EDAC: A Novel Energy-Aware Clustering Algorithm for Wireless Sensor Networks

Clustering is a useful technique for reducing energy consumption in wireless sensor networks (WSN). To achieve a better network lifetime performance, different clustering algorithms use various parameters for cluster head (CH) selection. For example, the sensor's own residual energy as well as the network's total residual energy are used. In this paper, we propose an energy-distance aware clustering (EDAC) algorithm that incorporates both the residual energy levels of sensors within a cluster radius as well as the distances. To achieve this, we define a metric that is calculated at each sensor based on local information within its neighborhood. This metric is incorporated within the CH selection probability. Using this metric, one can choose the sensors with low residual energy levels to have the greatest impact on CH selection which results in CH selection being biased to be close to these sensors. This results in reducing their communication energy cost to the CH. Simulation results indicate that our proposed EDAC algorithm outperforms both the LEACH and the energy-efficient DEEC protocols in terms of network lifetime.

@article{Ababneh2016,
  title     = {EDAC: A Novel Energy-Aware Clustering Algorithm for Wireless Sensor Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {5},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Ahmad A. Ababneh and Ebtessam Al-Zboun},
  doi       = {10.14569/IJACSA.2016.070545},
  url       = {https://doi.org/10.14569/IJACSA.2016.070545}
}