Abstract
In mobile wireless sensor networks, linear network coding has been shown to improve the performance of network throughput and reduce delay. However, the linear dependence of coded packets caused by linear network coding will result in significant instability of the entire network during data transmission. Moreover, current linear network coding schemes do not distinguish between the real-time requirements of different packets. To solve the above problems, a weighted Vandermonde echelon fast coding scheme is proposed based on an analysis of various coding schemes such as random and linear network coding. Weighted Vandermonde echelon fast coding scheme can reduce the dependence problem of the network coding matrix and distinguish between different packet priorities. The accuracy of the proposed weighted Vandermonde echelon fast coding method is investigated through a theoretical analysis and then its outstanding delivery performance is verified through discrete event simulation experiments.
Highlights
Compared to mature Internet technologies, a mobile wireless sensor network (MWSN)[1] has numerous inherent uncertainties and problems that must be addressed, especially the problem of unstable network transmission caused by node mobility
In section ‘‘The weighted Vandermonde echelon fast coding scheme (WVEFC) algorithm,’’ we propose the fast coding algorithm called WVEFC to improve the robustness of MWSNs
This article studied the network instability caused by the linear dependence of a coding matrix during network coding in MWSN environments; several coding methods were selected for comparison
Summary
Compared to mature Internet technologies, a mobile wireless sensor network (MWSN)[1] has numerous inherent uncertainties and problems that must be addressed, especially the problem of unstable network transmission caused by node mobility. Redundancy strategies have been adopted in several optimized schemes[3,4,5] by which in-network nodes generate and transmit additional coded packets In this way, these schemes ensure that a destination node can supplement the width of the decoding coefficient matrix by substituting these linear dependent vectors. In other words, when we use an n 3 n coding matrix with its value extracted from a Galois field GF(2M ) (m = 4, n = 3) to conduct a random linear coding operation, the probability of generating linearly dependent coded coefficient vectors is consistent with using an n 3 2n expanded coding matrix (where the elements are drawn from the same Galois field).
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