Residual Energy Aware Channel Assignment in Cognitive Radio Sensor Networks

Wireless Communications and Networking Conference

(WCNC), 2011 IEEE Xiaoyuan Li ; Dexiang Wang ; McNair, J. ; Jianmin Chen

Outline

• Introduction• Related work• System model• Channel assignment approaches• Simulation results• Conclusion

Introduction

• Existing WSNs are traditionally characterized by fixed spectrum allocation over crowded bands.

• The event-driven nature often generates bursty traffic, which increases the probability of collision and packet loss.

• Cognitive radio allows opportunistic spectrum access to multiple available channels, which gives potential advantages to WSNs by increasing the communication reliability and improving the energy efficiency.

Introduction (cont.)

• Most of the studies concentrate on sensing channel availability to improve spectrum utilization, modeling PU activity to avoid collision or analyzing QoS performance such as delay and throughput.

• However, only a few of the current studies for channel assignment in cognitive radio networks consider energy consumption problem, which is the critical concern for energy-constrained WSNs.

Introduction (cont.)

• In this paper, we consider a multi-channel CRSN, in which a cognitive radio is installed in each sensor.

• The radio can be tuned to any available channel. The channel assignment problem is investigated from the aspect of energy consumption and network lifetime.

Related Work

• OSA-MAC protocol based on IEEE 802.11 model is proposed for opportunistic spectrum access.

• It provides both uniformly random channel selection and spectrum opportunity-based channel selection.

• However, it does not consider the state change of PU behavior, which is studied in our work.

System Model

• Network model• Energy consumption model• Modeling primary user (PU) behavior

Network Model

Network Model (cont.)

• In each time slot, CMs will be in one of the three states, listen, transmit or sleep.

: Listen

: Sleep

: Transmit

assign_request

assign_request

assign_request: Listen

: Sleep

: Transmit

assign_reply

assign_reply

assign_reply

: Listen

: Sleep

: Transmit

: Listen

: Sleep

: Transmit

Energy Consumption Model

Ecir: RF radio circuit energy consumption

ε: the amplifier energy required at the receiver

D : the distance between CM and CH

α: path loss coefficient depending on the path characteristics

l : number of slots

Modeling Primary User (PU) Behavior

Channel Assignment Approaches

• R-Coefficient• Channel assignment

R-Coefficient

• The probability that sensor i only transmits for l slots on channel j due to the collision with PU:

• the statistically expected energy consumption for sensor i transmitting on channel j:

R-Coefficient (cont.)

• The predicted residual energy:

sensor i current residual energy

Channel assignment

• Random pairing• Greedy channel search• Optimization-based channel assignment

Random pairing

2

12

14

16

12

5 4 6

10

: Listen

: Sleep

: Transmit

Random pairing

10

14

12

65

: Listen

: Sleep

: Transmit

Random pairing

2

10

14

12

6

5 4 6

5

: Listen

: Sleep

: Transmit

Random pairing

5

12

12

01

: Listen

: Sleep

: Transmit

Greedy channel search

3

12

14

16

12

5 4 6

10

: Listen

: Sleep

: Transmit

Greedy channel search

12

14

13

1210

: Listen

: Sleep

: Transmit

Greedy channel search

3

12

14

13

12

5 4 6

10

: Listen

: Sleep

: Transmit

Greedy channel search

12

11

13

1210

: Listen

: Sleep

: Transmit

Optimization-based channel assignment

2

12

14

16

12

5 4 6

10

: Listen

: Sleep

: Transmit

Optimization-based channel assignment

10

9

10

810

: Listen

: Sleep

: Transmit

: Listen

: Sleep

: Transmit

Optimization-based channel assignment

10

9

10

810

2 5 4 5

: Listen

: Sleep

: Transmit

Optimization-based channel assignment

6

7

5

85

: Listen

: Sleep

: Transmit

Simulation Result

Conclusion

• In this paper, we study the channel assignment problem in a cluster-based multi-channel CRSN with consideration of energy consumption, residual energy balancing and network lifetime.

• The simulation results show evident improvement coming from the R-coefficient based channel assignment on both energy consumption and residual energy balance.

每日一句

• Therefore, energy consumption and residual energy balance are critical in WSN design.

• Therefore, energy consumption and residual energy balance are both critical in WSN design.