Toward Optimal Data Aggregation in Random Wireless Sensor Networks

Data gathering is one of the most important services provided by wireless sensor networks (WSNs). Since the predominant traffic pattern in data gathering services is many-to-one communication, it is critical to understand the limitations of many-to-one information flows and devise efficient data aggregation protocols to support prolonged operations in WSNs. In this paper, we provide a theoretical characterization of data aggregation processes under different communication modalities in WSNs. We demonstrate that data aggregation rates of Theta(log(n)/n) and Theta(1) are optimal when operating in fading environments with power path-loss exponents that satisfy 2 < alpha < 4 and alpha > 4, respectively. Furthermore, the optimal rate can be achieved using a generalization of cooperative beam-forming called cooperative time-reversal communication. In contrast, the non-cooperative multihop relay strategies widely adopted in literature are shown to be suboptimal in the low-to-medium attenuation regime (for 2 < alpha < 4).

[1]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[2]  Panganamala Ramana Kumar,et al.  A network information theory for wireless communication: scaling laws and optimal operation , 2004, IEEE Transactions on Information Theory.

[3]  Panganamala Ramana Kumar,et al.  A cautionary perspective on cross-layer design , 2005, IEEE Wireless Communications.

[4]  Piyush Gupta Design and Performance Analysis of Wireless Networks , 2000 .

[5]  Daryl J. Daley,et al.  An Introduction to the Theory of Point Processes , 2013 .

[6]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[7]  David Tse,et al.  Mobility increases the capacity of ad hoc wireless networks , 2002, TNET.

[8]  R.J. Barton,et al.  Order-Optimal Data Aggregation in Wireless Sensor Networks Using Cooperative Time-Reversal Communication , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[9]  Hesham El Gamal,et al.  On the scaling laws of dense wireless sensor networks: the data gathering channel , 2005, IEEE Transactions on Information Theory.

[10]  Panganamala Ramana Kumar,et al.  Computing and communicating functions over sensor networks , 2005, IEEE Journal on Selected Areas in Communications.

[11]  P. R. Kumar,et al.  Internets in the sky: The capacity of three-dimensional wireless networks , 2001, Commun. Inf. Syst..

[12]  M. Penrose,et al.  Large deviations for discrete and continuous percolation , 1996, Advances in Applied Probability.

[13]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[14]  Jennifer C. Hou,et al.  Capacity of wireless ad-hoc networks under ultra wide band with power constraint , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[15]  Mingyan Liu,et al.  On the Many-to-One Transport Capacity of a Dense Wireless Sensor Network and the Compressibility of Its Data , 2003, IPSN.

[16]  M. Penrose The longest edge of the random minimal spanning tree , 1997 .

[17]  Rong Zheng,et al.  Information Dissemination in Power-Constrained Wireless Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[18]  Massimo Franceschetti,et al.  On the throughput scaling of wireless relay networks , 2006, IEEE Transactions on Information Theory.

[19]  Massimo Franceschetti,et al.  On the throughput capacity of random wireless networks , 2004 .