Predictability and Efficiency in Predictability and Efficiency in Wireless Sensor Networks Lothar Thiele Jan Beutel Federico Ferrari, Matthias Keller, Roman Lim, Andreas Meier, Clemens Moser, MustafaYuecel, Matthias Woehrle
P Predictability and efficiency di t bilit d ffi i influences the whole life cycle, and is integrated into all abstraction layers and is integrated into all abstraction layers.
Overview • Sensor Networks – A Critical Review • Predictability and Efficiency – Testing and Verification – Protocols – Energy Scavenging • PermaSense 3
Wireless Sensor Networks Visions Visions 1991 1991 1996 1996 1999 1999 2000 2000 2001 2001 2003 2003 2004 2004 Ubiquitous Directed Scale Free Vision Smart Dust Diffusion COTS Dust Networks Wireless PicoRadio Paintable Terminodes Overlay Computing Applications 2000 2001 2003 2004 Argo Military Surveillance James Reserve Shooter Localization Sensor Webs Duck Island ZebraNet 4
Argo – Global Ocean Observation Strategy • Global array of temperature/salinity profiling floats – Satellite data relay to data centers on shore – Operational since 2000 – Developed and maintained mainly by oceanographers 5
Wireless Sensor Networks Visions Visions 1991 1991 1996 1996 1999 1999 2000 2000 2001 2001 2003 2003 2004 2004 Ubiquitous Directed Scale Free Vision Smart Dust Diffusion COTS Dust Networks Wireless PicoRadio Paintable Terminodes Overlay Computing WSN Community Applications 2000 2001 2003 2004 Argo Military Surveillance James Reserve Shooter Localization Prototypes, Experiments and Research Demos Sensor Webs Duck Island ZebraNet 6
“Proof-of-Concept” Deployment Experience [Prabal Dutt ta, UC Berkel ey] [Gilman nn Tolle, UC B Berkeley] 7 [Koen L Langendoen, TU Delft] [Prabal Dut tta, UC Berke eley]
Promises • Sensor nodes are cheap, so we can have plenty of p, p y them. • Nodes may be cheap, but deployment and y p, p y maintenance is expensive. • Additional redundant nodes make the system fault • Additional redundant nodes make the system fault tolerant automatically. • More nodes make the system more fragile • More nodes make the system more fragile. 8
Overview • Sensor Networks – A Critical Review • Predictability and Efficiency – Testing and Verification – Protocols – Energy Scavenging • PermaSense 9
Today's WSN Design and Development Simulation � TOSSIM [Levis2003] TOSSIM � PowerTOSSIM [Shnayder2004] � Avrora [Titzer2005] Deployment Deployment Virtualization and Emulation � In-network reprogramming [Levis2004,Hui2004] ale � EmStar [Ganesan2004] EmStar [Ganesan2004] Sca � Calibration and Verification C lib ti d V ifi ti � BEE [Chang2003,Kuusilinna2003] [Szewczyk2004] � Trial-and-error [Mainwaring2004, Hemingway2004,Cerpa2001] Test Grids id � Dependence on infrastructure [Szewczyk2004] � moteLab [Werner-Allen2005] � Emstar arrays [Cerpa03/04] Emstar arrays [Cerpa03/04] � Kansei [Dutta2005] Figure abridged from D. Estrin/J. Elson Reality 10
DSN: Result Comparison and Research Questions Platform Runtime Test success Tossim .5 minutes 80% DSN Testbed 11 minutes 0% • How can we formally y specify tests (driver, monitor, evaluation)? • • How can we proof the How can we proof the correctness of a test result (synchronization, partially ordered ti ll d d events)? 11
Testbed Example: Deployment-Support Network (DSN) Deployment-Support Network l S k � Temporary, minimal invasive � Virtual connections to nodes Virtual connections to nodes Developer Developer � Reliable, wireless, scalable Access Target Sensor Network Network 12
Testbed Example: The Deployment-Support Network Testbed Functions � Remote reprogramming Remote reprogramming � Extraction of log data � Analysis � Regression testing R i t ti Target Sensor Network Network 13
DSN Architecture Details 14
DSN Example –Test Case Generation 15
DSN Example – Network Control 16
DSN Example - Sniffer Three-Layer concept • WSN application • Spy-nodes listening • DSN for collecting captured traffic captured traffic Evaluation Tool for further analysis l i 17
DSN Example - Sniffer 18
DSN: Physical Characterization Architecture • Emulating the Environment... – Temperature Cycle Testing (TCT) Temperature Cycle Testing (TCT) • ... and Resource Usage – Different Power Sources: Batteries, rechargeable cells, Different Power Sources: Batteries, rechargeable cells, solar, fixed DC power... 19
DSN: 4 Nodes – Long Term Power Profiling Details 20
DSN: Formal Conformance Testing of Power Traces 21
DSN: Formal Conformance Testing of Power Traces • Use of timed automata for conformance testing • Modular modeling of • Modular modeling of – Hardware – Software S ft – Testing Environment – Trace • Complexity reduction by p y y – noise removal – abstraction and interval arithmetic • Testing by reachability analysis of the composed model (UPPAAL) model (UPPAAL) 22
DSN: Formal Conformance Testing of Power Traces • Experiment – TMOTE Sky (MSP 430 TI CC2420) running TinyOS2 TMOTE Sky (MSP 430, TI CC2420) running TinyOS2 – Harvester application, LPL MAC protocol – Besides testing a correct run (complex trace) several Besides testing a correct run (complex trace), several errors have been introduced (missing wake-up, inject error in low power scheduler, wrong low power state of p g p MC, specification error). 23
DSN: Formal Conformance Testing of Power Traces H Hardware Model d M d l Trace Automaton 24
DSN: Formal Conformance Testing of Power Traces Software Model Verification Results 25
Conclusion • Deployment Support Network (DSN) – integrated into continuous integration tool chain including regression test – enables design, early deployment and testing of distributed embedded systems • code distribution • distributed unit test • application control • application control • distributed trace analysis • physical parameter testing 26
Overview • Sensor Networks – A Critical Review • Predictability and Efficiency – Testing and Verification – Protocols – Energy Scavenging • PermaSense 27
Motivation – Wireless fire-alarm system • Wiring a building is cumbersome/costly or not possible • Project collaboration with Project collaboration with – Large buildings requires scalable (multi-hop) solution 28
Requirements for a Fire-Alarm System 1. Event reporting 1 : – Alarm delay: ≤ 10 seconds (node to sink) Al d l 10 d ( d t i k) – Robust and reliable 2. Status monitoring 1 : – Node failure report: ≤ 5 minutes (at the sink) 3. Energy efficiency: gy y – Lifetime greater than 3 years (on 2xAA): < 1% duty cycle → Large deployments require multi hop solution → Large deployments require multi-hop solution 1 European Norm (EN) 54-25:2008-06, June 2008 European Norm (EN) 54 25:2008 06, June 2008 29
Link Analysis – Distance vs. Packet Reception Rate (PRR) Gray Area 30
Link Analysis – Time vs. PRR (Link Stability) 88 % 88 % 12 % 12 % 31
Dwarf Algorithm: Network Structure • Nodes are organized in rings – according to their distance to the nearest sink • The neighbors of node u are divided into parents N u − , peers N u 0 , and children N u + – depending on the ring they belong to Towards sink 32
Dwarf Algorithm: Node Observation • Exchange status messages A) Detect node and link failures May interfere with y B) K B) Keep wake-up schedules up to date k h d l d alarm forwarding • Ring based status information aggregation Ring based status information aggregation – From the outer rings to towards the sink 33
Dwarf Algorithm: Alarm Forwarding • Idea: Constrained flooding in combination with a delay- Idea: Constrained flooding in combination with a delay aware nodes selection strategy • Alarm is forwarded to k neighbors – Degree k: redundancy vs message complexity Degree k: redundancy vs. message complexity • Select forwarding nodes according their wake-up times and S l t f di d di th i k ti d relative positions – Reduce alarm notification time Reduce alarm notification time 34
Dwarf Algorithm: Performance Evaluation • 80 nodes based on real wired placement – provided by Siemens – 1-5 hops (3 on avg.) – 2 floors 2 fl Sink Sink • Verified by GloMoSim simulation Verified by GloMoSim simulation • Patented P t t d • Part of future product 35
Conclusion • Problem: Safety critical alarm reporting requires – Robustness and timeliness and energy efficiency and gy y monitoring • Protocol Solution: Dwarf – Constrained flooding with delay-aware node selection – Ring based monitoring g g • Integrated with reliable link estimation and initialization. 36
Overview • Sensor Networks – A Critical Review • Predictability and Efficiency – Testing and Verification – Protocols – Energy Scavenging • PermaSense 37
Motivation [Heliomote: Srivastava] [Prometheus: Culler] BTnode 38 Computer Engineering and Networks Laboratory TIK
Recommend
More recommend
