This article combines the Gaussian mixture model with the extended propagation single particle filtering technique (ESPT) and proposes a Gaussian mixture distributed particle filtering algorithm that uses ESPT for state propagation. The algorithm is implemented through the average consensus algorithm in both distributed and iterative modes to improve computational efficiency. In this algorithm, the posterior probability is approximated as a Gaussian mixture model, and the fusion of the Gaussian mixture is achieved through importance sampling. Meanwhile, the use of ESPT effectively reduces computational complexity. Numerical examples further demonstrate that compared to other distributed particle filtering algorithms, this method also has significant performance advantages
Underwater sensor networks (USNs) have different characteristics from ground based wireless sensor networks (WSNs), making traditional WSN protocols unsuitable for uasn. In addition, energy issues directly affect the lifespan of the entire sensor network. The goal of this study is to transmit data to sink nodes in a timely and efficient manner when node resources are limited. Therefore, a reliable and scalable routing protocol EA-VBF for underwater sensor networks is proposed. The innovation of the protocol lies in utilizing the location information and remaining energy of intermediate nodes to make decisions on data forwarding. In addition, the number of times a node relays data packets within a cycle time is considered as a factor in determining. This is the novelty of this article. By introducing energy warning values and improving forwarding factors, the EA-VBF protocol reduces the energy cost of the network and balances the overall energy consumption of the network at the cost of a smaller packet delivery rate
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