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Distributed De La Garza algorithm for load-balancing routing in wireless sensor networks

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Abstract

Large scale wireless sensor networks raise many challenges in the design of efficient and effective routing algorithm due to their complexity and hardware constraints. However, the scalability challenge may be mitigated from a macroscopic perspective. One example is the distributed De la Garza iteration (DDLGI) algorithm for global routing load-balancing, based on a set of partial differential equations iteratively solved by the De la Garza method. We theoretically analyze the parallelism of DDLGI and illustrate that the region of interest may impact the degree of parallelism and error. Furthermore, though DDLGI always converges, the slow convergence and long-range information exchange problems may lead to excess energy consumption in communication. Thus, we propose various enhanced De la Garza routing (E-DLGR) algorithms to alleviate the energy consumption problem by which nodes may exchange less information and only need to exchange information with closer nodes to complete each iteration. Our theoretical analysis and simulation results show that the proposed E-DLGR algorithms may have less transmission overhead, thus further reducing energy consumption, and converge faster while still maintaining adequate accuracy.

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Notes

  1. The definition domain of \(\varPhi \) should be large enough to cover A. We distinguish A and the definition domain of \(\varPhi \) to integrate the two PDEs of (2) into one system of linear equations (10) via \(\delta _{i,j}\) [17].

  2. Though \(\widetilde{\varvec{C}}\) is not invertible, GSI (and its derived routing algorithms including DGSI, DDLGI, and E-DLGR) may still converge for (9). Thus, using GSI to solve (10) and (9) may obtain the same load-balancing routing vector field.

  3. More precisely, the forward sweep begins from the grid points called TL-initiators which have no up and left adjacent grid points.

  4. Via \(\hbox {PRECISE}\) referring to Sect. 4.1.

  5. Via \(\hbox {DONE}\) referring to Sect. 4.1.

  6. The grid points with the same value of \({\mathcal{O}}_{L}^{\scriptscriptstyle DG}\) may update their \(\varPhi \)s simultaneously.

  7. Since DDLGI, E-DLGR1, and E-DLGR2 maintain the lexicographical order of updating \(\varPhi \)s via (13) or equivalently (16); thus, the routing vector fields obtained from these three approaches are same.

  8. Each ROI is divided into \(20 \times 20\) grids. Thus each ROI has \(21 \times 21\) grid points, and some grid points are not in the ROI.

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Acknowledgments

This work is supported by the National Science Council, Taiwan, under grant NSC 102-2221-E-194-035.

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  1. Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-Yi, 621, Taiwan

    Jun-Yun Zheng & Ren-Song Ko

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  1. Jun-Yun Zheng
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Correspondence to Ren-Song Ko.

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Zheng, JY., Ko, RS. Distributed De La Garza algorithm for load-balancing routing in wireless sensor networks. Wireless Netw 21, 297–314 (2015). https://doi.org/10.1007/s11276-014-0771-5

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