energy aware self organized communication in complex networks jakob salzmann, dirk timmermann spp...
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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
DFG 1183 Organic Computing 3
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