Self-Organization in Autonomous Sensor/Actuator...

Self-Organization in Autonomous Sensor/Actuator

Networks

Falko DresslerAutonomic Networking GroupDept. of Computer Sciences

University of [email protected]

Bio-inspiredNetworkingBio-inspiredNetworking

GWU2005-11-08

Falko DresslerUniversity of Erlangen-Nuremberg, Germany

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Outline

Autonomic Networking GroupIntroductionResearch projects

Selected research issuesBio-inspired networking

Feedback-loopCongestion control

ProfilingRobot-assisted WSN

Data communication in WSN(semi-)reliable and authenticated communication in WSN

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Autonomic Networking Group

Dept. of Computer SciencesComputer Networks and Communication Systems

Autonomic Networking Group

Group membersFalko Dressler (group leader)Thomas Halva Labella (guest researcher)Isabel Dietrich (Ph.D. candidate)Gerhard Fuchs (Ph.D. candidate)… and numerous B.Sc. and M.Sc. candidates

Research objectivesAutonomous Sensor/Actuator NetworksBio-inspired NetworkingNetwork Monitoring and Security

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Autonomous Sensor/Actuator Networks

Sensor networksSensor motes: sensors + processing + wireless communication + batterySensor networks: ad hoc networks consisting of hundreds of sensor motesApplication: logistic, security, environmental observations, health, home automation, pervasive computingResearch issues: addressing, ad hoc routing, group communication, task allocation, coordination, self-organization, energy efficiency, time synchronization, coverage, localization, security, quality of service

Mobile robotsCooperating mobile autonomous systemsApplication: security, service, entertainmentResearch issues: localization, navigation, cooperation,coordination, tracking

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ROSES

RObot assisted SEnsornetworkS

Employment of robot systems forMaintenance in sensor networkProviding communication relays

Sensor assisted teams of robotsMore accurate localizationNavigationInfrastructure forenhancedcommunication

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ROSES

Research GoalsEnergy efficient operation, communication, and navigationSensor network assisted localization and navigation of the robotsUtilization of the robots as a communication relaybetween a sensor network and a global networkQuality of service aware communication in heterogeneous mobile networks with dynamic topologyOptimized task allocation and communication based on application and energy constraintsSecure communication and data management in mobile sensor networks

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BioNeting - Bio-inspired Networking

ConceptsMapping of mechanisms from cellular and molecular biology to networking architecturesStudy of large scale networksAnalyzing the internal functions of network components as well as their interactions in comparison with cellular systems and the associated intra- and inter-cellular signaling pathways

Research GoalsAnalysis of similarities of computer networks andcellular systemsDeduction of new concepts for behavior patterns of network nodesIncreasing the efficiency of individual subsystems


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Outline






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Self-Organization in Pervasive Environments

Identification of availableCommunication pathsNodesCapabilitiesResources

Handling of dataStorageAggregationDistribution

Without knowledge about topology, available nodes, their addresses, their location, …

S

SSSS

Requestfor sensor

data

Local information exchangeRelayed requests

S

SS

SS

SS

SSSS

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Biological Information Exchange

DNA

Signal(information)

Gene transcriptionresults in the formation of a specific cellular response to the signal

Receptor

DNA

Signal(information)

Gene transcriptionresults in the formation of a specific cellular response to the signal

Receptor

DNATissue 1

Tissue 2

DNA

DNA

DNA

DNA

DNA

Tissue 3

Blood

DNADNATissue 1

Tissue 2

DNADNA

DNADNA

DNADNA

DNADNA

DNADNA

Tissue 3

Blood

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Adaptation to Networking

Lessons to learn from biologyEfficient response to a requestShortening of information pathwaysDirecting of messages to an applicable destination

Application for efficient, i.e. optimizedTask allocationInformation exchangeGroup communication

Example: feedback loop mechanismdata dissemination and control loop in ad hoc sensor networks + self-initiated adaptive congestion controlSensor communication actuator

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Excurse: Current Solution

Occurrence of a critical eventTemperature dropsA sensor node detects the problemA request for solving the task is initiated

Start of the reactionThe request reaches an node which can solve the problemThe countermeasure is taken

2

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Excurse: Current Solution

Confirmation of the taskA confirmation message is sent back to the initiator of the requestFor this state information is accumulated in several nodes

Arrival of the responseThe confirmation message receives the initiatorThe transaction is finished

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Excurse: Regulation of Blood pressure

+

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Shifting the Paradigms

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request

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The smooth muscle cells, the kidney and the brain team up one “meta” nodeThis node knows the answer to the request

request

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No confirmation message is neededThe change of the environment indicates the successful initiation of the task

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Feedback Loop

Bio-inspired networking in generalComparing cellular structures with computer networks, we always find similar structuresSpecific signaling pathways are directly adaptable to information exchange in sensor networks (and to other networking issues)Self-organization of complex operations becomes possible using bio-inspired mechanisms

Feedback loop mechanismEmployment of out-of-band methods for task managementSuppression techniques for congestion-aware signalingReduction of control messagesUsage of asymmetric information pathwaysAdaptive congestion control

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Outline






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Data Dissemination in SN

Assumptions for typical sensor networksLarge number of participating nodesEach node transmits its measurement data with the same constant data rateDuring fast environmental changes ( emergencies), this data rate may show high bursts

Data disseminationAddress centric routing and data forwarding

due to scalability issues infrequently used in SNData-centric message forwarding, e.g. flooding, gossiping, probabilistic/weighted forwarding, diffusion techniques

usually lack congestion control mechanisms

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Congestion Control in SN

State of the artEnd-to-end – flow based, relying on window mechanisms or feedback informationPath-based – flow based, using congestion signaling along the data path towards the senderHop-by-hop – detection information is signaled to all neighboring nodes

All approaches are based on Internet technologyrequire unambiguous addressing informationrequire defined data paths between sender and receiver

Few of these requirements can be granted in typical wireless sensor networks

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Reasons for Congestion in SN

GW20°C

21°C

22°C

21°C

20°C

21°C

(A)

Congestion due to changes in the environment

Similar or even worse situations can be expected in heterogeneous network environments employing different sensors for multiple simultaneous tasks!

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Congestion Behavior

time

# m

essg

aes

lowhighlow+highlink capacity

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First Enhancements

Weighted probabilistic data dissemination1. assign importance I to each event2. calculate priority P(I) describing the distribution range3. for all neighboring nodes Nn and previously known remote

accessible nodes Nr, calculate an exponentially distributed weighting W(N)

4. forward message if W(N)<P(I)

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Locality Driven Congestion Control

Basic requirementsTo maintain control even if some links get temporarily saturatedTo give priority to important messagesTo prevent starvation of particular transmissions

AlgorithmBased on the number of successfully received messages N during the last time interval T

For each message Mupdate message counter N(M,T)

identify importance factor IMcalculate probability P(N,IM)if exponentialDist(P,T)=TRUE

forward message M

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Simulation Results

time

# m

essg

aes


time

# m

essg

aes


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Locality Driven Congestion Control

General requirements and solution spaceSelf-organizing and adaptive communication mechanisms are required for large-scale sensor networksStudies of biological mechanisms for self-organization such as the signaling pathways in cell and molecular biology provide high potentials

Locality driven congestion control provides the following features

Based on locally available information onlyhighly scalableno further control overhead / message overhead

Adaptive to changing network conditionsFeatures priority messagesNo starvation of less important transmissions

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Outline






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Profiling

MotivationConsider a stationary WSN consisting of possibly heterogeneous sensorsMultiple different applications exist to be executed by the sensorsSensors have different capabilities; different versions of the software might existDeployment of large-scale WSN is difficult, maintenance even more

GoalsMobile robots are used for maintenance of WSN

ConfigurationReconfiguration(Re-)programming

… and enhancing coverage and distribution of current applications

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Profiling

ProfilingA definition of profiles that characterize a software service, e.g. software modules, and such profiles that characterize environments, i.e. platforms on which services can be offered, e.g. sensor nodesA definition of profile matching rules defining how these platforms can be reconfigured with these services (in the sense of loadingnew software)

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Profiling – Behavior

a) The robot drives to the position where reconfiguration is necessary

b) It collects information about the environment, builds the context and explores its neighborhood

c) All sensor motes, which have received the exploration message, send their current profiles that contain information about the hardware and software of the node

d) The robot uses the information gathered in steps b) and c) to assign the roles of the sensor motes, optimized for the current goal

e) The robot re-programs selected sensor motes over the air

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Profiling – Algorithm

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Profile Descriptions

node {properties:address = 1;board = mica2;sensors = mts310;appl = LightMeasure;

}

application {properties:name = TempMeasure;modules = TempSensorM,

CalcM;requirements:board = mica2;

}

module {properties:name = TempSensorM;

requirements: sensor = mts310;

}

module {properties:name = TempSensorM;

requirements:sensor = mts101;

}

module {properties:name = CalcM;

}

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Simulation in OMNeT++

Analysis of alternate configurations and large-scale environments

Routing, media accessMobility models and strategiesCoordination, task allocation

Important measuresEnergy efficiency (lifetime)PerformanceAvailability

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Profiling – Conclusions

Location-dependent reconfiguration realized in a prototype

Resource-aware data format necessary and possible

Sensor motes do not have to provide functionality but to store an ID (profile) describing their current HW/SW configuration

Profile matching, code generation, and other resource-intensive tasks are provided by the mobile robot systems (behaving as local servers)

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Outline






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Mobile Ad Hoc Networks

CharacteristicsStrong resource limitations (storage, processing power)Error-prone and unreliable communication pathsQuick changes of the communications paths due to mobility

Even stronger limitations in wireless sensor networks (WSN) and WPANs

Necessary enhancementsReliable and semi-reliable communication servicesMechanisms to ensure data integrity and message authentication

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(Semi-)reliable Communications

Reliable communicationTypically based on ACK/NAK schemesUnacknowledged messages are retransmittedDifferent schemes to optimized this process have been studied

Stop-and-wait, sliding-windowTCP (RTO backoff, fast retransmit, …)

Semi-reliable communicationLost messages can be identifiedNo retransmission is initiated by the protocol

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Message Authentication

Required functionalityDetection whether a message was altered during transmissionIdentification of the sender of the message

Available algorithmsModification Detection Code (MDC)

Message Digest 5 (MD5)Secure Hash Algorithm 1 (SHA-1)

Message Authentication Code (MAC)MACs constructed from MDCsReasons for constructing MACs from MDCs

– Cryptographic hash functions generally execute faster than symmetric block ciphers

– There are no export restrictions to cryptographic hash functionsBasic idea: “mix” a secret key K with the input and compute an MDC

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Research Goals

Different mechanisms at different layers or achieved at different locations

Data integrityMessage authenticationReliability(Encryption)

Keeping in mind the resource limitations in WSN/WPAN

RAC

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RAC – Objectives

RAC: (semi-)reliable authenticated communication

Authentication(with key)

Data Integrity(without key)

Sem

i-rel

iabl

e tra

nsm

issi

on(w

ithou

t ret

rans

mis

sion

s)

Rel

iabl

e tra

nsm

issi

on(w

ith re

trans

mis

sion

s)

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RAC – Requirements

Scalabilitythe overhead due to the algorithm should be negligible (message count, message size, memory and processing requirements)

Flexibilitythe optional selection of needed functionality such as reliability vs. semi-reliability and data integrity check vs. full message authentication

Configurabilitythe option to adapt the parameters to the capabilities of the particular entities involved in the communication

Extensibilitythe possibility to implement new functionality such as data encryption to provide confidentiality

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RAC – Working Principle

The sender computes a MDC for every message andStored in a local databaseAppended to the message that is actually transmitted

The receiver used the MDC toVerifies the message integrity / authenticationAcknowledge received messages

Appl.m m

MD5

h+mh+m

Appl.m m

MD5

h hh‘+hhh

A BtACKtRET

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Algorithm Details – Sender

Message sending

for each message mto be sent

docalculate hm=h([K],m)get current time tmstore (tm,hm,[m]) in

databasetransmit (m,hm)

done

Periodically check for lost messages

get current time tcfor each (tm,hm,[m]) in

databasedo

if(tm+tRET>tc)do

retransmit m ornotice lost message

donedone

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Algorithm Details – Receiver

Message receiving

for each receivedmessage (m,hm)

doverify hm -> check

data integrity,messageauthentication

get current time tmstore (tm,hm) in

databasedone

Periodically send acknowledgments

get current time tcfor each (tm,hm) in

databasedo

if(tm+tACK>tc)do

acknowledge allhm in database

donedone

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RAC – Helper Functionality

Modification detection codeMD5, others can be employed as well

Key managementStatic, “ad hoc PKI” demanded for dynamic key management

Flow controlImplicitly defined by tRET and tACK, other groups study TCP-like flow control for ad hoc networks

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Evaluation Model

ParameterstRET – timeout for retransmission or notification of the applicationtACK – timeout for acknowledgmentsλs – data rateρs – loss ratioKs - Keymi – ith messageh([Ks],mi) – MDC of the ith message based on key Ks

λ1, ρ1

λ2, ρ2

tRET tACK

A B|mi, h([K1],mi)|

|mr, h([K2],mr)|

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Simulation Results

Analysis of the size of the retransmission buffer(tRET/tACK=10s/5s, λ=1, variable ρ)

0

2

4

6

8

10

12

0 50 100 150 200time [s]

retr

ansm

issi

on b

uffe

r ρ=0.01ρ=0.05ρ=0.1ρ=0.2

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Simulation Results

Analysis of the size of the retransmission buffer(λ=1, ρ=0.1, variable tRET/tACK)

0

5

10

15

20

0 50 100 150 200time [s]

retr

ansm

issi

on b

uffe

r rRET/tACK: 2s/1srRET/tACK: 5s/2srRET/tACK: 10s/5srRET/tACK: 20s/10s

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Simulation Results

Analysis of the size of the retransmission buffer(tRET/tACK=10s/5s, ρ=0.1, variable λ)

0

10

20

30

40

50

0 50 100 150 200time [s]

retr

ansm

issi

on b

uffe

r λ=1λ=2λ=5

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Simulation Results

Analysis of the overall loss ratio as recognized at the sender of the messages(tRET/tACK=10s/5s, λ=1, variable ρ)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 100 200 300 400 500 600time [s]

ratio

of s

ucce

ssfu

l tr

ansm

issi

ons

ρ=0.01ρ=0.05ρ=0.1ρ=0.2

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RAC – Conclusions

BackgroundAd hoc networks suffer from unreliable data pathsSecurity is a must in many application scenarios

RAC is a new communication method featuring(semi-)reliable data communicationData integrity / message authentication

Studied propertiesRAC is scalable even in WSN because it allows a more efficient utilization of available resources and leads to an improved quality of the global systemSimulation results proved the applicability of the proposed algorithm and allow an on-time adaptation of the individual parameters depending on the current characteristics of the communication pathwaysPrimary application is partial reliability, i.e. the application needs to be informed about loss ratio or about specific lost messages

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Selected Publications[1] F. Dressler and B. Krüger, "Cell biology as a key to computer networking," German Conference on Bioinformatics

2004 (GCB'04), Bielefeld, Germany, Abstract and Poster, October 2004. [2] F. Dressler, "Efficient and Scalable Communication in Autonomous Networking using Bio-inspired Mechanisms - An

Overview," Informatica - An International Journal of Computing and Informatics, vol. 29 (2), pp. 183-188, July 2005. [3] F. Dressler and G. Carle, "HISTORY - High Speed Network Monitoring and Analysis," Proceedings of 24th IEEE

Conference on Computer Communications (IEEE INFOCOM 2005), Miami, FL, USA, March 2005. [4] F. Dressler, B. Krüger, G. Fuchs, and R. German, "Self-Organization in Sensor Networks using Bio-Inspired

Mechanisms," Proceedings of 18th ACM/GI/ITG International Conference on Architecture of Computing Systems -System Aspects in Organic and Pervasive Computing (ARCS'05): Workshop Self-Organization and Emergence, Innsbruck, Austria, March 2005, pp. 139-144.

[5] F. Dressler, "Sensor-Based Localization-Assistance for Mobile Nodes," Proceedings of 4. GI/ITG KuVSFachgespräch Drahtlose Sensornetze, Zurich, Switzerland, March 2005, pp. 102-106.

[6] F. Dressler and G. Fuchs, "Energy-aware Operation and Task Allocation of Autonomous Robots," Proceedings of 5th IEEE International Workshop on Robot Motion and Control (IEEE RoMoCo'05), Dymaczewo, Poland, June 2005, pp. 163-168.

[7] F. Dressler, "Locality Driven Congestion Control in Self-Organizing Wireless Sensor Networks," Proceedings of 3rd International Conference on Pervasive Computing (Pervasive 2005): International Workshop on Software Architectures for Self-Organization, and Software Techniques for Embedded and Pervasive Systems (SASO+STEPS 2005), Munich, Germany, May 2005.

[8] F. Dressler, "Adaptive network monitoring for self-organizing network security mechanisms," Proceedings of IFIP International Conference on Telecommunication Systems, Modeling and Analysis 2005 (ICTSM2005), Dallas, TX, USA, November 2005.

[9] F. Dressler, "Reliable and Semi-reliable Communication with Authentication in Mobile Ad Hoc Networks," Proceedings of 2nd IEEE International Conference on Mobile Ad Hoc and Sensor Systems (IEEE MASS 2005): International Workshop on Wireless and Sensor Networks Security (WSNS'05), Washington, DC, USA, November 2005.

[10] F. Dressler and I. Dietrich, "Simulative Analysis of Adaptive Network Monitoring Methodologies for Attack Detection," Proceedings of IEEE EUROCON 2005, Belgrade, Serbia & Montenegro, November 2005. (accepted for publication)

[11] F. Dressler and H. Chaskar, "Security Architectures for Wired and Wireless Networks: Threats and Countermeasures," 1st IEEE/ACM International Conference on Communication System Software and Middleware (IEEE COMSWARE 2006), New Dehli, India, Tutorial, January 2006.

[12] B. Krüger and F. Dressler, "Molecular Processes as a Basis for Autonomous Networking," IPSI Transactions on Advances Research: Issues in Computer Science and Engineering, vol. 1 (1), pp. 43-50, January 2005.

Self-Organization in Autonomous Sensor/Actuator

Networks

Falko DresslerAutonomic Networking GroupDept. of Computer Sciences

University of [email protected]


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