bonsai in the fog an active learning lab with fog
play

Bonsai in the Fog: an Active Learning Lab with Fog Computing - PowerPoint PPT Presentation

Jan 02, 2023 •422 likes •756 views

Bonsai in the Fog: an Active Learning Lab with Fog Computing Antonio Brogi, Stefano Fort orti, Ahmad Ibrahim and Luca Rinaldi Service-oriented, Cloud and Fog Computing Research Group Department of Computer Science University of Pisa, Italy 3rd

  1. Bonsai in the Fog: an Active Learning Lab with Fog Computing Antonio Brogi, Stefano Fort orti, Ahmad Ibrahim and Luca Rinaldi Service-oriented, Cloud and Fog Computing Research Group Department of Computer Science University of Pisa, Italy 3rd IEEE International Conference on Fog and Mobile Edge Computing , Barcelona, 23 rd - 26 th April 2018.

  3. Deployment Models Cloud • Not sufficient per se to support the IoT mo momentu tum Internet alone. • There is a need for filt lterin ing and pr proc ocessin ing before the Cloud. M2M/ LAN/WAN • Processing should occur wherever it is best-pla laced ed for any given IoT application IoT+ T+Edge IoT+Clo loud • Low latencies, but • Huge computing power, but • Limited capabilities, • Mandatory connectivity, • Difficulties in sharing data • High latencies, • Bandwidth bottleneck.

  4. Deployment Models Cloud Internet Cloud Fog computing is a system-level horizontal architecture that distributes resources and services of computing, storage, control and M2M/ networking anywhere along the continuum from LAN/WAN Cloud to Things , thereby accelerating the velocity of decision making . Fog -centric architecture serves a specific subset of business problems that cannot be successfully Fog implemented using only traditional cloud based IoT+ T+Edge architectures or solely intelligent edge IoT+Clo loud devices . • Low latencies, but • Huge computing power, but • Limited capabilities, • Mandatory connectivity, [ OpenFog Reference Architecture , 2016.] • Difficulties in sharing data. • High latencies, • Bandwidth bottleneck.

  5. Fog Characteristics Context- & Low latency & Pervasiveness & location-awareness bandwidth savings geo-distribution Fog & Things Heterogeneity Mobility of devices

  6. Fog Computing in Education? • is calling for design of courses that include Fog computing in higher education programs. • We introduced Fog computing in our Advanced Software Engineering course ( 2 hours lec lectu ture and 2 hours activ tive learnin ing lab ab ).

  7. Our goals • Design an active learning lab that had to: First hand hands-on on expe xperience Qui Quick lear arning cur curve ve and Limi Limited cos osts and and act active lear arning two-hours time cro cross-platform

  8. Use Case

  9. Multi(functional)Lab BYOD Wi-fi Projector

  10. micro:bits • Programmable with an online editor either with blocks or in Ja JavaScri ript. • Cr Cross ss-platform rm. • Cost around €20 ☺ …

  11. * The ingredients

  12. Activity Plan Set-up of an IoT oT te testb tbed and Coding of a gate gateway mod module Ex Extensions to the gateway simple Edge application to stream/visualise data to module to perform more (IoT oT+Edge ) the Cloud (IoT+Cloud) computation (Fog og)

  13. Active Learning Discussion Tutorial Checkpoint Teamwork

  14. App Collector Collector Collector Radio serial radio Internet Collector Gateway Dashboard (serial) https://github.com/di-unipi-socc/bonsaifog

  15. Collector v1 • Measure mois oisture of one bonsai. • Plots the his histogram to the micro:bit LEDs. • When A pressed: shows mea easurement.

  16. IoT+Edge

  17. Collector v2 • Measure moisture of one bonsai. • Plots the histogram to the micro:bit LEDs. • When A pressed: shows measurement. • Streams data to the radio dashboard at the instructor’s laptop every second. • (Streams data to serial port at students ’ laptop.)

  18. Radio Dashboard • Every time a client connects, a LED is turned on. • Brightness of the LEDs depends on soil moisture of the associated bonsai. • Receives data from Collector clients. • Streams data to the serial port of the instructor’s laptop.

  19. Gateway v1 • Receives and parse data from serial. • Streams data to ThingSpeak.

  20. ThingSpeak • A Cloud service featuring MATLAB analytics and data visualisation for IoT data. • Playtime ☺

  22. Problem or opportunity?

  23. Where is the Fog?

  24. What can we make foggy ? 25

  25. Idea #1 Aggregate data and send an average every 10 seconds. 26

  26. Idea #2 Send data only if the difference between previous average is greater than a threshold. 27

  27. Idea #3 Send data only if the difference between previous average is greater than a threshold. Send it anyhow, if no data hasn’t been sent for 1 hour. 28

  28. Related Work • Literature focus either on - Iot ot+Edge e.g., (Shultz et al., 2015), Arduino, (Abraham, 2016), Raspberry Pi, IoT as a platform, (Wu and Zeng, 2016), simulated environments (Jang et al., 2017) - IoT oT+Cloud e.g., (Kortuem et al., 2013), IoT-based Cloud apps (Patil et al., 2016). Our goal was to showcase Fog computing and highlight the differences with respect to other deployment models for the IoT.

  29. Concluding Remarks ✓ ✓ ✓ First han hands-on on expe xperience and act active lear arning ✓ • Practically understand ✓ Fog computing. • Show differences with alternative deployment models.

  30. Concluding Remarks • Use of high-level language. • JavaScript everywhere. • Very good online docs. Qui Quick lear arning cur curve ve and two-hours time

  31. Concluding Remarks • Cost is around €30 euro per table. • All platforms supported. • We borrowed micro:bits from Limi Limited cos osts and cro cross-platform pisa.coderdojo.it

  32. Future Work Extend the testbed, increase Test scalability Perform quantitative heterogeneity of devices and and measure effec ectiveness measu easurements protocols. of the lab session. of bandwidth savings.

  33. Bonsai in the Fog: an Active Learning Lab with Fog Computing Antonio Brogi, Stefano Fort orti, Ahmad Ibrahim and Luca Rinaldi Service-oriented, Cloud and Fog Computing Research Group Department of Computer Science University of Pisa, Italy 3rd IEEE International Conference on Fog and Mobile Edge Computing , Barcelona, 23 rd - 26 th April 2018.

Recommend

Presentation of the BONSAI Board 2016 Prof. Bo Weidema, Chair Founder of the BONSAI initiative.

Presentation of the BONSAI Board 2016 Prof. Bo Weidema, Chair Founder of the BONSAI initiative.

Presentation of the BONSAI Board 2016 Prof. Bo Weidema, Chair Founder of the BONSAI initiative. Professor at Aalborg University, committed to trans-disciplinary research that bridges economic, social, engineering and environmental sciences. With

63 views • 3 slides
Agenda Intro to Active Learning Activity Design Resources for Active Learning Lunch with Active

Agenda Intro to Active Learning Activity Design Resources for Active Learning Lunch with Active

Agenda Intro to Active Learning Activity Design Resources for Active Learning Lunch with Active Learning Veterans Wrap Up Introduction to Active Learning What is active learning? Brainstorm your ideas with your group and generate a

855 views • 42 slides
Multi-Task Active Learning Yi Zhang Outline Active Learning Multi-Task Active Learning

Multi-Task Active Learning Yi Zhang Outline Active Learning Multi-Task Active Learning

Multi-Task Active Learning Yi Zhang Outline Active Learning Multi-Task Active Learning Linguistic Annotations (ACL 08) Image Classification (CVPR 08) Current Work and Discussions Constraint-Driven Active Learning

945 views • 47 slides
Bonsai: Balanced Lineage Authentication Ashish Gehani Bonsai:Balanced Lineage Authentication

Bonsai: Balanced Lineage Authentication Ashish Gehani Bonsai:Balanced Lineage Authentication

Bonsai: Balanced Lineage Authentication Ashish Gehani Bonsai:Balanced Lineage Authentication p. 1/19 What is data lineage ? Output Operation Input 1 Input n (a) Primitive operation (b) Compound operation tree Bonsai:Balanced Lineage

651 views • 19 slides
ACTIVE 1. WELCOME and JUMPING IN LEARNING 1. WHAT IS ACTIVE LEARNING? la Carte 1.

ACTIVE 1. WELCOME and JUMPING IN LEARNING 1. WHAT IS ACTIVE LEARNING? la Carte 1.

ACTIVE 1. WELCOME and JUMPING IN LEARNING 1. WHAT IS ACTIVE LEARNING? la Carte 1. CONSTRUCTING our TEACHING CHALLENGES in a low tech 1. ORCHESTRATION environment 1. SHOW-AND-TELL Selma Hamdani Psychology / DALC / Neuroscience

836 views • 50 slides
plants as bonsai, with a demonstration of potting up a Myrtle Beech ( Nothofagus cunninghamii )

plants as bonsai, with a demonstration of potting up a Myrtle Beech ( Nothofagus cunninghamii )

Aus Australian Na ralian Native ive Pla Plants s as B as Bonsa nsai The long tradition of the art of Bonsai can be traced back through Japan and China, especially over the past 200 years or so. The art now continues to develop and

73 views • 3 slides
Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability 2. Decision making 2. Application of knowledge 3. Explain/communicate 1. Deep learning Approach to Uni-directional 2. Interactive Learning

282 views • 5 slides
12.1 Active Learning: A Review When learning, it may be the case that getting the true labels of

12.1 Active Learning: A Review When learning, it may be the case that getting the true labels of

CS/CNS/EE 253: Advanced Topics in Machine Learning Topic: Active Learning Review and Kernel Methods Lecturer: Andreas Krause Scribe: Jonathan Krause Date: Feb. 17, 2010 12.1 Active Learning: A Review When learning, it may be the case that getting

213 views • 5 slides
Predator and Prey: Active Learning Is Social Learning Kenneth Ronkowitz Active Learning

Predator and Prey: Active Learning Is Social Learning Kenneth Ronkowitz Active Learning

Predator and Prey: Active Learning Is Social Learning Kenneth Ronkowitz Active Learning Symposium, Rutgers University, May 15, 2018 Typically part of the curriculum in biology, phrenology, biology, anatomy, craniometry, physiognomy, crime

484 views • 14 slides
Learning Loss for Active Learning Rymarczyk D., Zieliski B., Tabor J., Sadowski M., Titov M.

Learning Loss for Active Learning Rymarczyk D., Zieliski B., Tabor J., Sadowski M., Titov M.

Learning Loss for Active Learning Rymarczyk D., Zieliski B., Tabor J., Sadowski M., Titov M. Agenda 1. Active Learning introduction 2. Base methods in AL for Deep Learning 3. Learning Loss for Active Learning 4. Our ideas for Active

802 views • 43 slides
10.1 Active Learning Review What questions should be asked in order to collect best data?

10.1 Active Learning Review What questions should be asked in order to collect best data?

CS/CNS/EE 253: Advanced Topics in Machine Learning Topic: Active Learning 2: the myopic version-space shrinking strategy Lecturer: Daniel Golovin Scribe: Pete Trautman Date: 8 February 2010 10.1 Active Learning Review What questions should be

264 views • 4 slides
Active Learning: Rethinking Our Teaching to Promote Deeper Learning Facilitated by Ken Silvestri,

Active Learning: Rethinking Our Teaching to Promote Deeper Learning Facilitated by Ken Silvestri,

Active Learning: Rethinking Our Teaching to Promote Deeper Learning Facilitated by Ken Silvestri, CFE Instructional Designer Adapted for Institute of Technology By Menaka Abraham OVERVIEW Why Active Learning What is Active Learning

542 views • 18 slides
Active Learning for Regression: Active Learning for Regression: Algorithms and Applications

Active Learning for Regression: Active Learning for Regression: Algorithms and Applications

LAMDA group, Nanjing University Nov. 5, 2009. Active Learning for Regression: Active Learning for Regression: Algorithms and Applications Algorithms and Applications Masashi Sugiyama Tokyo Institute of Technology sugi@cs.titech.ac.jp

870 views • 64 slides
Using active learning pedagogy to enable the Presentation by Donna Whitehead teaching of

Using active learning pedagogy to enable the Presentation by Donna Whitehead teaching of

Using active learning pedagogy to enable the Presentation by Donna Whitehead teaching of enterprise and entrepreneurial skills Active learning Active learning methodology has become a preferred way to change the traditional teacher

146 views • 11 slides
Using R for Active Learning and Self-assessment in an e-Learning Environment Arnau Mir, Margaret

Using R for Active Learning and Self-assessment in an e-Learning Environment Arnau Mir, Margaret

Using R for Active Learning and Self-assessment in an e-Learning Environment Arnau Mir, Margaret Mir-Juli and Monica J. Ruiz-Mir University of the Balearic Islands Palma de Mallorca, SPAIN Using R for Active Learning and Self-assessment

740 views • 12 slides
Active Learning with Active Learning with Model Selection Neil Rubens Sugiyama Lab / Tokyo

Active Learning with Active Learning with Model Selection Neil Rubens Sugiyama Lab / Tokyo

Active Learning with Active Learning with Model Selection Neil Rubens Sugiyama Lab / Tokyo Institute of Technology Active Learning (NLP Motivation) NLP (common scenario) Large amounts of unlabeled data Large amounts of unlabeled

706 views • 36 slides
Learning J.J. (Jia-Jie) Zhu Boston College GAAL 2 Active learning classify 400 instances 30

Learning J.J. (Jia-Jie) Zhu Boston College GAAL 2 Active learning classify 400 instances 30

Generative Adversarial Active Learning J.J. (Jia-Jie) Zhu Boston College GAAL 2 Active learning classify 400 instances 30 randomly selected 30 selected using AL using logistic regression 70% accuracy 90% accuracy pool-based active

801 views • 12 slides
Course outline Online decision making 1. Statistical active learning 2. Combinatorial

Course outline Online decision making 1. Statistical active learning 2. Combinatorial

Active Learning and Optimized Information Gathering Lecture 3 Reinforcement Learning CS 101.2 Andreas Krause Announcements Homework 1: out tomorrow Due Thu Jan 22 Project Proposal due Tue Jan 27 (start soon!) Office hours Come to

573 views • 23 slides
Approximating Learning Curves for Active-Learning-Driven Annotation Katrin T omanek and Udo Hahn

Approximating Learning Curves for Active-Learning-Driven Annotation Katrin T omanek and Udo Hahn

Approximating Learning Curves for Active-Learning-Driven Annotation Katrin T omanek and Udo Hahn Jena University Language and Information Engineering (JULIE) Lab Agenda Introduction to Active Learning Stopping Conditions

511 views • 19 slides
Active Learning Community Open House Undergraduate Academic Affairs Undergraduate Academic

Active Learning Community Open House Undergraduate Academic Affairs Undergraduate Academic

Active Learning Community Active Learning Community Open House Undergraduate Academic Affairs Undergraduate Academic Affairs Active Learning Community Rutgers Learning Centers Digital Classroom Services Mary Emenike Dave Wyrtzen Director of

705 views • 24 slides
Developing Active Learners Workshop 1 Paul Ellis Head of Teaching & Learning Strategy Sri

Developing Active Learners Workshop 1 Paul Ellis Head of Teaching & Learning Strategy Sri

Developing Active Learners Workshop 1 Paul Ellis Head of Teaching & Learning Strategy Sri Lanka, December 2014 Active learning What do you understand by the term active learning? What is it not? Traditional vs.

165 views • 15 slides
Learning Algorithms for Active Learning Plan Background Matching Networks

Learning Algorithms for Active Learning Plan Background Matching Networks

Learning Algorithms for Active Learning Plan Background Matching Networks Active Learning Model Applications: Omniglot and MovieLens Critique and discussion Background: Matching Networks (Vinyals et al. 2016)

241 views • 20 slides
Presentation of the BONSAI Board 2018 Dr. Christopher Mutel, Chair As a student, consultant, and,

Presentation of the BONSAI Board 2018 Dr. Christopher Mutel, Chair As a student, consultant, and,

Presentation of the BONSAI Board 2018 Dr. Christopher Mutel, Chair As a student, consultant, and, since 2014, scientist at the Paul Scherrer Institute, I have used and researched life cycle assessment (LCA) for the last 10 years. My research

309 views • 3 slides
By Sara Stolbach Advanced CLT, Spring 2007 Definition In Active Learning the user is given

By Sara Stolbach Advanced CLT, Spring 2007 Definition In Active Learning the user is given

By Sara Stolbach Advanced CLT, Spring 2007 Definition In Active Learning the user is given unlabelled examples where it is possible to get any label but it can be costly. Pool-Based active learning is when the General Learning Model

218 views • 18 slides

More recommend