A Distributed Framework for Correlated Data Gathering in Sensor Networks

We consider the problem of correlated data gathering in sensor networks with multiple sink nodes. The problem has two objectives. First, we would like to find a rate allocation on the correlated sensor nodes such that the data gathered by the sink nodes can reproduce the field of observation. Second, we would like to find a transmission structure on the network graph such that the total transmission energy consumed by the network is minimized. The existing solutions to this problem are impractical for deployment because they have not considered all of the following factors: (1) distributed implementation; (2) capacity and interference associated with the shared medium; and (3) realistic data correlation model. In this paper, we propose a new distributed framework to achieve minimum energy data gathering while considering these three factors. Based on a localized version of Slepian-Wolf coding, the problem is modeled as an optimization formulation with a distributed solution. The formulation is first relaxed with Lagrangian dualization and then solved with the subgradient algorithm. The algorithm is amenable to fully distributed implementations, which corresponds to the decentralized nature of sensor networks. To evaluate its effectiveness, we have conducted extensive simulations under a variety of network environments. The results indicate that the algorithm supports asynchronous network settings, sink mobility, and duty schedules.

[1]  Nael B. Abu-Ghazaleh,et al.  A taxonomy of wireless micro-sensor network models , 2002, MOCO.

[2]  Parameswaran Ramanathan,et al.  Sensor Deployment Strategy for Detection of Targets Traversing a Region , 2003, Mob. Networks Appl..

[3]  Deborah Estrin,et al.  Directed diffusion for wireless sensor networking , 2003, TNET.

[4]  Klara Nahrstedt,et al.  Optimal resource allocation in wireless ad hoc networks: a price-based approach , 2006, IEEE Transactions on Mobile Computing.

[5]  Parameswaran Ramanathan,et al.  Rate feasibility under medium access contention constraints , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[6]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[7]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[8]  Baltasar Beferull-Lozano,et al.  Networked Slepian-Wolf: Theory and Algorithms , 2004, EWSN.

[9]  Stephen P. Boyd,et al.  Simultaneous routing and power allocation in CDMA wireless data networks , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[10]  Christos G. Cassandras,et al.  On maximum lifetime routing in Wireless Sensor Networks , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[11]  RamanathanParameswaran,et al.  Sensor deployment strategy for detection of targets traversing a region , 2003 .

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

[13]  Cauligi S. Raghavendra,et al.  PEGASIS: Power-efficient gathering in sensor information systems , 2002, Proceedings, IEEE Aerospace Conference.

[14]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[15]  Hanif D. Sherali,et al.  Recovery of primal solutions when using subgradient optimization methods to solve Lagrangian duals of linear programs , 1996, Oper. Res. Lett..

[16]  Jack K. Wolf,et al.  Noiseless coding of correlated information sources , 1973, IEEE Trans. Inf. Theory.

[17]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[18]  Roger Wattenhofer,et al.  Gathering correlated data in sensor networks , 2004, DIALM-POMC '04.

[19]  João Barros,et al.  Network information flow with correlated sources , 2006, IEEE Transactions on Information Theory.

[20]  Deborah Estrin,et al.  Modelling Data-Centric Routing in Wireless Sensor Networks , 2002 .

[21]  Zygmunt J. Haas,et al.  Virtual backbone generation and maintenance in ad hoc network mobility management , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[22]  Bhaskar Krishnamachari,et al.  Optimal information extraction in energy-limited wireless sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[23]  Deborah Estrin,et al.  Simultaneous Optimization for Concave Costs: Single Sink Aggregation or Single Source Buy-at-Bulk , 2003, SODA '03.

[24]  Konstantinos Kalpakis,et al.  Efficient algorithms for maximum lifetime data gathering and aggregation in wireless sensor networks , 2003, Comput. Networks.

[25]  Mahdi Lotfinezhad,et al.  Effect of partially correlated data on clustering in wireless sensor networks , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[26]  Toby Berger,et al.  Rate distortion theory : a mathematical basis for data compression , 1971 .

[27]  Deborah Estrin,et al.  The impact of data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

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

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

[30]  ChangJae-Hwan,et al.  Maximum lifetime routing in wireless sensor networks , 2004 .

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

[32]  Haiyun Luo,et al.  A new model for packet scheduling in multihop wireless networks , 2000, MobiCom '00.

[33]  Baltasar Beferull-Lozano,et al.  On network correlated data gathering , 2004, IEEE INFOCOM 2004.