Reliable and Real-time Wireless Sensor Networks: Protocols and …homepage.divms.uiowa.edu/~ochipara/presentations/uiowa... · 2013. 1. 15. · Wireless sensor networks • Wireless

Reliable and Real-time Wireless Sensor Networks: Protocols and Medical Applications

Octav ChiparaUniversity of California, San Diego

https://sites.google.com/site/ochipara/

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Wireless sensor networks• Wireless sensor networks:

• sensing + computation + wireless communication

• Trade-off computational power in favor of• low-power operation

• form factor and weight

• lower cost (today ≈ $70)

• ➡ enable continuous monitoring of physical phenomena at high resolution over long periods

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TelosB MicaZ

Irene

Mica Dot

AcmeSeeMote

Pervasive monitoring enables personalized care

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Detection of clinical deterioration

Effective emergency response

Analysis of motor function

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Picture credit: *Mercury Project, Harvard

Research contributions

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How do we deliver real-‐-me and reliable communica-on?

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Coverage using wall!class model (Jolley)

How do we deploy wireless sensor networks?

How do we manage limited energy budgets?

How do we ensure robustness in real-‐world systems?


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Protocols & schedulability analysis for real-‐-me sensor networks [TMC’11][RTSS’07][ICNP’06][IWQOS’06][IPSN’06] [ICDCS’05]

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Radio mapping toolkit for assessing network coverage [IPSN’10]

Radio power management based on workload proper-es [SenSys’08][SenSys’07][WCMC’09][ICDCS’05]

Cross-‐disciplinary medical collabora-ons:clinical monitoring, emergency response [SenSys’10][AMIA’09]


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Detecting clinical deterioration at low cost

• Clinical deterioration in hospitalized patients• 4-17% suffer adverse events (e.g., cardiac or respiratory arrests)

• up to 70% of such events could be prevented.

• Early detection of clinical deterioration• clinical deterioration is often preceded by changes in vitals

• Real-time patient monitoring is required• wired patient monitoring ➡ inconvenient

• wireless telemetry systems ➡ too expensive for wide adoption

• most general hospital units collect vitals manually and infrequently

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Detecting clinical deterioration at low cost

• Clinical deterioration in hospitalized patients• 4-17% suffer adverse events (e.g., cardiac or respiratory arrests)

• up to 70% of such events could be prevented.

• Early detection of clinical deterioration• clinical deterioration is often preceded by changes in vitals

• Real-time patient monitoring is required• wired patient monitoring ➡ inconvenient

• wireless telemetry systems ➡ too expensive for wide adoption

• most general hospital units collect vitals manually and infrequently

Goal: reliable and real-‐.me wireless clinical monitoring for general hospital units

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Wireless sensor networks vs. Wi-Fi• Commercial telemetry systems (Phillips, Cisco, GE):

• Wi-Fi ➡ single-hop wireless, wired backbone

• adoption limited to specialized hospital units

• Benefits of wireless sensor networks• more energy efficient than Wi-Fi at low data rate

• common vital signs have low data rate

• nurses are too busy to change batteries!

• low deployment cost

• eliminate wired infrastructure ➡ mesh networks

• ➡ ease of adoption (e.g., field hospitals, rural areas)

• reliability of wireless sensor networks - an open question!

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Related work• Wireless sensor networks for medical applications

• Assisted living: ALARM-NET

• Disaster recovery: AID-N, CodeBlue, WIISARD

• Emergency room: MEDISN, SMART

• Motion analysis: Mercury

• Numerous systems, limited results from clinical deployments

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MEDISN, John Hopkins

Code Blue, Harvard

Code Blue, Harvard

Related work• Wireless sensor networks for medical applications

• Assisted living: ALARM-NET

• Disaster recovery: AID-N, CodeBlue, WIISARD

• Emergency room: MEDISN, SMART

• Motion analysis: Mercury

• Numerous systems, limited results from clinical deployments

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MEDISN, John Hopkins

Code Blue, Harvard

Code Blue, Harvard

First holistic reliability study performed in a hospital unit using sensor networks

System architecture• Base-station

• laptop that will store medical data

• Relay nodes• redundant deployment:

• coverage

• fault-tolerance

• plugged into wall outlets

• Patient nodes• pulse oximeter + microcontroller

+ radio

• same pulse-oximeter as used in hospitals

• battery powered

Patient node

Relay node

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Reliable network architecture• Initial solution: use CTP [1] all nodes in the network

• Insight: isolate the impact of mobility

• Solution: two-tier architecture for end-to-end data delivery

• Dynamic Relay Association Protocol (DRAP): Patient ➡ 1st relay• DRAP used on mobile nodes ➡ dynamically associate relays

• single-hop protocol handles patient mobility

• simplify power management in patient nodes (send only)

• Stationary relay network: 1st relay ➡ … ➡ base station• CTP used on fixed nodes ➡ reuse well-tested CTP

• wall-plugged ➡ no need to worry about energy

10[1] O. Gnawali et. al., Collection Tree Protocol. SenSys 2009

• Step-down cardiac care unit• 16 patient rooms, 1200 m2

• Network• 18 relays: redundant network

• longest path: 3-4 hops

• channel 26 of IEEE 802.15.4

• HR and SpO2 collected every 30/60s• disposable adult probes

• data not available to nursing staff

• 46 patients enrolled• 41 days in total, 2-68 hours per patient

• up to 3 patients at a time

• 5 patients excluded from analysis

Clinical deployment

base station

relays

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Potential for detecting clinical deterioration

Pulmonary edema

Sleep apnea

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#$%&

Bradycardia

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#$%&

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#$%&

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System reliability• Network reliability per patient: 99.68% median, range 95.2% - 100%

• effectiveness of two-tier DRAP/CTP network architecture

• Sensing reliability per patient: 80% median, range 0.46% - 97.69% • 29% of patients with sensing reliability < 50%

• System reliability dominated by sensing reliability!

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Reliability metrics

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valid reading invalid reading

Reliability metrics

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time-to-failure

Reliability metrics

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time-to-failure time-to-recovery

Sensing reliability• Failures are common: median time-to-failure 2 min

• long-tailed distribution ➡ caused by sensor disconnections

• short failure bursts ➡ caused by human movement

• Mechanisms for improving reliability• oversampling

• disconnection alarmsCDF of time-to-failure CDF of time-to-recovery

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long tail

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90% of outages < 4 min

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Relax sampling requirement• Increase the required sampling period to 5, 10, 15 min

• consider a success if one valid measurement per sampling

• still orders of magnitude higher rate than manual measurement

• Oversampling is beneficial• reliability is increased, but at a diminishing return

Sensing reliability for different required sampling rates

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Alarms for sensor disconnections• Automatically notify a nurse after receiving no valid data for a time

• balance nursing effort and reliability gain

• similar reliability to 5 and 10 min timeout

• at 15 min timeout ➡ 1.55 interventions per patient, per day

Sensing reliability with different timeouts # of alarms per patient, per day

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Alarms for sensor disconnections• Automatically notify a nurse after receiving no valid data for a time

• balance nursing effort and reliability gain

• similar reliability to 5 and 10 min timeout

• at 15 min timeout ➡ 1.55 interventions per patient, per day

Sensing reliability with different timeouts # of alarms per patient, per day

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Alarms and oversampling are complementary• Complementary mechanisms

• alarms ➡ handles disconnections

• oversampling ➡ handles intermittent failures

Sensing reliability when alarms and oversampling are combined

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Alarms and oversampling are complementary• Complementary mechanisms

• alarms ➡ handles disconnections

• oversampling ➡ handles intermittent failures

Sensing reliability when alarms and oversampling are combined

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Contributions• Reliable two-tier architecture: 99.68% reliability per patient

• Cross-disciplinary research - key to understanding the problem• system reliability is dominated by sensing errors

• complementary mechanisms for combating sensing errors

• oversampling

• disconnections alarms

• ➡ reduced patient with low reliability (<70%) from 42% to 12%

• Potential for detecting deterioration• possible through inspection by clinician

• preliminary results of automatic detection of clinical deterioration

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Contributions• Reliable two-tier architecture: 99.68% reliability per patient

• Cross-disciplinary research - key to understanding the problem• system reliability is dominated by sensing errors

• complementary mechanisms for combating sensing errors

• oversampling

• disconnections alarms

• ➡ reduced patient with low reliability (<70%) from 42% to 12%

• Potential for detecting deterioration• possible through inspection by clinician

• preliminary results of automatic detection of clinical deterioration

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O. Chipara, C. Lu, T. C. Bailey, and G.-C. Roman. Reliable Clinical Monitoring using Wireless Sensor Networks: Experiences in a Step-down Hospital Unit. SenSys 2010

O. Chipara, C. Brooks, S. Bhattacharya, C. Lu, R. Chamberlain, G-C Roman, and T. C. Bailey. Reliable Real-time Clinical Monitoring Using Sensor Network Technology. AMIA 2010


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Medical applica-ons:clinical monitoring, emergency response

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Schedulability analysis for real-‐-me sensor networks

Radio mapping toolkit for assessing network coverage

Radio power management based on workload proper-es


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Medical applica-ons:clinical monitoring, emergency response

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Schedulability analysis for real-‐-me sensor networks

Radio mapping toolkit for assessing network coverage

Radio power management based on workload proper-es

Real-time wireless sensor network

• Medical systems will have stringent non-functional requirements• low latency, high throughput, and reliability

• Best-effort and brittle systems• CSMA ➡ difficult to bound performance

• Predicable systems ≡ check that its requirements are met

• TDMA ➡ fixed schedules difficult to adapt

• Build a predictable query service for sensor networks• optimized for sensor networks

• dispense with fixed schedules ➡ increased flexibility

Clinical Monitoring Fall Detection Smart Homes

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Query services for sensor networks

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SELECT acceleration FROM acceleration-sensorsSAMPLE RATE 10HzDEADLINE 0.1s

• Query - actual specification

• Query instance - repeated every period to collect data

• Properties of query services:

• shared routing tree

• data aggregation ➡ precedence constraints

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Network model• Graph model:

• Vertices - all nodes in network

• Communication edge (AB): A’s transmission may be received by B

• Interference edges (AB): B cannot receive while A transmits, even though B cannot decode A’s transmission

• Two transmissions are conflict free if:• (AB) and (CD) transmit

• (AD) and (CB) are not present in the graph

• Constructed using Radio Interference Detection (RID)[1]

A B

C D

[1] G. Zhou et. al., RID: radio interference detection in wireless sensor Networks. INFOCOM’05

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A B

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Dynamic query scheduling

• Planner: off-line

• constructs plans for a single query instance

• plan = seq. of transmissions necessary to deliver data to base station

• reduces query latency based on precedence constraints

• Scheduler: run-time

• dynamically schedule multiple concurrent queries

• improve throughput by concurrently executing multiple instances

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Separation of concerns

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Planner

Workload

Routing

Interference

Scheduler

Plans

Overload?ratecontrol

run-time

off-line

stateprocess

Planner: plans for single instances• Plan: a sequence of steps, each containing one/more transmissions

• interference constraints: transmissions in each step do not conflict

• precedence constraints: children transmit before their parents

• reliability constraints: each node transmits sufficient times

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Scheduling multiple query instances • A safe scheduler:

• execute a single query instance at a time in the network

• instances are executed non-preemptively based on their plans

• Drawback: reduced throughput!

10 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9Slots:

Q1:

Q2:

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Q1: start = 0, period = 10 Q2: start = 8, period = 16

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delay

Scheduling multiple query instances • A safe scheduler:

• execute a single query instance at a time in the network

• instances are executed non-preemptively based on their plans

• Drawback: reduced throughput!

10 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9Slots:

Q1:

Q2:

0 1 2 3 4 5 6

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0 1 2 3 4 5 6

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delay

Reduced throughput?

Enabling spatial reuse

• Min. step distance is the smallest number Δ such that:if distance between any two steps i and j exceeds Δ => conflict-free

• Enforce a gap of at least min. step distance between the start of consecutive instances ➡ conflict-free transmission

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di,j = |i− j| ≥ ∆

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di,j = |i− j| ≥ ∆

Min step distance: Δ = 0

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0 Δ Δ Δ Δ Δ Δ Δ1 Δ Δ Δ Δ Δ Δ Δ2 Δ Δ Δ Δ Δ Δ Δ3 Δ Δ Δ Δ Δ Δ Δ4 Δ Δ Δ Δ Δ Δ Δ5 Δ Δ Δ Δ Δ Δ Δ6 Δ Δ Δ Δ Δ Δ Δ




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di,j = |i− j| ≥ ∆


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0 Δ Δ Δ Δ Δ Δ1 Δ Δ Δ Δ Δ Δ2 Δ Δ Δ Δ Δ Δ3 Δ Δ Δ Δ Δ Δ4 Δ Δ Δ Δ Δ Δ5 Δ Δ Δ Δ Δ Δ6 Δ Δ Δ Δ Δ Δ




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di,j = |i− j| ≥ ∆


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di,j = |i− j| ≥ ∆

DCQS scheduler that support spatial reuse• Scheduler:

• release queue; Qn first instance in release queue

• Qc last instance that started execution

• start Qn after Qc completes at least ∆ steps

• Properties:• predictable throughput 1/Δ

10 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9Slots:

Q1:

Q2:

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DCQS scheduler that support spatial reuse• Scheduler:

• release queue; Qn first instance in release queue

• Qc last instance that started execution

• start Qn after Qc completes at least ∆ steps

• Properties:• predictable throughput 1/Δ

10 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9Slots:

Q1:

Q2:

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NS2 simulations

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DRANDDCQSDCQS-RC

• Setup: 81 nodes, 675m x 675m, 802.11 networks settings

• Workload: four queries with rates 8:4:2:1, increase rates prop.

NS2 simulations

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Rate control avoids overloads!

• Setup: 81 nodes, 675m x 675m, 802.11 networks settings

• Workload: four queries with rates 8:4:2:1, increase rates prop.

Contributions• A novel approach to transmission scheduling

• takes advantage of workload semantics

• integrates routing and reliability concerns

• dynamic transmission scheduling ➡ adapts to workload changes

• throughput improvement ≈ 58%, tight bounds

• Real-time query scheduling• provides bounded message latencies under static priorities

• bridges the gap between real-time theory and sensor networks

• Recently, extended analysis to support flows

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Contributions• A novel approach to transmission scheduling

• takes advantage of workload semantics

• integrates routing and reliability concerns

• dynamic transmission scheduling ➡ adapts to workload changes

• throughput improvement ≈ 58%, tight bounds

• Real-time query scheduling• provides bounded message latencies under static priorities

• bridges the gap between real-time theory and sensor networks

• Recently, extended analysis to support flows

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O. Chipara, C. Lu, J. Stankovic, and G.-C. Roman. Dynamic Conflict-free Query Scheduling for Wireless Sensor Networks. ICNP 2006

O. Chipara, C. Lu, and G. Roman. Real-time Query Scheduling for Wireless Sensor Networks. RTSS 2007

O. Chipara, C. Lu, J. Stankovic, and G.-C. Roman. Dynamic Conflict-free Query Scheduling for Wireless Sensor Networks. IEEE TMC 2011

Research opportunities in medical domain• Low-cost physiological monitoring

• applications: clinical deterioration, diabetes, Parkinson’s

• trade-off between accuracy and resource utilization (energy, bandwidth)

• Smart living spaces for elderly patients• applications: activity tracking, medication reminders

• sensor placement to improve activity detection

• Monitoring communities• applications: diseases control, support groups (weight/depression)

• extraction of social relationships

• Integrated medical systems• emergence of rich electronic medical records

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Research challenges for computer science• Predictable and robust wireless embedded systems

• holistic approach: sensing + computation + wireless

• ➡ techniques and tools to reason about system properties

• Interoperability• heterogenous systems with extreme differences in capabilities

• multiple wireless technologies that need to co-exist

• privacy and security cannot be sacrificed

• ➡ resource-aware middleware for efficiency

• Management and configuration• large scale systems

• ➡ algorithms for self-configuration and self-organization

• ➡ frequency- and time-domain wireless spectrum allocation

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Acknowledgements• Advisors

• Chenyang Lu and Catalin Roman, WUSTL

• William Griswold, UCSD

• Computer science collaborators• Jack Stankovic, UVA

• William Smart, WUSTL

• Medical collaborators • Thomas Bailey, Medical School @ WUSTL

• Nurses at Barnes Jewish Hospital

• Ted Chan and Colleen Buono, EMS & Disaster Medicine @ UCSD

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?questions

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Clinical Monitoring

Patients are mobile• Technical challenge: on general hospitals are ambulatory

• Initial solution: reuse CTP[1] to collect data from sensor nodes

• Mobility significantly degrades the performance of CTP

• routing loops

• stale information in routing tables

• retransmissions worsen the problem

37[1] Omprakash Gnawali et. al., Collection Tree Protocol. SenSys 2009




• routing loops



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Sources of sensing errors

Hand movement Improper placement

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Automatic detection of clinical deterioration

• Sample: 29 with significant cardiac/pulmonary problems and 7 without

• CUSUM ➡ partitions time series into chunks with similar statistical props.

• Chunk features: 5th- and 95-percentiles, slope of linear fit

• deterioration detected by comparison with thresholds

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Power management• Radio consumes 19 mA

• DRAP achieves duty-cycles of 0.12% - 2.09% during trial

• Sensor consumes 24 mA• periodically turn the sensor on

• 8-seconds warm-up time

• sample for 7 seconds

• Internal flash 2.3 mA• used to maintain statistics

• cache data in RAM, write infrequently to the internal flash

• Achieves up to 3 days of continuous operation

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Network reliability• Time-to-failure

• time interval during which the system continuously operates until failure

• Time-to-recovery• time interval from the occurrence of a failure until the system recovers

CDF of time-to-failure CDF of time-to-recovery

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median .me to failure 19 min

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median .me to failure 19 min

recover from 90% of failures within 2 mins => quick recovery

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System architecture

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CTP under normal activity

• Normal activity experiment showed

• most packet losses occur on the first hop

• packet drops tend to follow user movement

first-hop reliability: 85.38%relay reliability: 96.49%end-to-end reliability: 82.39%

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DRAP reliability

DRAP

one-hop reliability: 100%relay reliability: 99.33%end-to-end reliability: 99.33%

CTP

one-hop reliability: 85.38%relay reliability: 96.49%end-to-end reliability: 82.39%

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Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

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Real-time Sensor Networks

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Enabling spatial reuse• Minimum gap time is the smallest number ∆ such that:

if distance between the start of consecutive instances is at least ∆ => conflict free

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Real-time analysis for sensor networks

Real-‐-me analysis for WSN under realis-c interference and communica-on models

Real-‐-me systems WSN• processors • wireless channels

• reliable communication • lossy communication

• closed systems • dynamic systems

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Mapping query model to the task model

tasks queries

jobs are released periodically query instances are released periodically

jobs of a task share the same code query instances share the same plan = sequence of transmissions

bounded execution time plan length (?)

Periodic task model

WSN query model

SELECT acceleration FROM sensorsSAMPLE RATE 10HzDEADLINE 0.1s

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WIISARD

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Emergency response systems• Large scale events: accidents, quakes, terrorism

• numerous victims strain the response system

• ``Fed-ex`` for people• triage, minor treatments, prioritized transport

• Today’s approach• paper tags indicate triage status

• communication over noisy radios

• ➡ labor intensive, error-prone

• WIISARD: RFID tags + phones

Ensuring reliability during disasters• Goal: deliver patient data to all first responders

• Challenges• numerous mobile users

• dynamic wireless environments: interference + obstacles

• limited coverage ➡ frequent disconnections

• Initial approach: client server architecture + routing

• Insight: dispense with the creation of routes

• peer-to-peer architecture ➡ tolerates disconnections

• gossip-based dissemination protocol ➡ relies on local state

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RADIO MAPPING

Radio mapping• Challenge:

• signals affect by: distance, antenna orientation, objects, multi-path

• relative impact of each factor unknown for 802.15.4

• Insights: • trade-off between model complexity and measurement effort

• impact of walls more important than antenna orientation or multi-path

• Our approach:• take advantage of readily available floor plans

• automatically classify walls into a small number of classes

• use expectation maximization to fit parameters

• train multiple models with varying number of classes

• select the best model using cross-validation techniques

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Example coverage map

• Visible impact of walls on predicted coverage area

• Low false negative/false positive rates

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fp: exis-ng coverage, predicted no coverage

fn: no coverage, predicted coverage

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Example coverage map

• Visible impact of walls on predicted coverage area

• Low false negative/false positive rates

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correct predic-ons

fp: exis-ng coverage, predicted no coverage

fn: no coverage, predicted coverage

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O. Chipara, G. Hackmann, C. Lu, W. D. Smart, and G.-C. Roman. Practical Modeling and Prediction of Radio Coverage of Indoor Sensor Networks. IPSN 2010

Documents

Reliable and Real-time Wireless Sensor Networks: Protocols and …homepage.divms.uiowa.edu/~ochipara/presentations/uiowa... · 2013. 1. 15. · Wireless sensor networks • Wireless