Scale free network

• Most nodes have alike number of connections

• US highway system

Random network

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Scale free network (2)

• Random network break down at

random faults

• Scale free network very robust

against random faults

• But prone to attack on main

nodes

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Active nodeSink

Scale free network (3)

• Our approach:– starting with sink….– after attending the network,

node connects with all unconnected nodes in transmission range

• Combination with graceful degradation

ClusterheadSwitched off node

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Switched off nodeClusterheadSwitched off node

Sink

Stigmergy

• Behavior of nodes adapts to different environments

• Clusterheads in highly populated clusters can be exchanged easily

• Permitted to spend more energy

• Permitted to connect with more adjacent nodes

• New energy balanced scale free structure

g

SinkClusterhead (Sparsely populated Cluster)

Clusterhead (Highly populated Cluster)

Switched off node

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Current work (1)

• Simulation of scale free routing strategies to analyze– Guaranteed connectivity– Behaviour of network with failed nodes– Balanced hop number

• Matlab Less programming effort Advantageous visualization

Changing connection rules

Higher transmission range for densely

populated cells

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Current work (2)

• Simulation of selected network strategies to analyze– Energy behaviour of nodes– Network lifetime– Balancing factors

• NS2 Energy model available Realistic simulation

Extracting Energy

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Current work (3)

• Lifetime extension via energy aware role changing– Simulation of one routing path – Assignment of roles: Clusterhead, Gateway, Aggregator, Sensor

• Lifetime extension by 40%

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Current work (4)

• Analysis of different cell shapes – Hexagonal, triangular

• Enlargement of cells to include more nodes

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Future work

• Robustness by altruism?• Adaption of changing environment parameters through

learning at runtime?• Improved network behavior by more specialized roles?

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• Generic OC principles adopted and optimized for sensor networks

• New energy balanced and coverage aware OC routing strategy developed

• Successfully implemented in Matlab simulation environment

• Strategies should be compared in NS2 regarding network‘s robustness and lifetime

Conclusion

Salzmann, J.; Kubisch, S.; Reichenbach, F.; Timmermann, D., Energy and Coverage Aware Routing Algorithm in Self Organized Sensor Networks, Fifth Annual IEEE International Conference on Pervasive Computing and Communications, New York, March 2007, (submitted)

Kubisch, S.; Hecht, R.; Salomon, R.; Timmermann, D., Intrinsic Flexibility and Robustness in Adaptive Systems: A Conceptual Framework, 2006 IEEE Mountain Workshop on Adaptive and Learning Systems (SMCals/06), Logan, Utah, U.S.A., July 2006

Reichenbach, F.; Bobek, A.; Hagen, P.; Timmermann, D.; Increasing Lifetime of Wireless Sensor Networks with Energy-Aware Role-Changing, Proceedings of the 2nd IEEE International Workshop on Self-Managed Networks, Systems & Services (Self Man 2006), Dublin, Ireland, June 2006

Publications