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MAP: Medial axis based geometric routing in sensor networks

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Abstract

One of the challenging tasks in the deployment of dense wireless networks (like sensor networks) is in devising a routing scheme for node to node communication. Important consideration includes scalability, routing complexity, quality of communication paths and the load sharing of the routes. In this paper, we show that a compact and expressive abstraction of network connectivity by the medial axis enables efficient and localized routing. We propose MAP, a Medial Axis based naming and routing Protocol that does not require geographical locations, makes routing decisions locally, and achieves good load balancing. In its preprocessing phase, MAP constructs the medial axis of the sensor field, defined as the set of nodes with at least two closest boundary nodes. The medial axis of the network captures both the complex geometry and non-trivial topology of the sensor field. It can be represented succinctly by a graph whose size is in the order of the complexity of the geometric features (e.g., the number of holes). Each node is then given a name related to its position with respect to the medial axis. The routing scheme is derived through local decisions based on the names of the source and destination nodes and guarantees delivery with reasonable and natural routes. We show by both theoretical analysis and simulations that our medial axis based geometric routing scheme is scalable, produces short routes, achieves excellent load balancing, and is very robust to variations in the network model.

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Authors and Affiliations

  1. Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA

    Jehoshua Bruck

  2. Computer Science Department, Stony Brook University, Stony Brook, NY, 11794, USA

    Jie Gao

  3. Computer Science Department, Texas A&M University, College Station, TX, 77843-3112, USA

    Anxiao (Andrew) Jiang

Authors
  1. Jehoshua Bruck
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  2. Jie Gao
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  3. Anxiao (Andrew) Jiang
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Corresponding author

Correspondence to Jehoshua Bruck.

Additional information

A preliminary version appeared in ACM International Conference on Mobile Computing and Networking (MobiCom’05), August, 2005. This work was supported in part by the Lee Center for Advanced Networking at the California Institute of Technology, and by NSF grant CCR-TC-0209042.

Jie Gao’s work was done at Center for the Mathematics of Information, California Institute of Technology, Pasadena, CA 91125.

Anxiao (Andrew) Jiang’s work was done at Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125.

Jehoshua Bruck is the Gordon and Betty Moore Professor of Computation and Neural Systems and Electrical Engineering at the California Institute of Technology. During 2003–2005 he served as the founding Director of Caltech's Information Science and Technology (IST) program.

He received the B.Sc. and M.Sc. degrees in electrical engineering from the Technion, Israel Institute of Technology, in 1982 and 1985, respectively and the Ph.D. degree in Electrical Engineering from Stanford University in 1989.

His research combines work on the design of distributed information systems and the theoretical study of biological circuits and systems.

Dr. Bruck has an extensive industrial experience, including working with IBM Research for ten years where he participated in the design and implementation of the first IBM parallel computer. He was a co-founder and chairman of Rainfinity (acquired in 2005 by EMC), a spin-off company from Caltech that focused on software products for management of network information systems.

He is an IEEE fellow, and his awards include the National Science Foundation Young Investigator award and the Sloan fellowship. He published more than 200 journal and conference papers and he holds 25 US patents. His papers were recognized in journals and conferences, including, winning the 2005 S. A. Schelkunoff Transactions prize paper award from the IEEE Antennas and Propagation society and the 2003 Best Paper Award in the 2003 Design Automation Conference.

Jie Gao received her Ph.D in computer science from Stanford University in 2004, and her BS degree from University of Science and Technology of China in 1999. She is currently an assistant professor at Computer Science department, State University of New York, Stony Brook. Her research interests include algorithms, ad hoc communication and sensor networks, and computational geometry.

Anxiao (Andrew) Jiang received the B.S. degree with honors in 1999 from the Department of Electronic Engineering, Tsinghua University, Beijing, China, and the M.S. and Ph.D. degrees in 2000 and 2004, respectively, from the Department of Electrical Engineering, California Institute of Technology. He is currently an assistant professor in the Department of Computer Science, Texas A&M University.

He was a recipient of the four-year Engineering Division Fellowship from the California Institute of Technology in 1999. His research interests include algorithm design, ad hoc communication and sensor networks, and file storage and retrieval.

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Bruck, J., Gao, J. & Jiang, A.(. MAP: Medial axis based geometric routing in sensor networks. Wireless Netw 13, 835–853 (2007). https://doi.org/10.1007/s11276-006-9857-z

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