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. 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

3. 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?”

4. 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

5. 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, 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  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)

6. 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 SDJS 2. Each node prepares its transmission vector 3. SDJS scheme is processed 4. Each node has a reception vector

7. 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 hunters were there? Example:“How many devices are present in the cell?”

8. Albert Krohn, Michael Beigl, Sabin Wendhack, TecO, Universität Karlsruhe SDJS – The Estimation 1  Duck hunter problem; analogon in SDJS: s Slots k Devices sending one jam signal each a received jam signals => P(a|k) Distribution  No a-priori information: Maximum Likelihood k MLE =arg max k P(a|k)  With a-priori information: Maximum a-posteriori k MAP =arg max k P(a|k) P(k)

9. 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 estimation For k (MLE) 2. MAP-Confidence interval: Give an interval, [k min,k max ] 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. 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 during carrier sense affect the estimation

11. 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

12. 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

13. 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

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