GWO-GA Based Load Balanced and Energy Efficient Clustering Approach for WSN

  • Amruta LipareEmail author
  • Damodar Reddy Edla
  • Ramalingaswamy Cheruku
  • Diwakar Tripathi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)


Energy consumption of sensor nodes is one of the major challenges in wireless sensor networks (WSNs). Therefore, to defeat this challenge clustering technique is used. In cluster based WSN, the leader of cluster, called cluster head (CH) collects, aggregates, and sends data to the base station. Hence, data load balancing is also one of the crucial tasks in WSN. To overcome this problem, we use two bio-inspired algorithms for clustering namely Grey Wolf Optimization (GWO) and Genetic Algorithm (GA). The best fitted solutions from GWO and GA undergo the crossover and mutation operations to produce healthy off-springs. The clustering solution obtained from GWO-GA is well load balanced and energy efficient. We compare GWO-GA approach with some of the existing algorithms over fitness values and different network parameters namely first sensor node dies and half of the sensor nodes are alive in the network. We observe GWO-GA outperforms existing algorithms.


Grey wolf optimization Genetic algorithm Wireless sensor networks Energy efficiency Load balancing 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Amruta Lipare
    • 1
    Email author
  • Damodar Reddy Edla
    • 1
  • Ramalingaswamy Cheruku
    • 2
  • Diwakar Tripathi
    • 3
  1. 1.National Institute of TechnologyGoaIndia
  2. 2.Mahindra Ecole CetraleHyderabadIndia
  3. 3.Madanapalle Institute of Technology & ScienceMadanapalleIndia

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