A hierarchical transport architecture for wireless sensor networks

For practical applications, wireless sensor networks (WSNs) with diverse sensor types and that run heterogenous applications are becoming increasingly important. Our motivation in this work is to, for this sort of heterogenous WSNs, devise a flow control and resource allocation algorithm (with respect to both wireless channel usage and sensor node energy), that allow data to be gathered in the fairest manner, while still respecting the needs of different sensing tasks. A two-layer hierarchical transport architecture is designed to guarantee a certain measure of optimality in rate allocation, addressing the balance between fairness and performance. In essence, utility max-min fairness is achieved among upper-layer cluster heads, whereas utility proportional fairness is achieved within each lower-layer cluster. The proposed architecture is to be applied to a real marine sensor network on the Great Barrier Reef.

[1]  Steven H. Low,et al.  A duality model of TCP and queue management algorithms , 2003, TNET.

[2]  Marimuthu Palaniswami,et al.  Sensor Network Implementation Challenges in The Great Barrier Reef Marine Environment , 2008 .

[3]  William J. Skirving,et al.  A comparison of the 1998 and 2002 coral bleaching events on the Great Barrier Reef: spatial correlation, patterns, and predictions , 2004, Coral Reefs.

[4]  Marimuthu Palaniswami,et al.  Application-Oriented Flow Control for Wireless Sensor Networks , 2007, International Conference on Networking and Services (ICNS '07).

[5]  Ellen W. Zegura,et al.  Utility max-min: an application-oriented bandwidth allocation scheme , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[6]  Bo Li,et al.  A survey of transport protocols for wireless sensor networks , 2006, IEEE Network.

[7]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[8]  Yi Shi,et al.  Rate Allocation and Network Lifetime Problems for Wireless Sensor Networks , 2008, IEEE/ACM Transactions on Networking.

[9]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[10]  Miles Furnas,et al.  Catchments and Corals: Terrestrial Runoff to the Great Barrier Reef , 2003 .

[11]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[12]  Mark E. Borsuk,et al.  Integrative environmental prediction using Bayesian networks: A synthesis of models describing estuarine eutrophication , 2002 .

[13]  M. Palaniswami,et al.  Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness , 2006, IEEE/ACM Transactions on Networking.

[14]  Laurent Massoulié,et al.  Bandwidth sharing: objectives and algorithms , 2002, TNET.

[15]  Dimitri P. Bertsekas,et al.  Data Networks , 1986 .

[16]  Charles E. Perkins,et al.  Ad hoc On-Demand Distance Vector (AODV) Routing , 2001, RFC.

[17]  Jiong Jin,et al.  Utility Max-Min Fair Flow Control for Heterogeneous Sensor Networks , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.