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Poisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely. The authors propose an improved multi‐target extended tracking scheme GCI‐GGIW‐PMBM (Generalised Covariance Intersection GGIW‐PMBM) for the authors’ real‐time large‐scale aquaculture tracking sensor network. The detailed recursion and closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Comprehensive experiments are presented to verify the effectiveness of the proposed method.

Localization is one of the essential problems in wireless sensor applications (WSNs). Most range‐free localization schemes for mobile WSNs are based on the Sequential Monte Carlo (SMC) algorithm. Multiple iterations, sample impoverishment and less sample diversity, leading to low localizing efficiency, are the most usual problems demanding to be solved in these SMC‐based methods. An improved localization scheme for mobile aquaculture WSNs based on the Improving Dynamic Population Monte Carlo Localization (I‐DPMCL) method is proposed. A population of probability density functions is proposed to approximate the unknown location distribution based on a set of observations through an iterative mixture importance sampling procedure, accompanied by node dynamic behaviours being analysed quantitatively or definitely. Threefold constrain rules are put forward in the I‐DPMCL scheme to decrease the iteration number and trade off iteration number and enough valid samples to obtain the optimum iteration number. Then, these localization behaviours, especial delay, are predicted based on the statistical point of view. Moreover, performance comparisons of I‐DPMCL with other SMC‐based schemes are also proposed. Simulation results show that delay of I‐DPMCL has some superiority to those of other schemes, and accuracy and energy consumption are improved in some cases of lower mobile velocity. The I‐DPMCL scheme is elaborately proposed with a mixture weight sampling, combined with three constraint rules. Node dynamic behaviours are analysed quantitatively or definitely, accompanied by the HTC scheme. Compared to the population Monte Carlo localization (PMCL) scheme, we put emphasis on node dynamic behaviours. Localization behaviours such as errors, delay and energy consumption are validated. Comprehensive performance comparisons between the I‐DPMCL algorithm and other SMC‐based schemes are proposed.

The hidden-terminal problem and routing establishment or maintenance bring about a tremendous amount of energy consumption in multi-hop wireless sensor networks (WSNs). In this study, the authors propose a carrier sense multiple access with collision avoidance (CSMA/CA) scheme MultiCSMA for multi-hop WSNs adopting two strategies which refer to a novel hidden-terminal list algorithm to avoid the impact of hidden terminals and a parallel access scheme to dispense with taking the routing overhead into account. The accurate statistical performance metrics of throughput and delay of unsaturated, unacknowledged IEEE 802.15.4 beacon enabled networks for one-hop and two-hop scenarios are predicted based on Markov models adopting these two improved strategies, in which nodes are assumed to locate randomly over a circle plane according to Poisson distribution. Comprehensive simulations demonstrate that the analysis results match well with the simulation results, and delay performance of the authors schemes is superior to those of other schemes, whereas throughput is superior to others for relative higher node density.