An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.

The node localization in Wireless Sensor Network (WSN) presently plays an important role in the field of applications. Particle Swarm Optimization (PSO) algorithm is a typical swarm intelligence method. Researchers propose many PSO variants and try to apply PSO algorithm to the related problems in WSN. This paper focuses on the WSN node localization using PSO algorithm. This paper conducts the experiment simulation, comparison and evaluation work in the node localization using PSO algorithm. The performance of different PSO variants with different population topologies is analyzed. Experiment simulations show that WSN node localization using PSO algorithm can get good performance in ring topology and square topology. In particular, the two newly proposed PSO variants (GDPSO and LDPSO) have good performance on this problem. This paper proposes some useful conclusions, which will provide a valuable reference to WSN engineering field.

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