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• MiLAN is a middleware which goal is 1)maximizing application lifetime while 2)providing application QoS by controlling and optimizing network as well as sensors
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Motivation
High respiratory rate
Normal Heart Rate
Low blood pressureRespiratory
Rate
Blood O2
BloodPressure
Blood O2
HeartRate
0.8
0.7
High Heart Rate
ECGDiagram
BloodPressure
BloodO2
HeartRate
0.3
0.8
0.3
0.8
0.3
1.0
0.3
• Personal Health Monitor Application– The QoS of the different variables of interest at each different states of patient– The state-based Variable Requirement graph
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Motivation• Personal Health Monitor Application
– The QoS of the different variables depends on which sensors provide data to the application
– The Sensor QoS Graph
Bloodpressure
Heartrate
Bloodpulse
Bloodpress
Bloodflow
Pulseoxy
ECGBloodpress
Bloodflow
Pulseoxy
0.7 1.0 0.8 0.7 1.0 0.70.7 0.8
1.0
Virtual sensor
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Motivation
• The characteristics of sensor network
2. Dynamic Availability
Either mobility through space, addition of new sensors, or loss of existing sensors causes the set of available sensors to change over time
3. Resource Limitation
Both network bandwidth and sensor energy are constrained. This is especially true when considering battery-powered sensors and wireless networks
1. Inherent Distribution
The sensors are distributed throughout a physical space and primarily connected wirelessly
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Goal of MiLAN
ProvideApplication
QoS
Maximize Application
Lifetime
Control the network as well as the sensors
• Goal– To satisfy the given application QoS specification and
provide data to application as long as possible, MiLAN control sensor network as well as the sensors
ApplicationQoS
Requirement
NetworkMonitoring
State Of Monitored
Objects
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MiLAN Overview
App.Feasible
Set
NetworkFeasible
Set
Sensor Network Configuration
Sensor Network
ApplicationQoS Requirement
Sensed ObjectStates
NetworkInformation
OverallSet
Application Logic
Sensor Reading
Doctor
Application Middleware - MiLAN
Trade-off computation
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Application Feasible Set FA
Set # Sensors
1 Blood flow, Respiratory rate
2 Blood flow, ECG (3 leads)
3 Pulse oxymeter, Blood pressure,
ECG(1 lead), Respiratory rate
4 Pulse oxymeter, Blood pressure, ECG(3 leads)
5 Oxygen Measurement, Blood pressure, ECG(1 lead), Respiratory rate
6 Oxygen measurement, Blood pressure, ECG(3 leads)
• Multiple set of sensors, which can provide application QoS at a given state, can be derived from the state-based variable requirement graph and the sensor QoS graph
• A patient state– medium stress– high heart rate, normal respiratory rate, and low blood pressure
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Network Feasible Set FN
• Network Feasible Set– Network plugin’s job– The subsets of nodes that can be supported by
the network• Suppose that the sensors and processors communicate
using an IEEE 802.11a network• It can support overall throughput of nearly 11Mb/s• However, if multiple applications are running
simultaneously on the network and the personal health monitor application can only utilize 100kb/s of the throughput, the network would not be able to support the transmission of data from the ECG sensor with either 3, 5, or 12 leads
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Overall Set F
• F = FA ∩ FN
• Example of Overall set F– Suppose network can’t support ECG 3, 5 12 leads, since other applications are running
simultaneously
Set # Sensors
1 Blood flow, Respiratory rate
2 Blood flow, ECG (3 leads)
3 Pulse oxymeter, Blood pressure,
ECG(1 lead), Respiratory rate
4 Pulse oxymeter, Blood pressure,
ECG(3 leads)
5 Oxygen Measurement, Blood pressure, ECG(1 lead), Respiratory rate
6 Oxygen measurement, Blood pressure, ECG(3 leads)
Set # Sensors
1 Blood flow, Respiratory rate
3 Pulse oxymeter, Blood pressure,
ECG(1 lead), Respiratory rate
5 Oxygen Measurement, Blood pressure, ECG(1 lead), Respiratory rate
Application Feasible Set FA
Overall Set F
FA ∩ FN
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Trade-off Computation
• Goal– Among the elements in overall set F, MiLAN choose an element f
that represents the best performance/cost trade-off
• For getting more information about trade-off computation, refer to the paper named “Providing Application QoS through Intelligent Sensor Management” published in Elsevier Ad Hoc Network Journal, vol. 1, no. 2-3, 2003– Mathematically formulate the problem– Interpret the problem as a Generalized Maximum Flow Problem– None of the algorithms that are commonly used to solve
generalized maximum flow problem in polynomial time can be used for the sensor scheduling problem
– They use simple linear programming approach
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