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

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.