The n-Hop Multilateration Primitive for Node Localization Problems

The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. In this paper we study node localization, a component technology that would enhance the effectiveness and capabilities of this new class of networks. The n-hop multilateration primitive presented here, enables ad-hoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global non-linear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large non-linear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows fine-grained localization to run on a low cost sensor node we have developed.

[1]  Gaurav S. Sukhatme,et al.  Relaxation on a mesh: a formalism for generalized localization , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[2]  Srdjan Capkun,et al.  GPS-free Positioning in Mobile Ad Hoc Networks , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[3]  Martin Mauve,et al.  A survey on position-based routing in mobile ad hoc networks , 2001, IEEE Netw..

[4]  L. El Ghaoui,et al.  Convex position estimation in wireless sensor networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[5]  Michael Harrington,et al.  Constellation: a wide-range wireless motion-tracking system for augmented reality and virtual set applications , 1998, SIGGRAPH.

[6]  Mani B. Srivastava,et al.  A distributed computation platform for wireless embedded sensing , 2002, Proceedings. IEEE International Conference on Computer Design: VLSI in Computers and Processors.

[7]  Stergios I. Roumeliotis,et al.  Synergetic localization for groups of mobile robots , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[8]  Mani B. Srivastava,et al.  On the Error Characteristics of Multihop Node Localization in Ad-Hoc Sensor Networks , 2003, IPSN.

[9]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[10]  Elliott D. Kaplan Understanding GPS : principles and applications , 1996 .

[11]  Jan M. Rabaey,et al.  Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks , 2002, USENIX Annual Technical Conference, General Track.

[12]  Mani B. Srivastava,et al.  Dynamic fine-grained localization in Ad-Hoc networks of sensors , 2001, MobiCom '01.

[13]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[14]  Miodrag Potkonjak,et al.  Exposure in wireless Ad-Hoc sensor networks , 2001, MobiCom '01.

[15]  Satish Kumar,et al.  Next century challenges: scalable coordination in sensor networks , 1999, MobiCom.

[16]  Miodrag Potkonjak,et al.  Smart kindergarten: sensor-based wireless networks for smart developmental problem-solving environments , 2001, MobiCom '01.

[17]  Deborah Estrin,et al.  Robust range estimation using acoustic and multimodal sensing , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).