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“SDJS: Efficient Statistics in Wireless Networks”
Albert Krohn, Michael Beigl, Sabin Wendhack
TecO (Telecooperation Office)Institut für TelematikUniversität Karlsruhe
www.teco.edu
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS: research and application area
WSN (wireless sensor network) Battery powered Low computation capabilities
MANET (mobile ad hoc networks) Fast changing environment Devices frequently join and leave a group
BAN (body area network), PAN (personal area networks)
Sensors attached to people Many small devices
Ubiquitous and Pervasive Computing Settings with many devices (typically >100) Battery powered Mid computation capabilities
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS: Synchronous Distributed Jam Signalling
What is SDJS?
Method for ultra fast estimation of a parameter of a group of devices
Novel transmission scheme Extension of standard wireless ad hoc protocol Synchronous, parallel, superimposing jam signals Works infrastructure less For highly mobile settings with high number of
networked devices
Example for this talk: “How many devices are present in the cell?”
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
Related Work
Example:“How many devices are present in the cell?”
Budianu et al. 2003: Collect IDs from the Devices and do a Good-Turing estimation, can be done iteratively Targeted on large scale networks, not on speed Also probabilistic
Vogt 2002: For passive RFID Using a slotted aloha protocol, where tags randomly select a slot Adaptive frame size Time to estimate 200 nodes with 99% reliability > 3 sec. (assuming ISO 18000 RFID standard)
Normal “ping” on 802.11b: Around 5 seconds (best case) for 100 stations
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
Motivation Idea of SDJS
Example:“How many devices are present in the cell?”
Traditional: Ping & HELO, OLEHSlow, each node answers Packet implosion, collisionsHigh bandwidth necessary“deterministic”Generic functionality of data transport in the networkSame mechanisms for all information flow
Novel:Specific solution for collecting data of the same contextReduce redundant overheadReduce transported information to necessary minimumSDJS: include the physical layerUltra Fast and efficient: typ. 1000x faster Probabilistic, but adjustable accuracy/reliability (trade-off)
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – Activity Flow
Slotted (framed) Aloha Reduce Information to a
single jam signal Full distributed operation Hardware Requirements? Network Requirements? Collisions?
1. Station B starts SDJS2. Each node prepares its
transmission vector3. SDJS scheme is processed4. Each node has a reception
vector
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – The duck hunter problem
Estimation of the real number from a given number of signals (the reception vector)
Classical “Duck Hunter Problem” Solution: surjective mapping, partition theory
Group of hunters
How many hunterswere there?
Example:“How many devices are present in the cell?”
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – The Estimation 1
Duck hunter problem; analogon in SDJS:s Slotsk Devices sending one jam signal eacha received jam signals
=> P(a|k) Distribution
No a-priori information:Maximum Likelihood
kMLE=arg maxk P(a|k)
With a-priori information:Maximum a-posteriori
kMAP=arg maxk P(a|k) P(k)
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – The Estimation 2
How is estimation done in practice?
Start: count the number of received jam signals a
1. ML-Point estimation:Give an estimationFor k (MLE)
2. MAP-Confidence interval:Give an interval, [kmin,kmax] that contains the actual k with a given confidence (e.g. 90%)
In both cases: look-up table that can be prepared (no computation on nodes necessary)
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – Accuracy and Noise
Accuracy vs. Speed trade-off:accuracy depends on number of slots s
Noise:false positives and detection errors duringcarrier sense affect theestimation
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – The Implementation
TecO’s particle computer
Wireless sensor platform with 8Bit 20 Mhz processor
4kRAM, 4MBit Flash 125kbit/s wireless communication Customized ad hoc protocol
Find a partner <20ms Low power Low collisions
Development tools Over 1000 produced, large
developer community all over the world
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – The Experiment
Setting in an office with up to 50 particle computer
Impressive prove of concept:theory and real world setting are nearly identical
Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe
SDJS – Conclusion
SDJS is An extension to wireless radio protocols Efficient group communication for very specific tasks Probabilistic by nature
SDJS can Efficiently and fast estimate parameters (1000x faster) Achieve adjustable accuracy (speed – accuracy trade off)
Overall performance of SDJS depends severely on the underlying technology
“SDJS: Efficient Statistics in Wireless Networks”
Albert Krohn, Michael Beigl, Sabin Wendhack
TecO (Telecooperation Office)Institut für TelematikUniversität Karlsruhe
www.teco.edu
Thank you for your attention!