A Novel Hierarchical Data Aggregation with Particle Swarm Optimization for Internet of Things


Due to small battery powered devices and inefficient utilization of resources, the sensor nodes in Internet of Things (IoT) may be lost prematurely. In order to extend the lifetime and avoid energy-hole problem, a novel hierarchical data aggregation with particle swarm optimization for Wireless Sensor Networks (HDA-PSO) is proposed. Firstly, the fitness function is designed from multiple relational matrices, including the residual energy, the average distance among adjacent nodes and the centroid degree of the covering region. Secondly, in order to effectively encircling the optimal solution with initial position of particles, we propose a population initialization method based on beta distribution according to the distribution characteristics of nodes in sensor networks. Next, based on differential evolution, a novel operator is introduced for velocity update which effectively balances the exploration and development of particle swarm optimization. The experimental results show that the proposed algorithm can effectively balance the energy consumption of nodes under different node’s density, improve the energy efficiency and prolong the lifetime of network significantly.

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Xueqiang Yin conceived the study and performed the simulation experiments and wrote the paper. Shining Li reviewed and edited the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shining Li.

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Yin, X., Li, S. & Lin, Y. A Novel Hierarchical Data Aggregation with Particle Swarm Optimization for Internet of Things. Mobile Netw Appl 24, 1994–2001 (2019). https://doi.org/10.1007/s11036-019-01333-4

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  • Internet of things
  • Particle swarm optimization
  • Hierarchical data aggregation,wireless sensor networks