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2009-12-17 Falko DresslerUniversity of Erlangen
2
Outline
Self-organization as a (new) control paradigmIntroduction, coordination techniques
Sensor networks – an overviewSensor (and actor) networks, challenges
Distributed coordinationClustering in sensor networks
Programming self-organized systemsNetwork-centric data management
Conclusion
C
C
C
CS1
S3
S4
S2
2009-12-17 Falko DresslerUniversity of Erlangen
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Self-Organization
Flocks of birdsSchool of fish
Sand dunes
Proliferating cells
Self-organizing autonomous systems?
2009-12-17 Falko DresslerUniversity of Erlangen
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Self-Organization
Property DescriptionNo central control There is no global control system or global
information available. Each subsystem must perform completely autonomous.
Emerging structures The global behavior or functioning of the system emerges in form of observable pattern or structures.
Resulting complexity Even if the individual subsystems can be simple as well as their basic rules, the resulting overall system becomes complex and often unpredictable.
High scalability There is no performance degradation if more subsystems are added to the system. The system should perform as requested regardless of the number of subsystems.
2009-12-17 Falko DresslerUniversity of Erlangen
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Self-Organization
Definition: Self-organization“Self-organization is a process in which structure and functionality (pattern) at the global level of a system emergessolely from numerous interactions among the lower-levelcomponents of a system without any external or centralized control. The systems’ components interact in a local context either by means of direct communication or environmental observations without reference to he global pattern.”
Belousov-Zhabotinskiy reaction (Photographs by J. Pipscher)
vDvueGtvuDvuFtu
v
u
2
2
),(/),(/
∇+=∂∂
∇+=∂∂
Reaction-diffusion system:
2009-12-17 Falko DresslerUniversity of Erlangen
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Self-Organizing Systems
CS3
CS5
CS1
CS4
CS2
Communication among the nodesLocal system
control
Simple local behavior
CS6
Basic techniques used by self-organizing systems
Positive and negative feedback
Interactions among individuals and with the environment
Probabilistic techniques
2009-12-17 Falko DresslerUniversity of Erlangen
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Positive and Negative Feedback
Indirect measurements possibleNo requirements on bi-directional communication channels
MeasurementNot OK?
Reaction!
Source
Outcome Effect!
Activation
Suppression
Delayed effects
2009-12-17 Falko DresslerUniversity of Erlangen
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Interactions
Information transfer between individualsDirect, i.e. using available communication channelsIndirect, i.e. via the environment (stigmergy)
Interactions with the environment
CSiC
S
CS
CS
Direct interactionvia signals
Local workin progress
Indirect communicationvia the environment
2009-12-17 Falko DresslerUniversity of Erlangen
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Probabilistic Techniques
Simulation results
S. Camazine et al., Self-Organization in Biological Systems,Princeton University Press, 2003
2009-12-17 Falko DresslerUniversity of Erlangen
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Limitations of Self-Organization
ControllabilityPredictability vs. scalability
Cross-mechanism interferenceComposition of multiple self-organizing mechanisms can lead to unforeseen effects
Software developmentNew software engineering approaches are needed
System testIncorporation of the unpredictable environment
centralizedcontrol
distributedsystems
self-organizedsystems
determinism
scalability
2009-12-17 Falko DresslerUniversity of Erlangen
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Consequences of Emergent Properties
Amplification effects and sensibility to noiseSmall changes may result in different system behavior
Example: growth rate of a population xn+1 = r xn (1 - xn)
2009-12-17 Falko DresslerUniversity of Erlangen
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Outline
Self-organization as a (new) control paradigmIntroduction, coordination techniques
Sensor networks – an overviewSensor (and actor) networks, challenges
Distributed coordinationClustering in sensor networks
Programming self-organized systemsNetwork-centric data management
Conclusion
2009-12-17 Falko DresslerUniversity of Erlangen
13
Sensor Networks
Wireless Sensor Network (WSN)Hundreds of networked sensor nodes, composed of sensors + processing/storage + wireless comm. + battery
Wildlife monitoring
Logistics
Facility management
Fire detection
Smart Dust?
2009-12-17 Falko DresslerUniversity of Erlangen
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Sensor Hardware
Microcontroller (CPU and memory)E.g., Atmel ATmega128, 16 MHz, 64 kByte RAM, 128 kByte flash
Radio transceiverE.g., Chipcon CC1000 (315/433/868/915 MHz), CC2400 (2.4 GHz)
Battery - possibly in combination with energy harvestingSensors - light, temperature, motion, …
Micro controller
Memory
Storage
Radio transceiver
Battery
Sensor 1
Sensor n
…
2009-12-17 Falko DresslerUniversity of Erlangen
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Sensor Networks
Wireless Sensor Network (WSN)Hundreds of networked sensor nodes, composed of sensors + processing/storage + wireless comm. + battery
Internetr
direct access
indirect accessvia GW
2009-12-17 Falko DresslerUniversity of Erlangen
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V. Kumar, et al., "Robot and Sensor Networks for First Responders," IEEE Pervasive Computing, vol. 3 (4), pp. 24-33, October-December 2004
First Responder Scenario
2009-12-17 Falko DresslerUniversity of Erlangen
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Lab Work – Smart Home
direct accessfrom a robot
wireless accessfrom a robot
new sensors
multiple sensorsand actuators
2009-12-17 Falko DresslerUniversity of Erlangen
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Sensor Networks
ChallengesScalable controlled-load wireless communication
Wireless communication Shared medium with limited resources
e.g., 250 kbps shared among four nodes
sensornode
2009-12-17 Falko DresslerUniversity of Erlangen
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Sensor Networks
ChallengesScalable controlled-load wireless communication
Base station approach Increasing congestion towards the
base station
basestation
1x
2x 3x 4x
5x6x
1x
2x e.g., 8 messages arriving at thebase station in one sample period
sensornode
2009-12-17 Falko DresslerUniversity of Erlangen
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Sensor and Actor Networks
ChallengesCompared to sensor networks, also the reaction time is essential (responsiveness)
basestation
sensornode
actornode Base station approach
Increased response time due tohigh numbers of hops
e.g., worst case sensor to base station: 5 hopsWorst case base station to actor: 3 hops up to 8 transmissions required
2009-12-17 Falko DresslerUniversity of Erlangen
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Research Aspects
Coordination of autonomous (sub-)systemsManagement and controlAd hoc routing and data disseminationSecurity and safetyQuality of service…
ConstraintsMobility of nodes – commonly it is believed that sensor networks being stationary, nowadays, mobility is a mayor concernSize of the network – much larger than in a infrastructure networksDensity of deployment – very high, application domain dependentEnergy constraints – much more stringent compared to fixed or cellular networks, in certain cases recharging is impossible
2009-12-17 Falko DresslerUniversity of Erlangen
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Coordination in Sensor Networks
ObjectivesScalability – Management overhead for coordination, support for “unlimited” number of nodesLifetime – Application dependent description of the service quality including delays and availability
Need for management and control of dynamic, highly scalable, and adaptive systems
Self-organization as a paradigm?
2009-12-17 Falko DresslerUniversity of Erlangen
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Self-Organization in Sensor Networks
S
S
S S
SS
S
AA
CS1
CS2 C
S3
CS4
Coordination layer Task allocation Resource management Data lookup and retrieval
Communication layer Wireless links Routing Topology control
Virtual Cord Protocol (VCP)
Lifetime definition
Real-time MAC Protocols
2009-12-17 Falko DresslerUniversity of Erlangen
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Outline
Self-organization as a (new) control paradigmIntroduction, coordination techniques
Sensor networks – an overviewSensor (and actor) networks, challenges
Distributed coordinationClustering in sensor networks
Programming self-organized systemsNetwork-centric data management
Conclusion
cluster 1cluster 2
cluster 3
2009-12-17 Falko DresslerUniversity of Erlangen
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Distributed Coordination
Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data
A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”
A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters
2009-12-17 Falko DresslerUniversity of Erlangen
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LEACH
LEACH: Low-Energy Adaptive Clustering Hierarchy
CapabilitiesSelf-organization – Self-organizing, adaptive clustering protocol that uses randomization to distribute the energy load evenly among the sensors in the network. All nodes organize themselves into local clusters, with one node acting as the local base station or cluster-headEnergy distribution – Includes randomized rotation of the high-energy cluster-head position such that it rotates among the various sensors in order to not drain the battery of a single sensorData aggregation – Performs local data fusion to “compress” the amount of data being sent from the clusters to the base station, further reducing energy dissipation and enhancing system lifetime
2009-12-17 Falko DresslerUniversity of Erlangen
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LEACH
PrinciplesSensors elect themselves to become cluster-heads at any given time with a certain probabilityThe clusterhead nodes broadcast their status to the other sensors in the networkEach sensor node determines to which cluster it wants to belong by choosing the cluster-head that requires the minimum communication energy
Clustering at time t1 Clustering at time t1 + d
cluster 1cluster 2
cluster 3
cluster 1cluster 2
cluster 3
2009-12-17 Falko DresslerUniversity of Erlangen
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LEACH
Algorithm detailsOperation of LEACH is broken into roundsCluster is initialized during the advertisement phaseConfiguration during the set-up phaseData transmission during the steady-state phase
Advertisement phase
Cluster set-upphase
Steady-state phase
Single round
2009-12-17 Falko DresslerUniversity of Erlangen
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LEACH
Advertisement phaseEach node decides whether or not to become a clusterhead for the current round
Based on the suggested percentage of clusterheads for the network (determined a priori), and the number of times the node has been a clusterhead so farThe decision is made by the node n choosing a random number between 0 and 1; if the number is less than a threshold T(n), the node becomes a cluster-head for the current roundThe threshold is set as:
where P is the desired percentage of clusterheads (e.g., P = 0.05), r is the current round, and G is the set of nodes that have not been clusterheads in the last 1/P rounds
Using this threshold, each node will be a clusterhead at some point within 1/P rounds; the algorithm is reset after 1/P rounds
∈
×−=
otherwise0
if1mod1)(
Gn
PrP
P
nT
2009-12-17 Falko DresslerUniversity of Erlangen
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LEACH
Some measurement results
2009-12-17 Falko DresslerUniversity of Erlangen
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HEED / X-LEACH
HEED – Hybrid Energy-Efficient Distributed Clustering
X-LEACH – Extended LEACH
Hybrid approach – clusterheads are probabilistically selected based on their residual energy
Similar to LEACH but incorporates the currently available remaining energy at each node for the (still probabilistic) self-election of clusterheadsCalculation of the probability CHprob to become clusterhead based on the initial amount of clusterheads Cprob among all n nodes and the estimated current residual energy in the node Eresidual and maximum energy Emax
CHprob = Cprob x (Eresidual / Emax)
2009-12-17 Falko DresslerUniversity of Erlangen
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HEED
Some measurement results
Younis (2004)
2009-12-17 Falko DresslerUniversity of Erlangen
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Span
Topology maintenance for energy efficient coordination
Based on localized coordination instead of random election schemes
ObjectivesSpan ensures that enough coordinators are elected to make sure that each node has a coordinator in its radio rangeThe coordinators are rotated to distribute workloadThe algorithm aims at minimization of the number of coordinators in order to increase network lifetimeSpan provides decentralized coordination relying on local state information
2009-12-17 Falko DresslerUniversity of Erlangen
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Span
Protocol mechanismsProactive neighborship management using HELLO messagesThen, each non-coordinator node will become a coordinator if it discovers that two of its neighbors cannot reach each other either directly or via one or more coordinators ensures connectivity but does not minimize the costs
Solution: optimized backoff delay
TNRNC
EEdelay i
i
i
m
r××
+
−+
−=
2
11
Remainingenergy
Utility ofnode i
Randomvalue
Number of neighbors
Round-trip delay
2009-12-17 Falko DresslerUniversity of Erlangen
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Outline
Self-organization as a (new) control paradigmIntroduction, coordination techniques
Sensor networks – an overviewSensor (and actor) networks, challenges
Distributed coordinationClustering in sensor networks
Programming self-organized systemsNetwork-centric data management
Conclusion
S
Source set Destination set
CONDITION
ACTION
D S⊆
2009-12-17 Falko DresslerUniversity of Erlangen
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Alarm Detection and Response
ObjectivesDistributed monitoring and alarm detection (and possibly also validation)(Quick) reaction on the collected information
S
S
S
S
SS
S
S
A
A
A
ALARMACTION
2009-12-17 Falko DresslerUniversity of Erlangen
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RSN – Rule-based Sensor Network
Key objectivesData-centric operation – each message carries all necessary information to allow this specific handling
Specific reaction on received data – a rule-based programming scheme is used to describe specific actions to be taken after the reception of particular information fragments
Simple local behavior control – we do not intend to control the overall system but focus on the operation of the individual node instead. We designed simple state machines that control each node whether sensor or actuator
2009-12-17 Falko DresslerUniversity of Erlangen
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RSN – Rule based sensor network
S
S
S
S
SS
S
S
A
A
A
Message buffer
Sourceset
Workingset 1
Workingset 2
Workingset nΔt
Actionset
return
drop
Incoming messages
modify
actuate
sendIncoming messagesare stored in a buffer
According to a pre-specified condition,messages are associated to a working set
Messages in the working sets aremodified, dropped, forwarded, …
2009-12-17 Falko DresslerUniversity of Erlangen
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Rule-based Data Processing
Message encodingM:={type, region, confidence, content}
Examples{temperatureC, [10,20], 0.6, 20}
A temperature of 20°C was measured at the coordinates [10,20]. The confidence is 0.6, thus, a low-quality sensor was employed
{pictureJPG, [10,30], 0.9, ”binary JPEG”}A picture was taken in format JPEG at the coordinates [10,30]
2009-12-17 Falko DresslerUniversity of Erlangen
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Rule-based Data Processing
Rule statementif CONDITION then
{ ACTION }
Example: Gossiping + Aggregation
if $hopCount >= 8 then{ !drop; }
if $hopCount < 4 then{ !sendAll; !drop; }
if :random > 0.5 then{ !drop; }
if :count >= 1 then{ !send($hopCount := @minimum of $hopCount,
$value := @average of $value); }!drop;
S
Source set Destination set
CONDITION
ACTION
D S⊆
2009-12-17 Falko DresslerUniversity of Erlangen
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Example: Gossiping + Aggregation
2009-12-17 Falko DresslerUniversity of Erlangen
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Alarm Reporting
Sensor nodes Actor nodes!recordAll; !recordAll;if $hopCount >= DIAMETER if $value > THRESHOLD then { then {
!drop; !actuate($type:=rsnActuatorLS,} $value:=@average of $value,if :random <= GOSSIP-PROB $priority:=2);then { !drop;
!sendAll; }!drop; !drop;
}!drop;
S
S
S
S
SS
S
S
A
A
A
ALARMACTION
2009-12-17 Falko DresslerUniversity of Erlangen
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RSN – Simulation Model
C++ library integrated into OMNeT++rsnManagement – the core, handles messages, processes rulesrsnDispatcher – message exchange with local sensors and actuatorsrsnRouting – presently, simple broadcast module (as routing issues can be handled in RSN)
Network setup100 sensor nodes4 actors
Centralized (base-station)Distributed (RSN)
2009-12-17 Falko DresslerUniversity of Erlangen
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Results: End-to-end Delay
RSNtime until first copy arrives
RSNtime until any copy arrives
Centralizeddelay as observed by
the application
2009-12-17 Falko DresslerUniversity of Erlangen
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Results: Overhead
RSNnumber of duplicates
Centralizedratio of data to protocol
messages
2009-12-17 Falko DresslerUniversity of Erlangen
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Ongoing Work
S
S
S
S
SS
S
S
A
A
A
Update of Δt according to localsuccess or failure
S
S
S
S
SS
S
S
A
A
A
Co-stimulation by neighboring nodes
Feedback-basedtimeout management
2009-12-17 Falko DresslerUniversity of Erlangen
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Rule-based Data Processing
AdvantagesSelf-organized operation without central controlReduced network utilizationAccelerated response, e.g. actuationAllowance for centralized ”helpers” and self-learning
Open issuesHandling of unknown messages
Drop vs. seamless forwardingDuration of message storage, i.e. artificial per-hop delay
Aggregation quality vs. real-time message processingRule generation / distribution
Diffuse / random distribution vs. global optimization
2009-12-17 Falko DresslerUniversity of Erlangen
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Outline
Self-organization as a (new) control paradigmIntroduction, coordination techniques
Sensor networks – an overviewSensor (and actor) networks, challenges
Distributed coordinationClustering in sensor networks
Programming self-organized systemsNetwork-centric data management
Conclusion
2009-12-17 Falko DresslerUniversity of Erlangen
49
Self-Organizing Systems
From Hype to Reality?
Did we get it? Not really…
… there are specific applications that already benefit from self-organization techniques, however, the questions “how to engineer” and “how to control” generic self-organizing systems are still open…
… but we are getting closer!
2009-12-17 Falko DresslerUniversity of Erlangen
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BibliographyI. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A Survey on Sensor Networks," IEEE Communications Magazine, vol. 40 (8), pp. 102-116, August 2002.I. F. Akyildiz and I. H. Kasimoglu, "Wireless Sensor and Actor Networks: Research Challenges," Elsevier Ad Hoc Network Journal, vol. 2, pp. 351-367, October 2004. S. Camazine, J.-L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraula, and E. Bonabeau, Self-Organization in Biological Systems. Princeton, Princeton University Press, 2003.D. Culler, D. Estrin, and M. B. Srivastava, "Overview of Sensor Networks," IEEE Computer, vol. 37 (8), pp. 41-49, August 2004. I. Dietrich and F. Dressler, "On the Lifetime of Wireless Sensor Networks," ACM Transactions on Sensor Networks (TOSN), vol. 5 (1), pp. 1-39, February 2009. F. Dressler, Self-Organization in Sensor and Actor Networks. Chichester, John Wiley & Sons, 2007. F. Dressler, "A Study of Self-Organization Mechanisms in Ad Hoc and Sensor Networks," Elsevier Computer Communications, vol. 31 (13), pp. 3018-3029, August 2008.M. Eigen and P. Schuster, The Hypercycle: A Principle of Natural Self Organization. Berlin, Springer, 1979.W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-Efficient Communication Protocol for Wireless MicrosensorNetworks," Proceedings of 33rd Hawaii International Conference on System Sciences, 2000S. A. Kauffman, The Origins of Order: Self-Organization and Selection in Evolution, Oxford University Press, 1993.H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks, Wiley, 2005.T. Melodia, D. Pompili, V. C. Gungor, and I. F. Akyildiz, "Communication and Coordination in Wireless Sensor and ActorNetworks," IEEE Transactions on Mobile Computing, vol. 6 (10), pp. 1116-1129, October 2007. C. Prehofer and C. Bettstetter, "Self-Organization in Communication Networks: Principles and Design Paradigms," IEEE Communications Magazine, vol. 43 (7), pp. 78-85, July 2005. J. Yick and B. Mukherjee and D. Ghosal, "Wireless sensor network survey," Elsevier Computer Networks, vol. 52 (12), pp. 2292–2330, August 2008.O. Younis and S. Fahmy, "HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad-hoc Sensor Networks," IEEE Transactions on Mobile Computing, vol. 3 (4), pp. 366-379, October-December 2004H. Zhang and J. C. Hou, "Maintaining Sensing Coverage and Connectivity in Large Sensor Networks," Wireless Ad Hoc andSensor Networks: An International Journal, vol. 1 (1-2), pp. 89-123, January 2005.