使用無線傳輸之廣播特性以降低機器對機器網路之傳輸量

機器對機器網路為未來可能實現人類智慧型生活的科技，且存在著相較於今日十倍至百倍的通訊設備。因此，如何在同樣無線通訊頻譜的情況下，支持如此大量的無線通訊，將是機器與機器網路中一關鍵問題。可行的解決方法之一為辨別出網絡中多餘的傳輸訊號並減少傳輸量。我們注意到，無線感測器傳輸為機器對機器網路中之最主要的無線傳輸之ㄧ，且無線感測器網路中訊號有高度相關，因此我們提出一利用無線傳輸之廣播特性，使用訊號間之相關性以降低傳輸量的演算法。在給定一估計失真水平之下，此演算法一方面根據無線通道狀況及網路結構提供最佳的訊號合成方法，也判斷出最少的無線感測器子網路結構而達到所要求的估計水平。由電腦模擬數值結果，可看出一個失真界線的存在，若要求一個估計失真低於此失真界線，則此演算法顯著地降低需要的無線感測器數量，進而降低機器對機器網路中的傳輸量及感測器布置成本。隨著所要求估計失真的降低，此演算法能夠降低的傳輸量比例亦大幅增高。在某些狀況下，此演算法能夠省去90%以上的網路傳輸量。

關鍵字

機器與機器網路；廣播；訊號相關性；降低傳輸量

並列摘要

In Machine-to-Machine (M2M) networks, where 10-100 times number of commu- nication devices comparing to today coexist, spectrum insufﬁciency is a critical problem. How to support such number of communication devices becomes the major challenge in M2M networks. One potential solution is to identify redundant signals and reduce the transmission trafﬁc in the network. Since M2M network is consisted of sensors and M2M devices, and we note that there are high correlations among sensor signals, hence we propose a mechanism that utilizes the broadcast nature of wireless communication and the signal correlations between sensors, to reduce trafﬁc. The mechanism provides optimal fusion rules and topology reduction algorithm such that, depending on channel conditions, the system can dynamically turn on necessary sensors to achieve a desired estimation quality. Performance of the mechanism is measured as number of sensors be reduced, when an estimation quality is satisﬁed. Simulation results reveal a permissible distortion level exists such that, when we require estimator distortion lower than the permissible level, overhearing significantly reduces transmission trafﬁc. Speciﬁcally, the developed trafﬁc reduction mechanism can save over 90% trafﬁc under certain channel conditions.

並列關鍵字

Machine to Machine ； M2M network ； Broadcast ； Overhearing ； Signal Correlation ； Traffic Reduction

參考文獻

[1] S.-Y. Lien, K.-C. Chen, and Y. Lin, “Toward ubiquitous massive accesses in 3gpp machine-to-machine communications,” IEEE Communication Magazine, vol. 49, no. 4, April 2011.

[2] Y. Zhang, R. Yu, S. Xie, W. Yao, Y. Xiao, and M. Guizani, “Home m2m networks: Architectures, standards, and qos improvement,” IEEE Communication Magazine, vol. 49, no. 4, April 2011.

[4] D. Niyato, L. Xiao, and P. Wang, “Machine-to-machine communications for home energy management system in smart grid,” IEEE Communication Magazine, vol. 49, no. 4, April 2011.

[5] M. C. Vuran and I. F. Akyildiz, “Spatial correlation-based collaborative medium access control in

[6] J. N. Tsitsiklis, “Decentralized detection,” Advances in Statistical Signal Processing, Signal Detection,

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使用無線傳輸之廣播特性以降低機器對機器網路之傳輸量

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