Providing application qos through intelligent sensor management
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Providing Application QoS through Intelligent Sensor Management. Proceedings of the 1st IEEE International Workshop on SNPA '03. M. Perillo and W. Heinzelman. Nov. 11, 2003 Presented by Sookhyun, Yang. Contents. Introduction Multihop Sensor Network Management Problem Problem Formalization

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Providing Application QoS through Intelligent Sensor Management

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Providing application qos through intelligent sensor management

Providing Application QoS through Intelligent Sensor Management

Proceedings of the 1st IEEE International Workshop on SNPA '03

M. Perillo and W. Heinzelman

Nov. 11, 2003

Presented by Sookhyun, Yang


Contents

Contents

  • Introduction

  • Multihop Sensor Network Management Problem

    • Problem Formalization

    • Modeling

  • Simulation

  • Limitation

  • Conclusion


Introduction

Introduction

  • Wireless sensor network

    • Tight energy and bandwidth constraints

    • Tradeoff between power consumption and data reliability

  • Type of application QoS

    • Balancing the application reliability with energy-efficiency

  • In this paper

    • Turning off redundant sensors

    • Energy-efficient routing with joint scheduling

    • Intelligent management

      • Work with knowledge of future traffic patterns in the network

    • Maximizing lifetime while minimum level of reliability


Problem formalization 1 3

Multihop Sensor Network Management Problem

Problem Formalization (1/3)

  • Application activation

    • Perform an acceptable level of QoS using data from a number of different sensor sets

  • Strategy

    • Schedule the sets to maximize the sum of time that all sensor sets are used

    • Determine route selection in conjunction with the sensor schedule

      • Critical nodes for use as sensors

      • Length of time

        • Nodes that are active in the set

        • Nodes used in the chosen paths to the data sink

  • Feasible sensor sets

    • Bandwidth

    • Schedulable

    • Reliability


Problem formalization 2 3

Turn off

Multihop Sensor Network Management Problem

Problem Formalization (2/3)

  • Matter of scheduling

    • Which sensor combinations should be used to monitor the environment

    • How long sensor is turned on

    • How the data from these sensors should be routed to application

  • Multihop network

Sensor field

  • Multimode sensors

F2

F1

  • F: feasible set

F3

  • T: scheduled time

Active sensor

Sink

T1

T2

  • Power consumption

T3


Problem formalization 3 3

: Feasible sensor set

,

MAX

: length of scheduled time

<

Multihop Sensor Network Management Problem

Problem Formalization (3/3)

  • Constraints

    • Time (Node can route other node’s data)

    • Sensor cannot realistically operate in multiple modes within a single sensor set

    • Data forwarding is needed for the entire duration of each of its sensor set’s scheduled time if a sensor is not in direct communication

  • Objective of management problem

+

Time (Node’s initial energy)

Time (Node can be a active sensor)


Modeling 1 3

Energy

Time

Multihop Sensor Network Management Problem

Modeling (1/3)

F1

F2

s

S1

S2

  • Generalized maximum flow problem

d

S3

P211

R21

S1

F1

E1

S2

E2

F2

d

s

E3

Application

Energy bank

S3

E4

F3

S4

P431

R43

P432


Providing application qos through intelligent sensor management

Energy

Time

Multihop Sensor Network Management Problem

Modeling (2/3)

F1

F2

s

S1

S2

  • Generalized maximum flow problem

d

1/(# of intermediate node)

S3

1/(power consumption)

P211

R21

S1

F1

E1

1/(# of active sensor+ # of active sensors requiring data routing)

S2

E2

F2

1/(power consumption)

d

s

E3

Application

Energy bank

S3

E4

F3

S4

P431

R43

P432


Modeling 3 3

Energy

Time

Multihop Sensor Network Management Problem

Modeling (3/3)

  • Extension to multi-state applications

P211

R21

S1

State1

F1

State2

S2

F2

d

s

Application

Energy bank

S3

F3

Staten

S4

P431

R43

P432


Simulation 1 5

1 packet/sec

1J

15µJ

10µJ

sensor

Simulation (1/5)

  • Metric : lifetime

    • Optimal scheduling/routing from the feasible sensor sets

    • Randomly chosen from the feasible sensor sets

      • Shortest path routing

      • Shortest cost routing : energy consumption

  • Factors on lifetime improvement

    • Path length or transmission range

    • Sensor node density

    • Size of environment

  • Simulation setting

    • Feasible sensor sets are founded by determining which combinations of sensors would allow 100% of a predetermined portions of area to be monitored

    • # of feasible sensor sets = 50

    • Sensor node


Simulation 2 5

Simulation (2/5)

100*100m

25m

Sink

  • Result

    • Transmission range (Fig.2.)

      • Normalized to the optimal solution’s lifetime

      • Size of the benefit should remain relatively constant

    • Average shortest path length (Fig.3.)

      • Random set selection with shortest path/cost routing performs poorly

100 nodes

Fig. 2.

Fig. 3.


Simulation 3 5

Simulation (3/5)

100*100m

25m

Sink

  • Result (cont’d)

    • Sensor node density (Fig.4.)

      • As more energy is distributed, network lifetime is extended

      • Sensor node density seems to have a small effect on the size of relative improvement

Fig. 4. (b)

Fig. 4. (a)


Simulation 4 5

Simulation (4/5)

0.01node/m^2

Sink

  • Result (cont’d)

    • Size of environment (Fig.5.)

      • Since sensor location is random, the possibility of a lightly covered area increases

      • Average power consumption in the network should increase as the sensor data needs to be forwarded along more hops on average

Fig. 5. (a)

Fig. 5. (b)


Simulation 5 5

Simulation (5/5)

  • Result (cont’d)

    • Lifetime improvement (Table 1)

      • From nothing to more than a factor of 4


Limitation

Limitation

  • Overhead not considered

    • Setting up traffic schedules

    • Setting up and tearing down routes

  • Considered only routes in which each successive hop moves toward the base station to be valid

  • Require global information about the neighborhoods of each node

    • Not scale well for larger networks


Conclusion

Conclusion

  • Sensor scheduling and routing improves liftetime larger than a factor of 4 when compared with more random methods

  • Paper’s model represent some typical networks that are likely to be used in sensor


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