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Mobile Networks and Applications
Published in: Mobile Networks and Applications 6/2019

12-09-2019

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

Authors: Xueqiang Yin, Shining Li, Yun Lin

Published in: Mobile Networks and Applications | Issue 6/2019

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Abstract

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.
previous article Joint Time and Node Optimization for Cluster-Based Energy-Efficient Cognitive Internet of Things
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Metadata
Title
A Novel Hierarchical Data Aggregation with Particle Swarm Optimization for Internet of Things
Authors
Xueqiang Yin
Shining Li
Yun Lin
Publication date
12-09-2019
Publisher
Springer US
Published in
Mobile Networks and Applications / Issue 6/2019
Print ISSN: 1383-469X
Electronic ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-019-01333-4

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