Z. CHEN ET AL.171

and energy consumption per bit, the plot of energy con-

sumption per bit versus the throughput can compare the

energy saving performance of CLAC and TLAC fairly

and clearly.

In order to generate this plot, some assumptions must

be made about the parameters of the WSN. It is assumed

that the WSN consist of 16N

sensor nodes. A data

rate of , a noise power of

and a frequency bandwidth of are chosen

for the physical layer. A transmit probability

20kbpsR110dbmw

N

P

20kHzB

116p

is used for TLAC. The resulting energy consumption per

bit versus the throughput plot is shown in Figure 2. The

upper curve denotes the TLAC algorithm and the lower

curve denotes the CLAC algorithm.

Figure 2 shows that CLAC consumes less energy per

bit than TLAC at the same throughput demand. In the

high throughput region, two curves are close to each

other. The SNR increase along with the increase of

throughput, the MAI has more evident influence on BER

than noise. This evident influence of MAI result in that

the transmit probability designs can ignore the value of

SNR, so the performances of two algorithm are close. On

the other hand, CLAC have significant energy savings

relative to TLAC in the low throughput region. The SNR

decrease along with the decrease of throughput, the ef-

fect of noise on BER is obvious, so it is very necessary

for the transmit probability variety according to SNR.

Another interesting phenomenon can be found in Fig-

ure 2. For CLAC, the energy consumption per bit de-

creease as the decreasing of throughput, but the energy

consumption per bit for TLAC trended to be constant as

the decreeasing of throughput. The reason is that the

transmit probability for TLAC dose not change along

with SNR, the multiple access channel capacity is not

used adequately, and the decreasing of SNR and the de-

creasing of data rate have canceling effect on the energy

consumption per bit.

05 10 15 20 25

-400

-350

-300

-250

-200

-150

-100

-50

0

50

Throughput (kbit/s)

Energy/bit (dBJ)

CLAC

TLAC

Figure 2. Energy consumption per symbol vs. throughput

per node.

It also emphasized that the cross-layer optimizes access

control is indispensable for low throughput WSN.

5. Conclusions

This paper presents a WSN access control algorithm.

Based on slotted ALOHA protocol, this algorithm in-

corporates the power control of physical layer, the

transmitting probability of MAC layer, and the ARQ of

link layer. In this algorithm, a cross-layer optimization is

preformed to minimizing the energy consuming per bit.

Analytical results show that the cross-layer algorithm

results in a significant energy savings relative to layered

design subject to the same throughput per node. At the

same time, the cross-layer algorithm has low complexity

of implementation, and it is suitable to the low energy

consumption and low complexity WSN devices.

6. References

[1] K. Romer and F. Mattern, “The design space of wireless

sensor networks,” IEEE Wireless Communications, Vol.

11, No. 6, pp. 54–61, 2004.

[2] J. N. Al-Karaki, and A. E. Kamal, “Routing techniques in

wireless sensor networks: A survey,” IEEE Wireless

Communications, Vol. 11, No. 6, pp. 6–28, 2004.

[3] Y. E. Sagduyu and A. Ephremides, “The problem of

medium access control in wireless sensor networks,”

IEEE Wireless Communications, Vol. 11, No. 6, pp. 44–

53, 2004.

[4] W. Ye, J. Heidemann, and D. Estrin, “Medium access

control with coordinated adaptive sleeping for wireless

sensor networks,” IEEE/ACM Transactions Networking,

Vol. 12, No. 3, pp. 493–506, 2004.

[5] S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-constrained

modulation optimization,” IEEE Transactions Wireless

Communications, Vol. 4, No. 5, pp. 2349–2360, 2005.

[6] S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-efficiency

of MIMO and cooperative MIMO techniques in sensor

networks,” IEEE Journal on Selected Areas in Communi-

cations, Vol. 22, No. 6, pp. 1089–1098, 2004.

[7] J. Goldsmith and S. B. Wicker, “Design challenges for

energy constrained ad hoc wireless networks,” IEEE

Wireless Communications, Vol. 9, No. 4, pp. 8–27, 2002.

[8] M. De Sanctis, E. Cianca, and M. Ruggieri, “Energy

efficient transmit power control for HDR WPAN,” in

Proceeding’06 17th IEEE International Symposium on

Personal, Indoor and Mobile Radio Communications,

PIMRC’06, pp. 1–5, 2006.

[9] Howitt and J. Wang, “Energy efficient power control

policies for the low rate WPAN,” in Proceeding’04 IEEE

Conference on Sensor and Ad Hoc Communications and

Networks, SECON’04, pp. 527–536, 2004.

[10] G. M. Geoffrey, A. H. Jennifer, and J. D. Robert, “A

sensor network cross-layer power control algorithm that

Copyright © 2010 SciRes. WSN