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Sensor Networks ACOE 422 Adopted from IEEE Tutorial on Sensor Networks.

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Presentation on theme: "Sensor Networks ACOE 422 Adopted from IEEE Tutorial on Sensor Networks."— Presentation transcript:

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2 Sensor Networks ACOE 422 Adopted from IEEE Tutorial on Sensor Networks

3 Sensing Remote Sensing In-situ Sensing Networked Sensing

4 Remote Sensing As the term implies, sensors not co- located with phenomenon Generally, sensors detect electromagnetic radiation from target passive: spectroscopes or magnetometers, camera or TV active: laser distance finders, radar scanners Locations Near-surface (e.g. aerial photography), satellites [From

5 Remote Sensing Applications Remote Sensing Mapping Earth’s Physical Properties Prospecting for minerals and other resources Agriculture: Mapping Vegetation Urban Reconnaisance Planetary Exploration [From ]http://rst.gsfc.nasa.gov

6 Remote Sensing Analysis and Systems Analysis: mostly image-processing related spectral analysis image filtering classification (maximum likelihood) principal components analysis Systems GIS: Geographic Information Systems Flexibly combine various images [From ]http://erg.usgs.gov/

7 In-situ Sensing Sensors situated close to phenomena accurate, microscopic observations … but with limited range General uses in engineering applications Condition monitoring Performance tuning [From ]http://www.sensorland.com/

8 State of In-Situ Sensing System architecture One or a small cluster of dumb sensors … wired to a data acquisition unit … or to a device controller Very impressive device engineering Sensing accuracy Miniaturization Ruggedization Calibration [From ]http://www.sensorland.com/

9 Current Applications Agriculture Plant positioning Precision hoeing Automotive, highway systems Engine pressure and oxygen monitoring Suspension positioning of racing cars and motorcycles Road noise measurements Aviation Aircraft engine pressure Entertainment Rotational stability of Ferris wheels Acoustic adjustments in symphony halls Manufacturing processes Humidity in compressed air Tail-lift testing Fan pitch monitoring Calibrating bullet speeds Railways Braking control load distribution Traction control in slippery conditions Shipping Rudder positioning Space Rocket engine valve positioning Utilities Water distribution and storage

10 Industry Many, many sensor manufacturers Sensor Magazine’s buying guide lists 240 manufacturers of acceleration measurement sensors! Some names Agilent, Wilcoxon, Crossbow, GEMS, Penny and Giles, Delphi, Motorola, ScanTek, Bosch, National Instruments The applicability of sensors is vast … Companies often stratified by Industry segment (e.g. Delphi automotive) Application (e.g. vibration measurement, or pressure measurement) … and differentiate themselves by offering a wide range of products with different specifications or differing form factors

11 Standardization Fair amount of activity since the mid-1990s IEEE P1451.x Two thrusts: Sensor board to processor interfaces wired/wireless bus, point-to-point both for data access and sensor self- identification Object-oriented abstractions for sensor data and application interaction [From Plug-and-Play Sensors Sensors Magazine, December 02]

12 Sensing Remote Sensing In-situ Sensing Networked Sensing

13 Networked Sensing Enabler Small (coin, matchbox sized) nodes with Processor 8-bit processors to x86 class processors Memory Kbytes – Mbytes range Radio Kbps initially Battery powered Built-in sensors!

14 The Opportunity Large-scale fine-grain in-situ sensing and actuation 100s to 1000s of nodes 5m to 50m spacing Inherently collaborative … sensors cannot act alone because they have limited view Inherently distributed … since communication is energy-intensive (we’ll see this later) Embedded (In-Situ) Networked Sensing

15 Applications

16 Application Areas Seismic Structure Response Contaminant Transport Marine Microorganisms Ecosystems, Biocomplexity Structural Condition Assessment

17 Seismic Structure Response Interaction between ground motions and structure/foundation response not well understood. Current seismic networks not spatially dense enough to monitor structure deformation in response to ground motion. to sample wavefield without spatial aliasing.

18 A Wired Seismic Array

19 A Wireless Seismic Array Use motes for seismic data collection Small scale (10 or so) Opportunity: validate with existing wired infrastructure Experiments Factor building Four Seasons building

20 Condition Assessment Longer-term Challenges: Detection of damage (cracks) in structures Analysis of stress histories for damage prediction Applicable not just to buildings Bridges, aircraft

21 Contaminant Transport Industrial effluent dispersal can be enormously damaging to the environment marine contaminants groundwater contaminants Study of contaminant transport involves Understanding the physical (soil structure), chemical (interaction with and impact on nutrients), and biological (effect on plants and marine life) aspects of contaminants Modeling their transports Mature field! Fine-grain sensing can help Responsible Party contributions for cleanup of “Superfund” sites (source: U.S. EPA, 1996) Billions of dollars

22 Lab-Scale Experiments Use surrogates (e.g. heat transfer) to study contaminant transport Testbed Tank with heat source and embedded thermistors Measure and model heat flow [From CENS Annual Technical Report, 03]

23 Field-Level Experiments Nitrates in groundwater Application Wastewater used for irrigating alfalfa Wastewater has nitrates, nutrients for alfalfa Over-irrigation can lead to nitrates in ground-water Need monitoring system, wells can be expensive Pilot study of sensor network to monitoring nitrate levels

24 Marine Micro-organism Monitoring Algal Blooms (red, brown, green tides) impact Human life Industries (fisheries and tourism) Causes poorly understood, mostly because Measurement of these phenomena can be complex and time consuming Sensor networks can help Measure, predict, mitigate

25 Lab-Scale Experimentation Build a tank testbed in which to study the factors that affect micro-organism growth Actuation is a central part of this Can’t expect to deploy at density we need Mobile sensors can help sample at high frequency Initial study: thermocline detection 1m Tethered- robot sample collectors

26 Ecosystem Monitoring Remote sensing can enable global assessment of ecosystem But, ecosystem evolution is often decided by local variations Development of canopy, nesting patterns often decided by small local variations in temperature In-situ networked sensing can help us understand some of these processes

27 James Reserve Clustered architecture Weather-resistant housing design Sensors Light, temperature, pressure, humidity

28 Great Duck Island Study nesting behavior of Leach’s storm petrels Clustered architecture: backbone multihop sensor cluster Now running for several months Base-Remote Link Data Service Internet Client Data Browsing and Processing Transit Network Basestation Gateway Sensor Patch Patch Network Sensor Node

29 Challenges and Goals

30 Networked Sensing Challenges Energy is a design constraint Network lifetime now becomes a metric Interaction with the physical world A lot messier than we’ve been used to Autonomous deployment We’re not used to building systems that can self-deploy Single (or a small number) of users Need a different model

31 Communication is Expensive The Communication/Computation Tradeoff Received power drops off as the fourth power of distance 10 m: 5000 ops/transmitted bit 100 m: 50,000,000 ops/transmitted bit Gets networking and distributed systems researchers excited! At short transmission ranges, reception costs are significant Implications Avoid communication over long distances Cannot assume global knowledge, or centralized solutions Can leverage data processing/aggregation inside the network Can leverage data processing/aggregation inside the network

32 Communication is Expensive The Communication/Computation Tradeoff Received power drops off as the fourth power of distance 10 m: 5000 ops/transmitted bit 100 m: 50,000,000 ops/transmitted bit Gets networking and distributed systems researchers excited! At short transmission ranges, reception costs are significant Implications Avoid communication over long distances Cannot assume global knowledge, or centralized solutions Can leverage data processing/aggregation inside the network Can leverage data processing/aggregation inside the network

33 The Goal An infrastructure that can be used by many different sensing applications

34 Components of Infrastructure Processor PlatformsRadiosSensors Operating Systems LocalizationTime SynchronizationMedium AccessCalibration Collaborative Signal Processing Data-centric RoutingData-centric Storage Querying, Triggering Aggregation and Compression Collaborative Event Processing Monitoring Security

35 Tutorial Overview Discuss components bottom-up Present a networking and systems view In each topic Present relatively mature systems (to the extent they exist) first Then discuss systems research Finally, present some theoretical underpinnings

36 Hardware: Platforms, Radios and Sensors

37 Overview Berkeley motes Mantis Cerfcube Processors Atmel StrongARM X-Scale Radios Chipcon CC1000 Bluetooth Zigbee Sensors Platforms Mica-2 Stargate iMote

38 Processors Architecture CISC vs. RISC Von-Neumann vs. Harvard Most embedded processors/MCUs are RISC Harvard architecture! Speed Cache/Memory Power Dissipation

39 Atmel Atmega 128L Harvard 8-bit RISC Speed : 8MHz Memory: 128KB program memory 4KB SRAM 4KB EEPROM Power draw: run mode: 16.5mW sleep mode: < 60µW Microcontroller used in Mica2

40 Intel StrongARM SA1100 Harvard 32-bit RISC Speed: 206MHz Cache: 16KB instruction cache 8KB data cache Power draw: run mode : 800mW idle mode : 210mW sleep mode : 50µW Microprocessor used in iPAQ H3700

41 Intel XScale PXA-250 A successor to the StrongARM Harvard 32-bit RISC Speed : 200/300/400MHz Cache: 32KB instruction cache 32 KB data cache Power draw: run mode : 400mW idle mode : 160mW sleep mode : 50µW Microprocessor used in iPAQ H3900

42 Atmel ATMEGA128(L) StrongARM SA1100 Intel XScale Type MicroControllerMicroprocessor ArchitectureHarvard, 8-bit RISCHarvard 32-bit RISC Speed8MHz/16MHz206MHz200/300/400 MHz Cache/ Memory 128KB programming memory 4KB data memory 16KB instruction cache 8KB data cache 32KB instruction cache 32KB data cache Power Draw run mode: 16.5mW sleep mode: < 60µW run mode: 800 mW idle mode: 210 mW sleep mode: 50µW run mode: 400 mW idle mode: 160mW sleep mode: 50µW Example Platforms Mica2 moteCompaq iPAQ3700Compaq iPAQ3900 Processor Comparison

43 Radios Low power, short range a must Relevant criteria: Frequency Modulation scheme Encoding scheme Data rates Frequency diversity Power considerations Receive power Transmit power For short range communication, the two are comparable!

44 [From RFM TR1000 OOK/ASK 433/916 MHz Date rate up to 115.2Kbps Power draw: Rx: 3.8mA Tx:12mA No spread spectrum support Used in earlier platforms Now largely obsolete

45 [From Chipcon CC1000 FSK, up to 76.8 KBaud Frequency range 300 – 1000 MHz programmable frequency in 250Hz steps Encoding scheme: NRZ, Manchester Current Draw: programmable, min 5.3 mA, max 26.7 mA Used in Mica2

46 Bluetooth Modulation Gaussian Filtered FSK (GFSK) 2.4GHz (ISM) Diversity coding Frequency Hopping Spread Spectrum. Power consumption: Transmit: 150 mW Receive: 90 mW Gross data rate: 6-12 KBps Point-to-point and point-to-multipoint protocols. up to 8 devices per piconet

47 IEEE Carriers and Modulation 868 Mhz, 900 Mhz ISM, 2.5 Ghz ISM Different modulations at different frequencies Diversity coding Direct sequence MAC layer CSMA/CA Status Standards complete, radios expected soon Zigbee Alliance is pushing for this Home and industrial automation They have defined a simple topology construction/routing layer on top of this

48 Sensors We describe some of the sensors types commonly used in some of our applications Theory of operation Performance parameters

49 [From Sensors Magazine, “How to Select and Use the Right Temperature Sensor”] Temperature Sensor Operation Change in resistance induced by temperature change Semiconductor or metal Differential changes in resistance Thermocouples, thermopiles Parameters Temperature range Linearity Resolution (sensitivity)

50 Photo Sensors Operation Uses Photoconductive material  Resistance decreases with increase in light Parameters Peak Sensitivity Wavelength Illuminance Range Ambient Temperature

51 Accelerometer Operation Capacitive Piezoresistive Parameters Single Axis, 2-Axis, 3-Axis Acceleration range ( in g) Acceleration sensitivity ( in mV/g) Dynamic acceleration. (vibration), static acceleration. (gravity) Shock survival limit ( in g)

52 Displacement Sensors (LVDT) Operation Iron core between Primary and Secondary Coils Displacement causes voltage output Parameters Range Linearity Sensitivity

53 Humidity Sensors Operation Capacitive Polymer dielectric that absorbs or releases water proportional to humidity Change in capacitance measure of humidity Parameters Range (e.g. 10% to 90%) and Accuracy Sensitivity Ambient Temp. Range Response Time

54 Magnetic Field Sensor Operation Ferro-magnetic material (e.g. iron, nickel, cobalt) change shape and size when placed in a Magnetic Field Parameters Number of axes ( one, two, three) – direction of magnetic field Range (Gauss) Noise Sensitivity Linearity Sensitivity (V/Gauss)

55 Pressure Sensors Operation Pressure information is converted to displacement which is measured using a Disp. Sensor Parameters Range Temperature Sensitivity Accuracy, Resolution Response Time

56 Platforms Several platforms in the community Various combinations of the processors, radios and sensors discussed so far

57 MICA 2 Atmel processor Multi channel radio receiver: Chipcon CC1000 Light, Temperature, pressure, acceleration, acoustic, Magnetic sensors Wireless Reprogramming Software Platform TinyOS [http://www.xbow.com]

58 Intel Research Mote Strong Arm Processor 12 Mhz, 64kB SRAM, 512 kB FLASH Bluetooth for communication Digital sensor interface UART, JTAG Link Layer reliability and security Battery Life > 6 months with AA cells and 1% duty cycle Software platform TinyOS Abstraction layer for Bluetooth [From Intel Corp.]

59 Stargate High Processing Node (Gateway) 400Mhz Xscale processor, 64MB RAM, 32MB Flash 3.5'' x 2.5 '' Ethernet, UART, JTAG, USB via daughter card Connectors for sensor boards Standard Mica2 Connector Software Platform Linux [From Crossbow Inc.,

60 GNOME 16-bit MSP chip 12-bit ADC 60k Flash Solar panel for rechargeable battery Sensors for temperature, humidity Compass and GPS Bluetooth support, RF radio, Ethernet

61 Medusa MK-2 High Capability node Two Microcontrollers 8-bit RISC Atmega 128L 4MHz, 32k Flash, 4kb RAM, JTAG, UART 32-bit RISC, 40MHz, 1MB Flash, 136KB RAM, JTAG, UART, GPS They communicate via UART On Board Power Mgmt. And Tracking Unit

62 Medusa MK-2 Power consumption 200 mW (fully operational) RF Radio compatible with Mica motes Two accessory board for ultrasonic distance measurement Software Platform PALOS (power aware light weight OS) Event Driven Priority Support [http://nesl.ee.ucla.edu]

63 MANTIS Hardware: The Nymph Chipcon CC bit ADC GPS Support UART and JTAG interface 3.5 x 5.5 sq. cm Support for up to 8 batteries [http://mantis.cs.colorado.edu]

64 MANTIS MOS Unix like development and runtime environment Multithreaded with priority support. But Round-Robin (not event driven) New H/w addition via a new H/w driver Remote login and reprogramming via wired and wireless [http://mantis.cs.colorado.edu]

65 Components of Infrastructure Processor PlatformsRadiosSensors Operating Systems LocalizationTime SynchronizationMedium AccessCalibration Collaborative Signal Processing Data-centric RoutingData-centric Storage Querying, Triggering Aggregation and Compression Collaborative Event Processing Monitoring

66 Operating Systems Depending the platform, various choices Tiny OS [Hill et al. 2000] Embedded Linux  -OS [Shih et al. 2001] We focus on Tiny OS Most different from the *nix variants

67 TinyOS De-facto sensor programming platform Initially developed by UC-Berkeley History v0.5.1 – released at 10/19/2001 v0.6.0 – released at 02/13/2002 v0.6.1 – released at 05/10/2002 v1.0.0 – released at 10/14/2002 v1.1.0 – released at 09/23/2003

68 H/W Platforms using TinyOS Crossbow MicaCrossbow Mica2Intel Mote CPU ATmega128LATmega128L(4MHz)ARM core(12MHz) Program Memory 128k Flash memory 512k Flash memory Data Memory 4k SRAM 64k SRAM Radio RFM TR1000ChipCon CC1000Bluetooth Data Rate 40 kbps76.8 kBaud1 Mbps Frequency MHz315/433/868/915M Hz 2.4GHz

69 TinyOS Programming Model Event-driven execution no polling, no blocking Concurrency intensive operation multi-threads based, no long-running thread Component-based program := layering of components No dynamic memory allocation program analysis and code optimization[Gay03] easy migration from software to hardware

70 TinyOS Architecture A tiny scheduler + a graph of components 2-level tiny scheduler task : run to completion (FIFO scheduling) event: immediately performed, preempt task Component Task Frame Command handler Event handler

71 TinyOS-nesC System programming language [Gay03] To support TinyOS programming model Component specification provides and uses interfaces interface contains commands and events Component implementation Module : provide code and implement interfaces Configuration : connect uses-interface of component to provides- interface of other component Support for concurrency

72 Component Specification

73 Component Implementation CodeWiring

74 A TinyOS application: DIM

75 Common System Components AM (Active Message) Messaging layer implementation that for packet de-muxing RadioCRCPacket Provides simple radio abstraction Send/receive packets over radio

76 Common System Components UARTFramedPacket provides serial communication to Host PC 19.2kbps(Mica/Mica2Dot), 57.6Kbps(mica2) Timer provides periodic and one-shot timers ADC abstraction of the analog-to-digital converter used by sensing components Temperature, light sensor

77 Components of Infrastructure Processor PlatformsRadiosSensors Operating Systems LocalizationTime SynchronizationMedium AccessCalibration Collaborative Signal Processing Data-centric RoutingData-centric Storage Querying, Triggering Aggregation and Compression Collaborative Event Processing Monitoring

78 Components of Infrastructure Processor PlatformsRadiosSensors Operating Systems LocalizationTime SynchronizationMedium AccessCalibration Collaborative Signal Processing Data-centric RoutingData-centric Storage Querying, Triggering Aggregation and Compression Collaborative Event Processing Monitoring

79 MAC Layer Issues Energy-efficient MAC layers Topology control for higher energy-efficiency MAC and radio layer performance

80 Medium Access Control Important design considerations Collision avoidance Energy efficiency Scalability in node density Latency Fairness Throughput Bandwidth utilization Reduce idle listening, collisions, control overhead, overhearing

81 MAC Design in TinyOS CSMA/Collision Avoidance Optional MAC layer acknowledgement (Mica) Hill et al Synchronization

82 Sensor-MAC (S-MAC) Tradeoffs Higher latency, less fairness Higher energy efficiency Major components in S-MAC Periodic listen and sleep Collision avoidance Overhearing avoidance Message passing Combine TDMA and contention-based protocols Ye et al., Infocom2002  Latency Fairness Energy

83 Collision Avoidance Solution: Similar to IEEE ad hoc mode (DCF) Physical and virtual carrier sense Randomized backoff time RTS/CTS for hidden terminal problem RTS/CTS/DATA/ACK sequence Overhearing avoidance Reserve channel for duration of entire message (rather than a fragment) … so that others can aggressively sleep to avoid overhearing

84 Periodic Listen and Sleep Reduce long idle time Reduce duty cycle to ~ 10% (120ms on/1.2s off) Longer time-slots than TDM, looser synchronization requirements Schedule can differ Preferable if neighboring nodes have same schedule easy broadcast & low control overhead Node 1 sleep listen sleep Node 2 sleep listen sleep

85 Schedule 2 Coordinated Sleep Nodes coordinate on sleep schedules Nodes periodically broadcast schedules New node tries to follow an existing schedule Nodes on border of two schedules follow both Periodic neighbor discovery and synchronization Early part of listen interval devoted to this  Schedule 1 1 2

86 Implementation on Testbed Platform Mica/Mica2 Motes TinyOS Used as NIC for x86/xscale embedded Linux box Configurable S-MAC options Low duty cycle with adaptive listen Low duty cycle without adaptive listen Fully active mode (no periodic sleeping)

87 S-MAC Performance Two-hop network at different traffic loads S-MAC consumes much less energy than like protocol w/o sleeping At heavy load, overhearing avoidance is the major factor in energy savings At light load, periodic sleeping plays the key role Source 1 Source 2 Sink 1 Sink 2

88 Adaptive Topology Control Can we put nodes to sleep for long periods of time? More aggressively than S-MAC Leverage redundant deployments Topology adapts to Application activities Environmental changes Node density Extend system lifetime Reduce traffic collision Complementary to topology control schemes that adjust transmit power levels

89 Example: ASCENT The nodes can be in active or passive state Active nodes forward data packets (using a routing mechanism that runs on the topology). Passive nodes do not forward packets but might sleep or collect network measurements. Each node joins the network topology or sleeps according to the number of neighbors and packet loss as measured locally.

90 ASCENT State Transitions Test Passive Sleep Active after Tt after Tp after Ts neighbors < NT and loss > LT loss < LT & help neighbors > NT (high ID for ties); or loss > loss T 0 NT: neighbor threshold LT: loss threshold T?: state timer values (p: passive, s: sleep, t: test)

91 Topology Control Schemes Empirical adaptation: Each node adapts based on measured operating region. ASCENT (Cerpa et al. 2002) Routing/Geographic topology based: Redundant links are removed. SPAN (Chen et al. 2001), GAF (Xu et al. 2001) Cluster based: Workload is shared within clusters CEC (Xu et al. 2002) Data/traffic driven: Nodes starts on demand using paging channel STEM (Tsiatsis et al. 2002)

92 Understanding Radio Vagaries Notoriously unpredictable Variable environment noise Device calibration Non-linear signal strength decay Multi-path effect Transmission collision Additional constraints for sensor networks Energy efficiency (Low power radio) Possibly high density deployment High packet loss, Asymmetry, High temporal variance Zhao et al. Impact on systems design Hardware/Physical Layer Modulation Scheme Base-band Frequency Encoding Scheme MAC Protocol Reliable Data Delivery Path Selection in Routing Congestion Control “Soft-state” Maintenance

93 Spatial Profile of Packet Delivery Node positions 4B6B Encoding High Tx Power In-door 2hrs (7200 pkts) “Gray Area” is evident in the communication range

94 Grey Area in Packet Loss Relatively large region of poor connectivity Across a wide variety of environments Spanning as large as 30% of the effective transmission range In-door Out-door Unobstructed

95 4B6B Encoding High Tx Power High Packet Loss Note: Nodes are not uniformly spaced. CDF is slightly bias to bad link. Heavy tail in packet loss distributions for both in-door and habitat environments

96 Standard Deviation in Packet Loss Window size = 40 4B6B Encoding High Tx Power Variability over time with large dynamic range

97 Components of Infrastructure Processor PlatformsRadiosSensors Operating Systems LocalizationTime SynchronizationMedium AccessCalibration Collaborative Signal Processing Data-centric RoutingData-centric Storage Querying, Triggering Aggregation and Compression Collaborative Event Processing Monitoring

98 What is localization? Determining the location of a node in a global coordinate system Availability of location information is a fundamental need Interpreting the data Routing (GPSR) Geo-spatial queries Location based addressing

99 Why not equip every node with GPS? GPS needs line of sight, cannot be used in indoor environments in the presence of foliage

100 Early Schemes Active Bat (AT&T) People wear badges which emit ultra-sound pulse Receivers mounted in a regular grid on ceiling Time of flight based triangulation (centralized) CRICKET (MIT) Ultrasound ranging Fixed emitter infrastructure with known positions RADAR (Microsoft Research) Uses existing LAN Signal/Noise ratio of the targets used for localization

101 Ad-hoc Localization Sensor nodes are randomly scattered “Where am I”? Only a “small” fraction of nodes have GPS Anchors The rest have to infer their global positions somehow

102 General Approach Find distance to neighboring nodes Ranging Neighbors of anchors fix position relative to anchors Position fixing Other nodes fix their positions relative to at least three neighbors Iterative refinement

103 Ranging Radio Received signal strength based Use a path loss model to estimate range Need careful calibration for accuracy Can get to within 10% of radio range Examples: SpotON (Hightower et al.), Calamari (Whitehouse et al.) Acoustic Use time of flight of sound (ultrasound) Potentially high accuracy: 1% of radio range May need code spreading to counter multipath effects Examples: Girod et al., Savvides et al.

104 Position Fixing Taxonomy Topological Schemes Rely only on topology information Can result in very inaccurate localization Usually require less resources Geometric Schemes Use geometric techniques to determine location Usually result in highly accurate position estimates May require more resources

105 d1d1 d2d2 d3d3 d 1 +d 2 +d 3 Topological Schemes DV-Hop (Niculescu et. al.) Find average distance or hop davg distance = davg*(No of Hops) requires no ranging!!! DV-Dist (Niculescu et. al.) Standard distance vector algorithm range as metric Refinement can help significantly

106 x y  Geometric Schemes Savvides et. al. Each anchor defines a coordinate system, anchor is the origin. nodes localize in this coordinate system using hop by hop lateration nodes maintain (nodeId, x,y) 3 such tuples can be used to localize Observations Local coordinates may be translated, rotated or flipped versions of the global system distance from anchor is invariant

107 Comparison of existing schemes 3 schemes (20% anchors, 1% error) Geometric (Savvides et al., Niculescu et. al.) Topological (DV-dist) Localization extent Nodes localized within 2m Topological scheme Higher localization extent than geometric scheme. Need neighbors

108 The State of Localization Lots of research in the area Probably far from deploying robust systems in the field Every component is hard and error-prone Ranging Position-fixing

109 Components of Infrastructure Processor PlatformsRadiosSensors Operating Systems LocalizationTime SynchronizationMedium AccessCalibration Collaborative Signal Processing Data-centric RoutingData-centric Storage Querying, Triggering Aggregation and Compression Collaborative Event Processing Monitoring

110 Time Synchronization Critical piece of functionality Applications: Sample-level correlation Event time correlations Wide variety of requirements Microsecond level for acoustic localization Perhaps less for events Global? Post-facto? Prior work NTP Research Systems RBS (Elson et al.) TPSN (Ganeriwal et al.) DMTS (Ping Su) LTS (van Greunen et al.) Theory Optimal global synchronization (Karp et al. and Hu et al.)

111 One-Hop Synchronization Key step: determine clock offset Observation: Can use timestamped message exchange to infer clock offset Similar to the NTP algorithm Tricky

112 Timing Components Send Processing Time Access Time Propagation Latency Transmission Latency Receive Processing Time Non-Deterministic Negligible Deterministic

113 RBS (Elson et al.) Inter-receiver synchronization Based on broadcast from sender Receivers exchange timestamps of received messages Sender side non-determinism eliminated Receive processing costs Gaussian Estimated by averaging Clock drift estimated by linear regression Reference Broadcast Inter-receiver Synchronization

114 TPSN and LTS Sender-side synchronization Use the NTP algorithm TPSN Timestamps packets as close to radio as possible to remove non-determinism LTS Uses RBS-style averaging to remove non-determinism

115 DMTS One-way, one-packet synchronization Receiver computes offset from sender’s clock Do away with all sources of non-determinism by timestamping close to radio layer Send Processing Time Access Time Propagation Latency Transmission Latency Receive Processing Time Non-Deterministic Negligible Deterministic Timestamp

116 Multihop Synchronization Goals Synchronize a pair of nodes across multiple hops Synchronize all nodes with a base-station Basic idea is the same in both cases Successively synchronize nodes along a path In the latter case, do it over a spanning tree Error accumulates linearly

117 Reducing the Error One-hop synchronization estimates offsets between nodes For global synchronization, need: Minimum variance offset estimation Consistent maximum likelihood estimation of clock values Theoretical result: There exists estimators that jointly determine minimum variance offsets that give maximum likelihood times Karp et al. and Hu et al. Both based on the observation that one can use information along multiple paths Error grows logarithmically

118 Components of Infrastructure Processor PlatformsRadiosSensors Operating Systems LocalizationTime SynchronizationMedium AccessCalibration Collaborative Signal Processing Data-centric RoutingData-centric Storage Querying, Triggering Aggregation and Compression Collaborative Event Processing Monitoring


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