Kuangrong Hao

An endocrine cooperative particle swarm optimization algorithm for routing recovery problem of wireless sensor networks with multiple mobile sinks

In the wireless sensor networks with multiple mobile sinks, the movement of sinks or failure of sensor nodes may lead to the breakage of the existing routes. In most routing protocols, the query packets are broadcasted to repair a broken path from source node to sink, which cause significant communication overhead in terms of both energy and delay. In order to repair broken path with lower communication overhead, we propose an efficient routing recovery protocol with endocrine cooperative particle swarm optimization algorithm (ECPSOA) to establish and optimize the alternative path. In the ECPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is enriched by the endocrine mechanism, which can enhance the capacity of global search and improve the speed of convergence and accuracy of the algorithm. By using this method, the alternative path from source nodes to the sink with the optimal QoS parameters can be selected. Simulation results show that our routing protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

Target coverage optimisation of wireless sensor networks using a multi-objective immune co-evolutionary algorithm

Target coverage is an important topic of wireless sensor networks. The target cover can be modelled as a minimal multi-objective vertex cover model with constraint of network connection. In order to search the optimal solution of the target cover set, we propose a multi-objective immune co-evolutionary algorithm (MOICEA) for target coverage. The MOICEA is inspired from the biological mechanisms of immune systems including clonal proliferation, hypermutation, co-evolution, immune elimination and memory mechanism. The affinity between antibody and antigen is used to measure the optimal target cover, and the affinity between antibodies is used to evaluate the diversity of population and to instruct the population evolution process. In order to examine the effectiveness of the MOICEA, we compare its performance with that of integer linear program and genetic algorithm in terms of four objectives while maintaining network connectivity. The experiment results show that the MOICEA can obtain promising performance in efficiently searching optimal vertex set by comparing with other approaches.

An ensemble kernel classifier with immune clonal selection algorithm for automatic discriminant of primary open-angle glaucoma

An ensemble kernel classifier is proposed in this paper by integrating a kernel principal component analysis (KPCA) with a support vector machine (SVM) as well as an immune clonal selection algorithm (ICSA). The KPCA approach is used to extract features, whereas the SVM technique is employed to deal with classification, and the ICSA is applied to optimize the parameters of the proposed scheme. The proposed ensemble classifier can automatically select the kernel type and optimize its parameter sets, in order to produce various SVM classifiers with different kernels. Regardless of whether the data is linear or nonlinear, an optimum classification result can be obtained. In order to demonstrate the effectiveness of the classifier, it is applied to discriminate the primary open-angle glaucoma (POAG) using a standard classification dataset. Experimental results reveal that the proposed ensemble classifier is accurate and more effective when compared to other approaches in the literature. It is envisaged that ensemble kernel classifier could hold a high potential in classification of pattern recognition problems.

Moment stability of fractional stochastic evolution equations with Poisson jumps

Fractional differential equations have wide applications in science and engineering. In this paper, we consider a class of fractional stochastic partial differential equations with Poisson jumps. Sufficient conditions for the existence and asymptotic stability in pth moment of mild solutions are derived by employing the Banach fixed point principle. Further, we extend the result to study the asymptotic stability of fractional systems with Poisson jumps. An example is provided to illustrate the effectiveness of the proposed results.

Adaptive feedback synchronisation of complex dynamical network with discrete-time communications and delayed nodes

This paper considered the synchronisation of continuous complex dynamical networks with discrete-time communications and delayed nodes. The nodes in the dynamical networks act in the continuous manner, while the communications between nodes are discrete-time; that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov–Krasovskii function to govern the characteristics of the discrete communication instants, we investigate the adaptive feedback synchronisation and a criterion is derived to guarantee the existence of the desired controllers. The globally exponential synchronisation can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.

Adaptive design optimization of wireless sensor networks using Artificial Immune Algorithms

The topology control is a very important issue in wireless sensor networks (WSNs). Many approaches have been proposed to carry out in this aspect, including modern heuristic approach. In this paper, the Topology Control based on Artificial Immune Algorithm (ToCAIA) is proposed to solute the energy-aware topology control for WSNs. ToCAIA is a heuristic algorithm, which is heuristic from the immune system of human. In ToCAIA, the antibody is the solution of the problem, and the antigen is the problem. ToCAIA could be used to solve the multi-objective minimum energy network connectivity (MENC) problem, and get the approximate solution. The experiment result shows that the topology control by using ToCAIA can be utilized for WSNs network optimization purposes.

A sparse autoencoder compressed sensing method for acquiring the pressure array information of clothing

In this paper, we integrate some ideas of sparse autoencoder of deep learning into compressed sensing (CS) theory, and set up a sparse autoencoder compressed sensing (SAECS) model, which can improve the compressed sampling process of CS with compression of sparse autoencoder in deep learning. The original CS theory has no function of autonomic regulation, so we introduce the idea of sparse autoencoder of deep learning to improve CS theory. Then we calculate the error between the recovery output data and the input data. By judging the obtained error and the acceptable error, the SAECS model can choose autonomously the most appropriate sparsity and the most appropriate length of measurement vector. This SAECS model can then reconstruct the original signal that can satisfy the acceptable error requirement with the minimum length of measurement vector in CS theory. We investigate the effectiveness of the proposed method by using sampled pressure data from human body model. Experimental results demonstrate that, in comparison with the state-of-the-art methods for running time, our SACES approach can effectively decrease the running time to find the shortest measurement vector in the case of accepted error.