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Self-Organization inWireless Sensor Networks

Falko DresslerUniversity of Erlangen

dressler@informatik.uni-erlangen.de

2009-12-17 Falko DresslerUniversity of Erlangen

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

Self-Organization

Flocks of birdsSchool of fish

Sand dunes

Proliferating cells

Self-organizing autonomous systems?

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.

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

),(/),(/

∇+=∂∂

Reaction-diffusion system:

Self-Organizing Systems

Communication among the nodesLocal system

control

Simple local behavior

Basic techniques used by self-organizing systems

Positive and negative feedback

Interactions among individuals and with the environment

Probabilistic techniques

Positive and Negative Feedback

Indirect measurements possibleNo requirements on bi-directional communication channels

MeasurementNot OK?

Reaction!

Source

Outcome Effect!

Activation

Suppression

Delayed effects

Interactions

Information transfer between individualsDirect, i.e. using available communication channelsIndirect, i.e. via the environment (stigmergy)

Interactions with the environment

Direct interactionvia signals

Local workin progress

Indirect communicationvia the environment

Probabilistic Techniques

Simulation results

S. Camazine et al., Self-Organization in Biological Systems,Princeton University Press, 2003

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

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)

Outline

Conclusion

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?

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

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

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

Lab Work – Smart Home

direct accessfrom a robot

wireless accessfrom a robot

new sensors

multiple sensorsand actuators

Sensor Networks

ChallengesScalable controlled-load wireless communication

Wireless communication Shared medium with limited resources

e.g., 250 kbps shared among four nodes

sensornode

Sensor Networks

ChallengesScalable controlled-load wireless communication

Base station approach Increasing congestion towards the

base station

basestation

2x 3x 4x

2x e.g., 8 messages arriving at thebase station in one sample period

sensornode

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

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

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?

Self-Organization in Sensor Networks

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

Outline

Conclusion

cluster 1cluster 2

cluster 3

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

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

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

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

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)(

Some measurement results

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)

Some measurement results

Younis (2004)

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

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

EEdelay i

Remainingenergy

Utility ofnode i

Randomvalue

Number of neighbors

Round-trip delay

Outline

Conclusion

Source set Destination set

CONDITION

ACTION

D S⊆

Alarm Detection and Response

ObjectivesDistributed monitoring and alarm detection (and possibly also validation)(Quick) reaction on the collected information

ALARMACTION

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

RSN – Rule based sensor network

Message buffer

Sourceset

Workingset 1

Workingset 2

Workingset nΔt

Actionset

return

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, …

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]

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;

Source set Destination set

CONDITION

ACTION

D S⊆

Example: Gossiping + Aggregation

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;

ALARMACTION

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)

Results: End-to-end Delay

RSNtime until first copy arrives

RSNtime until any copy arrives

Centralizeddelay as observed by

the application

Results: Overhead

RSNnumber of duplicates

Centralizedratio of data to protocol

messages

Ongoing Work

Update of Δt according to localsuccess or failure

Co-stimulation by neighboring nodes

Feedback-basedtimeout management

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

Outline

Conclusion

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!

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.

self-organization in wireless sensor · pdf fileself-organization in wireless sensor networks...

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