Deep Learning and Evolutionary Model for Energy Efficient Node Localization in WSN

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@article{1704894,
      author = {Nagaraj C., Dr. P. Prabhusundhar},
      title = {Deep Learning and Evolutionary Model for Energy Efficient Node Localization in WSN},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {353-363},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704894.pdf},
      abstract = {Being aware of the nodes positions is a key issue in order to locate precisely the sensor node, localization is very important information about sensor nodes in wireless sensor network (WSNs). Hence, the precision improvement is a significant issue that allows an effective data transmission between sensor network (SN) in order to save their energy and extend the network lifetime. In this work, propose and implement a new mechanism for routing. In this work node localization is performed using Improved recurrent neural network (IRNN). Once the localization algorithm has detected the location of nodes with unknown position, the proposed mechanism selects effectively the next-elected CH to reduce the energy dissipation of sensor nodes using mutation chicken swarm optimization, which leads to an extension of the network lifetime. The main advantages of the proposed mechanism are three folds: the first is to minimize the position error of nodes and reduces the error localization average. The second is to increase the number of packets transmitted to the next hop cluster head (CH) based on the localization algorithm. The third one is to, reduce the energy consumption of nodes and then extends the network lifetime using an efficient selection of next hop CH. The obtained simulation results show that the proposed mechanism outperforms the existing solutions in terms of energy consumption, execution time (localization time) and localization error, similarly for the number of the packets transmitted to the base station. Experimental results show the effectiveness of the proposed model in terms of packet delivery ratio, energy consumption, execution time and localization error.},
      keywords = {Deep Learning, Energy -Efficient, Wireless Sensor Network (WSNs), Improved Recurrent Neural Network (IRNN), Chicken Swarm Optimization (CSO)},
      month = {July},
  }