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

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 RFID standard)  Normal “ping” on b: 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, 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)

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

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?”

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)

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)

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

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 Telematik Universität Karlsruhe Thank you for your attention!