Reliable and Real-time Wireless Sensor Networks: Protocols

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

Octav ChiparaUniversity of Iowa

https://cs.uiowa.edu/~ochipara

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

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

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

6[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

HR

SpO2

Bradycardia

HR

SpO2

HR

SpO2

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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

• An option to improve reliability• oversampling

CDF 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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Sources of sensing errors

Hand movement Improper placement

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50

75

100

0 3.75 7.5 11.25 15

Time (min)

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Time (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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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 thresholds16

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False Positive Rate/(1 - specificity)

Random

79.3%

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

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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?questions

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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


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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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Reliable and Real-time Wireless Sensor Networks: Protocols