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Research on Improved Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks

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Abstract

In wireless sensor networks (WSNs), due to the limited battery power of the sensor nodes, the communication energy consumption is the main factor to affect the lifetime of the networks. A reasonable design of the communication protocol can effectively reduce the energy consumption of the network system. Based on low-energy adaptive clustering hierarchy (LEACH), an improved LEACH protocol in WSNs is proposed. In order to optimize the cluster head (CH) election in the cluster setup phase, the improved LEACH takes into account a number of factors, including energy consumption of communication between nodes, remaining energy of the nodes, and the distance between nodes and base station (BS). In the steady phase, one-hop routing and multiple-hop routing are combined to transmit data between CHs to improve energy efficiency. The forward CH is selected as relay node according to the values of path cost. The simulation results show that the proposed algorithm performs better in balancing network energy consumption, and it can effectively improve the data transmission efficiency and prolong the network lifetime, as compared with LEACH, LEACH-C (LEACH-centralized) and NDAPSO-C (an adaptive clustering protocol based on improved particle swarm optimization) algorithms.

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Ying Zhang  (张颖), Peisong Li  (李培嵩) & Lin Mao  (毛林)

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  1. Ying Zhang  (张颖)
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  2. Peisong Li  (李培嵩)
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  3. Lin Mao  (毛林)
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Corresponding author

Correspondence to Ying Zhang  (张颖).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 61673259), and the International Exchanges and Cooperation Projects of Shanghai Science and Technology Committee (No. 15220721800)

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Zhang, Y., Li, P. & Mao, L. Research on Improved Low-Energy Adaptive Clustering Hierarchy Protocol in Wireless Sensor Networks. J. Shanghai Jiaotong Univ. (Sci.) 23, 613–619 (2018). https://doi.org/10.1007/s12204-018-1991-0

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  • DOI: https://doi.org/10.1007/s12204-018-1991-0

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