1. 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

2. Outline Introduction Related work System model Channel assignment approaches Simulation results Conclusion

3. 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.

4. 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.

5. 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.

6. 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.

7. System Model Network model Energy consumption model Modeling primary user (PU) behavior

8. Network Model

9. Network Model (cont.) In each time slot, CMs will be in one of the three states, listen, transmit or sleep.

10. : Listen : Sleep : Transmit

14. Energy Consumption Model E cir : 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

15. Modeling Primary User (PU) Behavior

16. Channel Assignment Approaches R-Coefficient Channel assignment

17. 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:

18. R-Coefficient (cont.) The predicted residual energy: sensor i current residual energy

19. Channel assignment Random pairing Greedy channel search Optimization-based channel assignment

20. Random pairing 2 12 14 16 12 546 10 : Listen : Sleep : Transmit

21. Random pairing 10 14 12 6 5 : Listen : Sleep : Transmit

22. Random pairing 2 10 14 12 6 546 5 : Listen : Sleep : Transmit

23. Random pairing 5 12 0 1 : Listen : Sleep : Transmit

24. Greedy channel search 3 12 14 16 12 546 10 : Listen : Sleep : Transmit

25. Greedy channel search 12 14 13 12 10 : Listen : Sleep : Transmit

26. Greedy channel search 3 12 14 13 12 546 10 : Listen : Sleep : Transmit

27. Greedy channel search 12 11 13 12 10 : Listen : Sleep : Transmit

28. Optimization-based channel assignment 2 12 14 16 12 546 10 : Listen : Sleep : Transmit

29. Optimization-based channel assignment 10 9 8 : Listen : Sleep : Transmit

30. Optimization-based channel assignment 10 9 8 2 545 : Listen : Sleep : Transmit

31. Optimization-based channel assignment 6 7 5 8 5 : Listen : Sleep : Transmit

32. Simulation Result

37. 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.

38. 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.