Controllable Cluster of FlatSats Use Case 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: ESG-Grid u%lizes 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 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 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 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. 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 Economy for Broad Appeal ESG Cloud CubeSat AXSEM Radio Board & PIC 24 Raspberry PI computer Smartphone & Free App Less than $ 200 ESG-Grid Computationally Augmented RLNC Rotation Progression CubeSat Ground Stations P 0 **** P 1 P k **** **** **** P m TS0 TS1 TS2 *** TSk CA: RLNC SDP ESG Cloud Communications Coordination Engine
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
8/3/15, 11:18 AMAcademy of Aerospace Quality | Academy of Aerospace Quality
Page 1 of 2http://aaq.auburn.edu/
search this site
Login
Home Curriculum Forums Lessons LearnedCase Studies References AAQ Team
Academy of Aerospace Quality
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.
How to use this SiteThe Academy of Aerospace Quality is built around a curriculum. You can see a diagram of this curriculum below. Theavailable curriculum items can be accessed from the "Curriculum" section of the main menu. When you click on one ofthese items, you will be taken to the module page for that topic. The module page lists all the materials that are relatedto that specific topic. For each topic, there is a training tutorial, quizzes and other related materials. Training tutorialsseek to introduce the visitor to the selected topic. They are structured in the form of a lesson that the visitor can readthrough in order to become familiar with the topic. They contain information, examples, figures and exercises that willguide the visitor through the learning process. Quizzes are intended to test the visitor's knowledge of the topic. They canbe accessed through the topic module page or throughout the tutorial. They are composed of multiple choice and true orfalse questions. Some training modules contain additional exercises that the user is asked to solve and, once finished,will display the correct results.
The main menu also contains links to other content such as discussion forums, lessons learned and case studies. Thereferences section of this site provides a list of useful references that are used throughout the AAQ website. Theseinclude relevant standards, a glossary and an acronym list. To learn more about the AAQ project and those involved,check out the AAQ Team section!
For more detailed instructions on how to use this website, visit our video tutorial section.
AAQ Curriculum
Read more
Read more
Read more
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 Use Case
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:ESG-Gridu%lizesComputa%onallyAugmentedRandomLinearNetworkCoding(RLNC)FountainCode,underReinforcedMachineLearningMethodology,QID
ToMaximizeInforma%onValueFlow
1 U Cube
10 1X10X10cm FlatSats
ejection vector
Rotation
Progression
CubeSat
Ground Stations
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.
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
Experimentation:,Vehicle,for,Increased,,Educational,Outreach
Economy for Broad Appeal
ESGCloud
CubeSat
AXSEM Radio Board & PIC 24
Raspberry PIcomputer
Smartphone & Free AppLess than $ 200
ESG-Grid
Computationally Augmented RLNC
Rotation
Progression
CubeSat
Ground Stations
Experimentation:,Efficient,Communications
P0 ****P1 Pk **** **** **** Pm
TS0 TS1 TS2 *** TSk
CA: RLNC SDP
ESG Cloud Communications
Coordination Engine
Related Documents
![[George Steiner and Robert Fagles] Homer a Collec(BookZZ.org)](https://static.cupdf.com/doc/110x72/56d6c0901a28ab30169ae98b/george-steiner-and-robert-fagles-homer-a-collecbookzzorg.jpg)
