Distributed, Decentralized Collaboration: New Ways of Information Collection and Coordination 01.12.2010 Photo by Kevin Uttig Tobias Zimmer [email protected]
This talk is about information collection and processing techniques that are researched in a field of computer science that is called “Ubiquitous Computing” Studied Computer Science in Karlsruhe. Hold a PhD in Engineering. Worked in the field of Ubiquitous and Pervasive Computing on Context Processing for over 6 years. This talk will be about 20 minutes complemented by a 10 minute Q&A session
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How to Get Information?
• Large number of nodes• Efficiently
Photo by pastaboy sleeps
Presenter
Presentation Notes
Topic centres around the question “How to get information from a large number of nodes in an efficient way?”. Ubiquitous Computing is researching computing and communication in environments with very specific characteristics. I will give a brief introduction. The methods researched in Ubiquitous Computing are potentially interesting for all environments that share characteristics with typical Ubiquitous Computing settings. This talk is meant to be creatively inspiring and perhaps trigger new ideas how to benefit form the presented ideas in the space domain. I will not go into the technical details of the method that I present, but invite you to approach me for a coffee-shop session if you are interested to learn more details.
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Agenda
• Introduction: Ubiquitous Computing
• Mapping Problem Domains
• SDJS: Synchronous Distributed Jam Signalling
• Hunting Ducks!
• Conclusion and Q&A
Photo by Vinoth Chandar
Presenter
Presentation Notes
Introduction: Ubiquitous Computing Mapping Problem Domains (parameter estimation) (dm-study) SDJS: Synchronous Distributed Jam Signalling Hunting Ducks! Conclusion and Q&A
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Problem Domain
• Many devices
• Sensing
• Communication
• Computing
Information Collection
Collaboration
Presenter
Presentation Notes
The Problem Domain addressed by this research is characterized by: The presents of many devices Devices are equipped with sensors or provide abstract information (e.g. value) Devices can communicate; mostly using RF networks Devices have computing power to (pre-)process information
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Constrains
• Computing Power
• Communication Bandwidth
• Amount of Information
• Response Time
Presenter
Presentation Notes
Limited Computing Power Limited Communication Bandwidth Huge amount of information to be aggregated from, processed by many devices (partly in parallel). Response Time: mainly real-time or near real-time applications depending on the collected data.
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Agenda
• Introduction: Ubiquitous Computing
• Mapping Problem Domains
• SDJS: Synchronous Distributed Jam Signalling
• Hunting Ducks!
• Conclusion and Q&A
Photo by Vinoth Chandar
Presenter
Presentation Notes
Introduction: Ubiquitous Computing Mapping Problem Domains SDJS: Synchronous Distributed Jam Signalling Hunting Ducks! Conclusion and Q&A
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Environmental Monitoring
• Many sensors / nodes• RF communication• Mash network
• Wireless Sensor Network (WSN)• Active RFID
Presenter
Presentation Notes
Many sensors / nodes RF communication (shared medium) Mash network (not necessarily fully mashed!) Device classes that are typically used in this domain: Wireless Sensor Network (WSN) Active RFID
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Parameter Estimation
• No Exact Data• Mean• Majority• Singletons• Distributions
Presenter
Presentation Notes
No Exact Data Mean Majority Singletons Distributions
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Examples
• Temperature Distribution• Radiation• Chemicals• Age• Sell-by Date • Value
Presenter
Presentation Notes
Concrete sensory data Temperature Distribution Radiation Chemicals Abstract information Age Sell-by Date (monetary) Value
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Application
• Study with dm-drogeriemarkt• Quick estimation• Distribution of sell-by dates• Active RFID
• Solution: SDJS
Presenter
Presentation Notes
Monitoring of perishable good in a retail store High effort for manual checking of shelfs (up to 16h per product group; max. for pharmacological products) Parameter: Sell-by date / Best before date Idea: Automate checking process and integrate it with the ordering process Active RFID-Tags send the information on the sell-by dates in parallel Questions: How are the dates distributed? How many goods are close to their sell-by dates?
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Agenda
• Introduction: Ubiquitous Computing
• Mapping Problem Domains
• SDJS: Synchronous Distributed Jam Signalling
• Hunting Ducks!
• Conclusion and Q&A
Photo by Vinoth Chandar
Presenter
Presentation Notes
Introduction: Ubiquitous Computing Mapping Problem Domains SDJS: Synchronous Distributed Jam Signalling Hunting Ducks! Conclusion and Q&A
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SDJS• Synchronous Distributed Jam Signalling
Presenter
Presentation Notes
SDJS realizes an OR-combination of the transmission vectors on the radio channel Scaling is constant in time independent from the number of nodes The trade-off is between accuracy and speed of the schema Comparable to hash functions: a higher number of buckets/slots decreases collision probability and increases accuracy How is the analysis of the received data done? Mathematical approach is derived form the “Duck Hunter Problem”
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Agenda
• Introduction: Ubiquitous Computing
• Mapping Problem Domains
• SDJS: Synchronous Distributed Jam Signalling
• Hunting Ducks!
• Conclusion and Q&A
Photo by Vinoth Chandar
Presenter
Presentation Notes
Ubiquitous Computing Mapping Problem Domains (parameter estimation) (dm-study) Hunting Ducks! Practical Application: SDJS (Real World Results) Conclusion and Q&A
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Duck Hunter Problem
• Flock of Ducks• Hunting Party• Perfect Aim• Random Target
Photo by pastaboy sleeps
Presenter
Presentation Notes
Some times also called “Birthday Problem” Classical: Number of Hunters is known Size of Flock is known How many Ducks die? Advanced: Size of Flock is known Number of dead Ducks is known How many Hunters fired?
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Mapping
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k Hunters Devices
s Ducks Slots
Shooting Jamming
a Killed Ducks Received Jam Signals
“Overkilled” Ducks
Collisions
Presenter
Presentation Notes
Overkilled Duck are ducks that got hit by more than one bullet. A given number of duck hunters k [number of devices] are waiting for a flock of ducks [number of SDJS slots s] to appear. They all are experienced hunters and therefore always kill a duck when they aim at it. They all have a just one bullet. Suddenly, the ducks appear and all hunters immediately aim at a random duck and shoot [transmit a jam signal]. They don’t have the time to discuss who of them will aim at which duck. Therefore, a number of ducks will be killed [received number of jam signals a]; some of them even by two or more hunters [collisions of jam signals]. Others (ducks) will be lucky and survive. The duck hunter problem discusses the probability that a certain number of ducks die. In SDJS, the question is slightly different: SDJS asks how many hunters have been there (Advanced Duck Hunter Problem). We can derive the closed form equation through a basic problem of partitions theory.
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Math!
• How many Hunters?• Surjective Mapping • Partition Theory• MLE and MAP
Photo by pastaboy sleeps
Presenter
Presentation Notes
MLE: Maximum likelihood estimation MAP: Maximum a posteriori estimation (Conditional) Probability of seeing a dead ducks under the condition that k hunters shot at the flock.
LOCOSTIX EU funded project: FP6 IST (CoBis) and Netherlands Smart Surroundings Initiative Partners: TU Karlsruhe (KIT), TU Chemnitz, Philips GmbH, SAP Research, dm-drogeriemarkt GmbH RFID-Tags: Printed Circuit Polymer Electronics
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Agenda
• Introduction: Ubiquitous Computing
• Mapping Problem Domains
• SDJS: Synchronous Distributed Jam Signalling
• Hunting Ducks!
• Conclusion and Q&A
Photo by Vinoth Chandar
Presenter
Presentation Notes
Introduction: Ubiquitous Computing Mapping Problem Domains SDJS: Synchronous Distributed Jam Signalling Hunting Ducks! Conclusion and Q&A
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Conclusion - Study
• dm’s potential savings:• 2.5 MM per shop and year• 2,024 shop (in 07/08)• ~ 400 MY
• Why is it so significant?
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Conclusion
• Non-exact: Estimation• Parallel data collection• 1000 times faster • Low demands• Low cost• Excellent scaling
150 low price temperature sensors for SDJS High precision sensors for “ground truth” Round-up: “How to get information from a large number of nodes in an efficient way?”. Overview of what characterizes Ubiquitous Computing Environments How this translates to other problem domains with similar features Innovative approach to the central question: SDJS Hope to have seeded some new inspiring ideas how to benefit form Ubiquitous Computing Research in the Space Domain. The slide-set contains some background info on Ubiquitous Computing and References to papers on SDJS. Now I like to invite you to ask your questions!
ReferencesAlbert Krohn, Tobias Zimmer, Michael Beigl, Christian Decker, Till RiedelSDJS: The Duck Hunter Problem in Wireless Sensor NetworksPERVASIVE 2006, Feb. 7-10, Dublin, Ireland
Albert Krohn, Tobias Zimmer, Michael Beigl, Christian DeckerCollaborative Sensing in a Retail Store Using Synchronous Distributed Jam Signalling3rd International Conference on Pervasive Computing 2005, Munich, Germany 8-13 May 2005
Albert Krohn, Michael Beigl, Sabin WendhackSDJS: Efficient Statistics in Wireless Networks12th IEEE International Conference on Network Protocols (ICNP) 2004, Berlin, Germany, October 5-8. 2004
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Ubiquitous Computing
“Ubiquitous Computing enhances computer use by making computers available throughout the physical environment, while making them effectively invisible to the user”
Mark Weiser(1952-1999)
Presenter
Presentation Notes
The name goes back to Mark Weiser who introduced it first in 1988. Mark Weiser was working in XeroxParc Palo Alto. In 1991 he published his well known corner stone article titled: “The Computer of the 21th Century” in the journal Scientific America. Weiser’s focus was on the shift of the usage paradigm for computers: Mainframe: one computer many users Personal Computer: one computer one user UbiComp: many computers one user In this article he described his vision of computers and computing devices to become ubiquitously present in the environment while at the same time disappearing more and more form the conscious perception of the people in these environments.
