information dissemination tuning - aaq

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Controllable Cluster of FlatSats Dr. Paul Darby, Assistant Prof. CAPE CubeSat Faculty Advisor, Electrical and Computer Engineering, University of Louisiana at Lafayette Lafayette, Louisiana 70504 – Research Area: Mobile Grid Computing Informa(onal Value : Highly Valued Informa(on (HVI) is that which is innova(ve, informa(ve, and provided under “just in (me” arrival, i.e. to the right loca(on(s) at just the right (me to be “ac(onable” and useful. The network design under QID focuses on the intelligent dissemina(on of informa(on in the background in support of the cyber-physical processes of the portable and/or mobile device cluster or swarm, termed as the “Mobile Grid.” Cyber-physical Collec(ve Ac(on : Not all informa(on is created equally. Its value is dependent upon the extent to which it can be used facilitate ac(onable decisions and the ensuing cyber-physical processes the decisions effect. These cyber- physical processes may include decisions at each device, or collec(vely at a number of devices (e.g. satellite, robots, mobile ground sta(ons) to sense, process, communicate, receive, actuate and/or to move. Ac(ons may be taken singularly or collabora(vely. Increasing Informa(onal Value : The value of informa(on, whether in packe(zed form or not, is affected by its informa(on content, size, accuracy, arrival (me (with respect to other events going on), its arrival loca(on(s), and whether or not it is informa(ve and ac(onable at the receiving device(s). The efficient dissemina(on of informa(on in a network has the poten(al to provide increased value from the perspec(ve of the mission process at hand. Demonstra(on Via Control of Disseminated Cyber-physical Checkpoints : Checkpoin(ng is more crucial in Mobile Grid (MoG) cyber-physical systems than in conven(onal grid compu(ng networks due to host mobility, dynamicity, less reliable wireless links, and the resultant frequent wireless disconnec(ons. In QID, the MoG takes on a new paradigm in that cyber-physical checkpoints are done as opposed to conven(onal checkpoints, allowing devices taking the place of the failed devices to intelligently con(nue the interrupted physical ac(on (i.e. sensing, movement, etc.) either singularly or collec(vely. For the checkpoint recovery process to work prac(cally, checkpointed data from each cyber-physical sub- process running on a given Mobile Host (MH), should be transmiTed to and replicated on other strategic MHs in the MoG, for safe storage and use if needed. So, effec(ve and highly robust dissemina(on of checkpointed data is crucial. Exploi(ng the wireless broadcast medium, Random Linear Network Coding (RLNC), has demonstrated significant gains over tradi(onal data rou(ng, but it alone may not be adequate for the MoG environment. QID, being Informa(on Value Aware, controls the behavior of the RLNC store and forward func(ons and adapts them based upon feedback from exchanged network metrics processed via machine learning techniques. RLNC’s LIstening-speaking Informa(on value flow Profile (LISP) can be modulated under QID to effect op(mal informa(on value flow when and where in the network it is needed in support Mission ac(ons. Tes(ng is underway via simula(on, and demonstra(on is intended for a FlatSat cluster working in conjunc(on with Dr. Darby’s patent pending ESG-Grid or virtual ground sta(on network. Ejec%on of 10 FlatSats Methodology: QID – Quiescent Intelligent Dissemination of Information in Mobile Grids (MoGs) Photo: CAPE 2 CubeSat Experiment Case: Computa%onally Augmented Random Linear Network Coding (RLNC) Fountain Code, under Reinforced Machine Learning Methodology, QID To Maximize Informa%on Value Flow 1 U Cube 10 1X10X10cm FlatSats ejection vector Rotation Progression CubeSat Ground Stations Information Program? Process? Middleware? Metaware? Information - What is it? Location Time Innovative / relativity Perception / reflexive Informative / able to infer Value Actionable / order vs. disorder - entropy, fusible Efficient / reproducibility Accuracy / uncertainty / self- correcting Securability / Security Availability / detectability Dissemination / Exchange Multiplicative Impact Shelf life Rate of Change Cost Storage Depreciation Energy / Resources Rate Latent defects / Testability CA-RLNC Packet Dissemination in the MoG Fn F3 F2/ D S F1 D Fn-1 Ps Ps Ps Ps Ps Ps Batch Completion Probability. 0 0.2 0.4 0.6 0.8 1 1.2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 P b Composite Probability, P C Probability of Complete Batch at one or more Forwarding Hosts n=2 n=4 n=8 Motivating Example P b = l n " # $ $ % & ' ' i h " # $ $ % & ' ' P c i (1 P c ) h i i = m h * + , , - . / / l = k n l j h " # $ $ % & ' ' P c j (1 P c ) h j j = 0 m 1 * + , , , - . / / / n l ρ j = γ ji p i i = 0 m 1 p 0 , p 1 , , p i , , p m 1 GF (2 8 ) h r m P c = P s × P w × P i Functional underpinnings! composite, success, listening window, innovative Best case analysis, yet we can still learn a great deal. RLNC Batch consists of m packets. Information Dissemination Tuning Consumer Function TX MoG Wireless Neighborhood Org Rate FWD Rate MAD BCU -- + Feedback Balance Control Batch Completion neighbor reports FIT TOP MAM Count Helper Function Mobile Host -- running QID QID - Quiescent Intelligent checkpoint Dissemination at a participating host QI Predictor controller Incoming Packets delay Local batch completion counts QID Supportive Subfunctions.. QID applies RL Techniques to control TOP and FIT TOP – Transmission Origination Probability FIT – Forwarding Information Threshold The Essence of QID QID System and Control (partitioned) State Spaces. QID’s RL QI-Predictor Controller Middleware. P ( A A j ) = C (m, j ) C (m, l ) l , for all l

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Page 1: Information Dissemination Tuning - AAQ

8/3/15, 11:18 AMAcademy of Aerospace Quality | Academy of Aerospace Quality

Page 1 of 2http://aaq.auburn.edu/

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The AAQ (Academy of Aerospace Quality) serves as an internet based forum for providing quality assurance training toacademics (or academic institutions) and commercial space service providers involved in aerospace research, technologydevelopment, and payload design and development. The AAQ curriculum includes modules for all aspects of qualityassurance necessary to ensure project success and provides a virtual community for networking and sharing of lessons-learned among like-minded scholars. Dr. Alice E. Smith and Dr. Jeffrey S. Smith are the principal investigators fromAuburn, and Brian Hughitt (NASA Headquarters), Craig McArthur (Marshall Space Flight Center), and Ken Crane (GlennResearch Center) direct the project from NASA.

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News

U.N. / SouthAfrica Symposiumon Basic SpaceTechnology

September 1-4, 2015

United NationsBasic SpaceTechnologyInitiative (BSTI)

Guidance onSpace ObjectRegistration andFrequencyManagement forSmall an

Small SatelliteConference

August 8-13, 2015

Controllable Cluster of FlatSats

Dr. Paul Darby, Assistant Prof. CAPE CubeSat Faculty Advisor,

Electrical and Computer Engineering, !University of Louisiana at Lafayette!

Lafayette, Louisiana 70504 – Research Area: Mobile Grid Computing!

Informa(onalValue:HighlyValuedInforma(on(HVI)isthatwhichisinnova(ve,informa(ve,andprovidedunder“justin(me”arrival, i.e.totherightloca(on(s)atjusttheright(metobe“ac(onable”anduseful.ThenetworkdesignunderQIDfocusesontheintelligentdissemina(onofinforma(oninthebackgroundinsupportofthecyber-physicalprocessesoftheportableand/ormobiledeviceclusterorswarm,termedasthe“MobileGrid.”Cyber-physicalCollec(veAc(on:Notallinforma(oniscreatedequally.Itsvalueisdependentupontheextenttowhichitcanbeused facilitateac(onabledecisionsand theensuing cyber-physicalprocesses thedecisionseffect. These cyber-physical processesmay include decisions at each device, or collec(vely at a number of devices (e.g. satellite, robots,mobile ground sta(ons) to sense, process, communicate, receive, actuate and/or to move. Ac(ons may be takensingularlyorcollabora(vely.Increasing Informa(onal Value: The value of informa(on, whether in packe(zed form or not, is affected by itsinforma(on content, size, accuracy, arrival (me (with respect to other events going on), its arrival loca(on(s), andwhetherornotitisinforma(veandac(onableatthereceivingdevice(s).Theefficientdissemina(onofinforma(oninanetworkhasthepoten(altoprovideincreasedvaluefromtheperspec(veofthemissionprocessathand.Demonstra(on Via Control of Disseminated Cyber-physical Checkpoints: Checkpoin(ng ismore crucial inMobile Grid(MoG) cyber-physical systems than in conven(onal grid compu(ng networks due to host mobility, dynamicity, lessreliablewirelesslinks,andtheresultantfrequentwirelessdisconnec(ons.InQID,theMoGtakesonanewparadigminthatcyber-physicalcheckpointsaredoneasopposedtoconven(onalcheckpoints,allowingdevicestakingtheplaceofthefaileddevicestointelligentlycon(nuetheinterruptedphysicalac(on(i.e.sensing,movement,etc.)eithersingularlyorcollec(vely.Forthecheckpointrecoveryprocesstoworkprac(cally,checkpointeddatafromeachcyber-physicalsub-process runningon a givenMobileHost (MH), shouldbe transmiTed to and replicatedonother strategicMHs in theMoG, forsafestorageanduse ifneeded.So,effec(veandhighlyrobustdissemina(onofcheckpointeddata iscrucial.Exploi(ng thewirelessbroadcastmedium,RandomLinearNetworkCoding (RLNC),hasdemonstrated significant gainsovertradi(onaldatarou(ng,butitalonemaynotbeadequatefortheMoGenvironment.QID,beingInforma(onValueAware, controls the behavior of the RLNC store and forward func(ons and adapts them based upon feedback fromexchangednetworkmetricsprocessedviamachinelearningtechniques.RLNC’sLIstening-speakingInforma(onvalueflowProfile(LISP)canbemodulatedunderQIDtoeffectop(malinforma(onvalueflowwhenandwhereinthenetworkitisneeded in support Mission ac(ons. Tes(ng is underway via simula(on, and demonstra(on is intended for a FlatSatclusterworkinginconjunc(onwithDr.Darby’spatentpendingESG-Gridorvirtualgroundsta(onnetwork.

Ejec%onof10FlatSats

Methodology: QID – Quiescent Intelligent Dissemination of Information in Mobile Grids (MoGs)

Photo:CAPE2CubeSatExperimentCase:Computa%onallyAugmentedRandomLinearNetworkCoding(RLNC)FountainCode,underReinforcedMachineLearningMethodology,QID

ToMaximizeInforma%onValueFlow

1 U Cube

10 1X10X10cm FlatSats

ejection vector

Rotation

Progression

CubeSat

Ground Stations

Information

Program? Process?

Middleware? Metaware?

Information -What is it?

Location TimeInnovative /

relativityPerception /

reflexive

Informative /able to infer

Value

Actionable / order vs. disorder - entropy,

fusible

Efficient / reproducibility

Accuracy / uncertainty / self-

correcting

Securability / Security

Availability / detectability

Dissemination / Exchange

Multiplicative

ImpactShelf life

Rate of Change

CostStorage Depreciation

Energy / Resources

Rate

Latent defects / Testability

CA-RLNC Packet Dissemination in the MoG

Fn

F3

F2/D

S

F1

D

Fn-1Ps

Ps

Ps

PsPs Ps

!

!!

Batch Completion Probability. !

0!

0.2!

0.4!

0.6!

0.8!

1!

1.2!

0.1! 0.2! 0.3! 0.4! 0.5! 0.6! 0.7! 0.8! 0.9!

P b#

Composite#Probability,#PC#

Probability#of#Complete#Batch#at#one#or#more#Forwarding#Hosts#

n!=!2!n!=!4!n!=!8!

Motivating Example

Pb =l

n"

# $ $ %

& ' '

i

h"

# $ $ %

& ' ' Pc

i (1− Pc )h−i

i=m

h

∑*

+

, ,

-

.

/ /

l=k

n

∑l

j

h"

# $ $

%

& ' ' Pc

j (1− Pc )h− j

j=0

m−1

∑*

+

, , ,

-

.

/ / /

n−l

ρ j = γ ji pii=0m−1∑

p0, p1,, pi,, pm−1

GF(28)

h ≥ r ≥m

Pc = Ps × Pw × Pi

Functional underpinnings!

composite, success, listening window, innovative

Best case analysis, yet we can still learn a great deal.

RLNC

Batch consists of m packets.

Information Dissemination Tuning

ConsumerFunction

TX

MoGWireless

Neighborhood

Org Rate

FWD Rate

MAD

BCU

--

+

Feedback

Balance Control

Batch Completion neighbor reports

FIT

TOP

MAMCount

HelperFunction

Mobile Host -- running QID

QID - Quiescent Intelligent checkpoint Dissemination at a participating host

QI Predictorcontroller

Incoming Packets delay

Local batch completion

counts

QID Supportive Subfunctions..

QID applies RL Techniques to control

TOP and FIT

TOP – Transmission Origination Probability

FIT – Forwarding Information Threshold

The Essence of QID

QID System and Control (partitioned) State Spaces.

QID’s RL QI-Predictor Controller Middleware.

P(A⇐ A j ) =C(m, j)

C(m,l)l∑

, for all l