Reliable and Real-time Wireless Sensor Networks: Protocols and...

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?

Research contributions

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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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Coverage using wall!class model (Jolley)

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]

Research contributions

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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]

Research contributions

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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]

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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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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valid reading invalid reading

time-to-failure

Reliability metrics

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valid reading invalid reading

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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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

long  tail

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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

90%  of  outages  <  4  min

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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

90%  of  outages  <  4  min

90%  of  outages  <  2  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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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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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

Research contributions

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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

Research contributions

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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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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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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

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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0 q1 n p2 f k o z s3 e l h t r4 c j g w5 b m6 d

senders

steps

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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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0 q1 n p2 f k o z s3 e l h t r4 c j g w5 b m6 d

senders

steps

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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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0 q1 n p2 f k o z s3 e l h t r4 c j g w5 b m6 d

senders

steps

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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:

0 1 2 3 4 5 6

0 1 2 3 4 5 6

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

0 1 2 3 4 5 6

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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

0 1 2 3 4 5 6

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

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| ≥ ∆

0 1 2 3 4 5 6

0

1

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5

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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| ≥ ∆

Min step distance: Δ = 0

0 1 2 3 4 5 6

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

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| ≥ ∆

Min step distance: Δ = 1

0 1 2 3 4 5 6

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

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

28

di,j = |i− j| ≥ ∆

Min step distance: Δ = 3

0 1 2 3 4 5 6

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

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| ≥ ∆

Min step distance: Δ = 4

0 1 2 3 4 5 6

0 Δ Δ Δ1 Δ Δ2 Δ3

4 Δ5 Δ Δ6 Δ Δ Δ

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| ≥ ∆

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:

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

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

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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:

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

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

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NS2 simulations

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312.5

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2.5 3 3.5 4 4.5 5 5.5 6 6.5 7

Thro

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ata

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Total query rate (Hz)

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

30

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62.5

125

187.5

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312.5

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437.5

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2.5 3 3.5 4 4.5 5 5.5 6 6.5 7

Thro

ughp

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Total query rate (Hz)

DRANDDCQSDCQS-RC

58.6%

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

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

IH

GF

A

D

C

B

E

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

IH

GF

A

D

C

B

E

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

IH

GF

A

D

C

B

A ...B ...D ...

E

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

IH

GF

A

D

C

B

A ...B ...D ...

E

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

IH

GF

A

D

C

B

A ...B ...D ...

E

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

IH

GF

A

D

C

B

A ...B ...D ...

E

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

IH

GF

A

D

C

B

A ...B ...D ...

E

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

IH

GF

A

D

C

B

A ...B ...D ...

E

Sources of sensing errors

Hand  movement Improper  placement

0

25

50

75

100

0 3.75 7.5 11.25 15

Time (min)

0

25

50

75

100

0 3.75 7.5 11.25 15

Hea

rt R

ate

Time (min)

38

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

39

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1Tru

e P

osi

tive

Ra

te/(

sen

sitiv

ity)

False Positive Rate/(1 - specificity)

Random

79.3%

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

40

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

41

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

median  .me  to  failure  19  min

41

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

median  .me  to  failure  19  min

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

41

System architecture

42

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%

43

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%

44

Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

A1A2

A3

A4

P

AN

45

Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

A1A2

A3

A4

B1

B2B3

P

AN

45

Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

A1A2

A3

A4

B1

B2B3

P

AN

45

Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

A1A2

A3

A4

B1

B2B3

P

AN

45

Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

A1A2

A3

A4

B1

B2B3

P

AN

45

Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

A1A2

A3

A4

B1

B2B3

P

AN

45

Root cause of failures• Hypothesis:

mobility leads to CTP using outdate routes

base station

A1A2

A3

A4

B1

B2B3

P

AN

45

Real-time Sensor Networks

0 1 2 3 4 5 6

0

1

2

3

4

5

6

Δ = 7

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

47

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0

1

2

3

4

5

6

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

47

0 1 2 3 4 5 6

0

1

2

3

4

5

6

Δ = 6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

0

1

2

3

4

5

6

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

47

0 1 2 3 4 5 6

0

1

2

3

4

5

6

Δ = 50 1 2 3 4 5 6

0

1

2

3

4

5

6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

Δ = 40 1 2 3 4 5 6

0

1

2

3

4

5

6

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

47

0 1 2 3 4 5 6

0

1

2

3

4

5

6

0 1 2 3 4 5 6

0

1

2

3

4

5

6

0 1 2 3 4 5 6

0

1

2

3

4

5

6

0 1 2 3 4 5 6

0 1 2 3 4 5 6

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

48

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

49

WIISARD

51

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

52

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

54

Example coverage map

• Visible impact of walls on predicted coverage area

• Low false negative/false positive rates

1

1

1

1

1

1

1 1

1

1

1

Coverage using wall!class model (Jolley)

correct  predic-ons

fp:  exis-ng  coverage,  predicted  no  coverage

fn:  no  coverage,  predicted  coverage

55

Example coverage map

• Visible impact of walls on predicted coverage area

• Low false negative/false positive rates

1

1

1

1

1

1

1 1

1

1

1

Coverage using wall!class model (Jolley)

correct  predic-ons

fp:  exis-ng  coverage,  predicted  no  coverage

fn:  no  coverage,  predicted  coverage

55

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