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“SDJS: Efficient Statistics in Wireless Networks”

“SDJS: Efficient Statistics in Wireless Networks”. Albert Krohn, Michael Beigl, Sabin Wendhack TecO (Telecooperation Office) Institut für Telematik Universität Karlsruhe www.teco.edu. SDJS : research and application area. WSN (wireless sensor network) Battery powered

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“SDJS: Efficient Statistics in Wireless Networks”

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  1. “SDJS: Efficient Statistics in Wireless Networks” Albert Krohn, Michael Beigl, Sabin Wendhack TecO (Telecooperation Office) Institut für Telematik Universität Karlsruhe www.teco.edu

  2. 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

  3. 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?”

  4. 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 scalenetworks, 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

  5. Motivation Idea of SDJS Example:“How many devices are present in the cell?” • Novel: • Specific solution for collecting data of the same context • Reduce redundant overhead • Reduce transported information to necessary minimum • SDJS: include the physical layer • Ultra Fast and efficient: typ. 1000x faster • Probabilistic, but adjustable accuracy/reliability (trade-off) • Traditional: • Ping & HELO, OLEH • Slow, each node answers • Packet implosion, collisions • High bandwidth necessary • “deterministic” • Generic functionality of data transport in the network • Same mechanisms for all information flow

  6. SDJS – Activity Flow • Slotted (framed) Aloha • Reduce Information to a single jam signal • Full distributed operation • Hardware Requirements? • Network Requirements? • Collisions? Station B starts SDJS Each node prepares its transmission vector SDJS scheme is processed Each node has a reception vector

  7. SDJS – The duck hunter problem Example:“How many devices are present in the cell?” • Estimation of the real number from a given number of signals (the reception vector) • Classical “Duck Hunter Problem” • Solution: surjective mapping, partition theory How many hunterswere there? Group of hunters

  8. 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)

  9. SDJS – The Estimation 2 • How is estimation done in practice? Start: count the number of received jam signals a • 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)

  10. SDJS – Accuracy and Noise • Accuracy vs. Speed trade-off:accuracydepends on number of slots s • Noise:false positives and detection errors duringcarrier sense affect theestimation

  11. 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, largedeveloper community all over the world

  12. 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

  13. 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

  14. “SDJS: Efficient Statistics in Wireless Networks” Thank you for your attention! Albert Krohn, Michael Beigl, Sabin Wendhack TecO (Telecooperation Office) Institut für Telematik Universität Karlsruhe www.teco.edu

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