organic sensor networks - kitprojects.aifb.kit.edu/effalg/oc/...sensor_network.pdfe-clash –...
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Organic Sensor Networks
Jakob Salzmann, Ralf Behnke, Dirk Timmermann
SPP 1183 9th Colloquium Organic Computing
20.-21.09.2009, Augsburg
Institute of Applied Microelectronicsand Computer Engineering
University of Rostock
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DFG 1183 Organic Computing 2
Outline
• Project Overview
• Clustering Algorithms– Localization-based algorithms– Localization-free algorithms
• Routing Algorithms– Scale-free inspired routing– Anticipatory routing
• Conclusion
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DFG 1183 Organic Computing 3
Task:• Collect sensor data at
many locations
• Transmit collected data to sink
Scenarios:
Sensor Network
Network properties:
Node properties:
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DFG 1183 Organic Computing 4
Typical problems:
• Limited energy
• Missing global information
• Dynamic events impact network
structure
Project Overview
Overall goal: Increase lifetime and robustnessof sensor networks by using OC
Resulting properties:
• Centralized control infeasible
• Required self-organization
• Required energy awareness
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5
ProjectOverview
Erroneous nodedetectionErroneous nodedetection
ClusteringClustering
RoutingRouting
Deployed Sensor NetworkDeployed Sensor Network
• Establish connection tothe sink• Healing of failed links
• Hierarchic layer• Redundancy detection• Healing of failed nodes
• Reduced communication• Altruism of nodes withdefect senors
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DFG 1183 Organic Computing 6
Outline
• Project Overview
• Clustering Algorithms– Localization-based algorithms– Localization-free algorithms
• Routing Algorithms– Scale-free inspired routing – Anticipatory routing
• Conclusion
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Switched-off node
sor node Clusterhead
al XGAF cell
• Subdivision into square cells
• Dimension of cells guarantees
coverage of a cell by each of its
nodes
• Cluster emergence in each cell
• Possibility to save energy by
switching-off all nodes but one
per cell
GAF* [Sal07a]
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• Subdivision into square cells
• Reduced cell dimension
guarantees partial coverage of
adjacent cells
• Possibility to save energy by
switching-off half of all cells
• Clustering behavior like XGAF
• Simple self-healing algorithm
available
MASCLE* [Sal07d]
ched-off node
or node
terhead
Virtual 2-MASCLE cell
Switched-off cell
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MASCLE* [Sal09a]• Subdivision into square cells
• Further reduction of cell size leads to
increased covered area
• Possibility to save energy by
switching-off three quarter of all cells
• Clustering behavior like XGAF
• Complex energy aware
self-healing algorithm available
ed-off node
node
rhead
Virtual 4-MASCLE cell
Switched-off cell
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Evaluation Active nodes
DFG 1183 Organic Computing 10
Simulation environment:Area to observe: 1km² , working range: 56 m, 500 simulations Deployed Nodes: 10000
XGAF
2-MASCLE
4-MASCLE
All nodes active
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Evaluation Lifetime
DFG 1183 Organic Computing 11
Simulation environment:Area to observe: 1km² , working range: 56 m, 500 simulations Deployed Nodes: 10000
XGAF
2-MASCLE
4-MASCLE
All nodes active
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DFG 1183 Organic Computing 12
Outline
• Project Overview
• Clustering Algorithms– Localization-based algorithms– Localization-free algorithms
• Routing Algorithms– Scale-free inspired routing – Anticipatory routing
• Conclusion
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Free-GAF* [Sal08a]
*Localization Free - Geographic Adaptive Fidelity 13
• Cluster determination via broadcast
with reduced transmission range
• Detection of adjacent clusterhead
via broadcast
• Refinement of clusterhead choice
via broadcast of cluster members
• New clusterhead repeats procedure
• Cluster emergence in the whole
network
• Clustering behavior like XGAF
Switched-off node
Sensor node
Possible Clusterhead(High probability)
Clusterhead
Possible Clusterhead(Low probability)
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Free-CLASH* [Sal09c]
*Localization Free – Clustering with Approximation to Symmetric Hexagons 14
• Cluster emergence similar to
Free-GAF
• Start with 2 clusterheads
• Only nodes with two neighbour cluster
become possible clusterheads
• Approximation to (regular) hexagon
structure
• Improved coverage and regularity
• Clustering behavior like XGAF
Switched-off node
Sensor node
Possible Clusterhead
Clusterhead
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aluation tive nodesation environment:o observe: 0.1km² , working range: 56 m, 500 simulations
Better:Better:
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aluation hieved functionalityation environment:o observe: 0.1km² , working range: 56 m, 500 simulations
Better:Better:
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DFG 1183 Organic Computing 17
Outline
• Project Overview
• Clustering Algorithms– Localization-based algorithms– Localization-free algorithms
• Routing Algorithms– Scale-free inspired routing– Anticipatory routing
• Conclusion
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Scale-Free inspired Routing
18
• Network results from preferred connection
• High robustness againstrandom failures
Vizualisation of the internet [Barabási04]
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Scale-Free inspired Routing [Sal07a]
19
Sensor node
Sink
Clusterhead
Switched-off node
• Emerging of clusters with different lifetimes
• Routing approach:• Starting with the sink• After attending the network,
nodes connect with all unconnected nodes in range
• Establish a network with scale-free similar behavior
• Optimizations:• Range Reduction• Limited Connectivity• Wait and See
• Well populated clusters become hubs
Lowly populated Cluster
Well populated Cluster
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Evaluation Lifetime
DFG 1183 Organic Computing 20
• Lifetime balancing • Lifetime increase by 130%
Lifetime/ shortestlifetime of a onenode cell [%]
Average lifetimewithout energy-awareness
Average lifetimewith appliedoptimizations
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DFG 1183 Organic Computing 21
Outline
• Project Overview
• Clustering Algorithms– Localization-based algorithms– Localization-free algorithms
• Routing Algorithms– Scale-free inspired routing, – Anticipatory routing
• Conclusion
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rrent work – Anticipatory Routing [Sal09b]pletion of the 2. Period)
• After node loss via dynamic event, unconnected nodes
choose new routing path autonomously
• Afterwards, adjacent routing path structure changes
• Affected routing path is balanced
Advantages:
• Less probability of further failures (in the same region)
• Increased network lifetime
• Combination with Scale-free inspired routing
Challenges
F i l ti f ti l d
node
ode
d route
hed connection
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tline
roject Overview
lustering Algorithms– Localization-based algorithms– Localization-free algorithms
Routing Algorithms– Scale-free inspired routing, – Anticipatory routing
onclusion
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nclusion
uccessful development of self-organized energy-aware algorithms– Localization-based algortihms– Localization-free algorithms
gorithms base on each other– Extendable via cluster-based self-healing algorithms– Scale-free inspired routing utilizes emerged cluster structure
elf-Healing in three hierarchic layers– Intra-Cluster, Inter-Cluster, Routing layer
4 international and national publications (2 more planned)
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blications
l09c] Jakob Salzmann, Ralf Behnke, Jiaxi You, Dirk Timmermann:
e-CLASH – Improved Localization Free Clustering in Large Wireless Sensor Networkse International Workshop on Scalable Ad Hoc and Sensor Networks, St. Petersburg, Deutschland, Oktober 2009
l09b]Jakob Salzmann, Ralf Behnke, Dirk Timmermann:
ticipatory Route Change Algorithm for Robust Self-Organized Sensor NetworksInternational Forum Life Science Automation, Rostock, Deutschland, Oktober 2009
l09a] Jakob Salzmann, Ralf Behnke, Martin Gag, Dirk Timmermann:
MASCLE - Improved Coverage Aware Clustering with Self Healing Abilitiese International Symposium on Multidisciplinary Autonomous Networks and Systems (MANS 2009), pp. 537-543, ISBN: 978-0-
95-3737-5/09, Brisbane, Australien, Juli 2009
u09b] Jiaxi You, Dominik Lieckfeldt, Qi Han, Jakob Salzmann, Dirk Timmermann:
ok-ahead Geographic Routing for Sensor NetworksIEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS 2009), ISBN: 978-1-4244-
04-9, Galveston, Texas, USA, März 2009
h09] Ralf Behnke, Jakob Salzmann, Dominik Lieckfeldt, Frank Golatowski, Dirk Timmermann, Kerstin Thurow:
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blications
u09a] J. You, D. Lieckfeldt, J. Salzmann, D. Timmermann,
F&Co: Connectivity Aware Topology Management for Sensor Networks, 20th IEE International Symposium on Personal,
oor and Mobile Radio Communications, Tokyo, Japan, 2009
r09] Claas Cornelius, Frank Sill, Hagen Sämrow, Jakob Salzmann, Dirk Timmermann, Diógenes da Silva:
countering gate oxide breakdown with shadow transistors to increase reliabilityt Symposium on Integrated Circuits and Systems Design (SBCCI), S. 111-116, ISBN: 978-1-60558-231-3, Gramado, Brasilien,
ptember 2008
l08b] Jakob Salzmann, Ralf Behnke, Dirk Timmermann:
ocalization-Free Wireless Sensor Network Clustering Approach with Convergence to Uniformityernational Forum Life Science Automation, p. 81, ISBN: 978-3-938042-17-5, Rostock, Deutschland, September 2008
l08a] Jakob Salzmann, Ralf Behnke, Dirk Timmermann:
Self-Organized Localization-Free Clustering Approach for Redundancy Exploitation in Large Wireless Sensor NetworksJahrestagung, Workshop: Adaptive und organische Systeme, pp. 747-754, ISBN: 978-3-88579-228-4, München, Deutschland,
ptember 2008
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blications
l07c] Jakob Salzmann, Ralf Behnke, Dirk Timmermann:
alyse regelmäßiger Clusterformen in SensornetzwerkenSymposium Maritime Elektrotechnik, Elektronik und Informationstechnik, S. 375 - 380, Rostock, Deutschland, Oktober 2007
l07b] Jakob Salzmann, Ralf Behnke, Dirk Timmermann:
ographical Clustering with Coars-Grained LocalizationInternational Forum ''Life Science Automation'', ISBN: 978-3-938042-12-0, Landsdowne, Virginia, USA, Oktober 2007
l07a] Jakob Salzmann, Stephan Kubisch, Frank Reichenbach, Dirk Timmermann:
ergy and Coverage Aware Routing Algorithm in Self Organized Sensor Networksceedings of Fourth International Conference on Networked Sensing Systems, pp. 77-80, ISBN: 1-4244-1231-5, Braunschweig,
utschland, Juni 2007
i06] Frank Reichenbach, Andreas Bobek, Philipp Hagen, Dirk Timmermann:
reasing Lifetime of Wireless Sensor Networks with Energy-Aware Role-ChangingProceedings of the 2nd IEEE International Workshop on Self-Managed Networks, Systems & Services (SelfMan 2006), LNCS
96, pp. 157-170, ISBN: 978-3-540-34739-2, Dublin, Ireland, Juni 2006
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estions?
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ckup
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lf-Organized Role Changingei06]
Sink initiated role changing
• Sink polls each node periodically
• Sink assigns new roles to sensor nodes
Self-organized role changing
• If energy level of a node drops below a
certain value node informs its
clusterhead autonomously
• Clusterhead assigns new roles to
remaining energy rich nodes
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aluation
Self-organized role changingSink initiated role changing
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al Problems:
mited energy
ssing global information
namic events impact network
ucture
ntralized control infeasible
quired self-organization
quired energy awareness
ject Overview
Overall goal: Increase lifetime and robustness ofsensor networks by using OC
Phase 1:
Research of applicableOC principles for sensor networks
Research of self-organizedcommunication
Phase 2:
Utilization of OC principles foremergence of network structures
Utilization of OC principles toh dl d i t
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tlook (3. Project Phase)
ttacks on self-organized networks– Analyzing the efficiency of different attack scenarios– Develop threat which exploit the self-organizing behavior
elf-protecting sensor networks– Research of possibilities of attack detection– Energy-aware attack reaction
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tlook (3. Project Phase)
Organic adaptation to real world challenges– Realistic channel model
• Shadowing• Reflection• Fading
– Multiple medium access• Overhearing• Idle Listening• Unsynchronized clocks
– Range-based clustering ► Connectivity-based clustering– Utilization of mass of nodes
• Utilization of birthday paradoxon