An innovative framework for balanced cluster‐based data aggregation in sensor networks. (24th May 2022) - Jain, Khushboo; Kumar, Anoop
Summary: The essential design concern of a sensor network is to balance energy consumption of sensor nodes (SNs) to prolong the network lifetime. Many research works cited that clustering techniques efficiently utilize the network's energy resource by organizing SNS into groups of clusters and benefits in reduced data transmission. An extensively used category of cluster‐based protocols is the probabilistic clustering technique in which a preset optimum likelihood is used to facilitate the selection procedure of cluster head (CH). These clustering techniques suffer from non‐uniform dissemination of CH, which leads to uneven load balance and uneven energy consumption of SNs during network activities like data transmitting and data receiving. This causes an energy‐hole problem and reduces network lifetime. In order to solve these issues, we have focused on to design a balanced cluster‐based data aggregation and formulate a method that increases the energy efficiency of probabilistic clustering techniques by optimizing the number of clusters and the dissemination of CHs in the sensor network. The simulation analysis proves that the proposed technique accomplishes significantly enhanced than the existing works. Abstract : An innovative framework for balanced cluster‐based data‐aggregation (SOPCH) is proposed by introducing two improvements in the existing Probabilistic based Energy‐Efficient Single‐hop Clustering Technique (EESCT) to guarantee the optimum number of clusters and well‐dispersed CHs across the sensor network in each communication round and by estimating and selecting proximity parameter (φ) and augment threshold parameter (Χ) for uniform distribution of CHs to balance the sensor system's energy depletion. Sustaining the optimum proportion of CH in each round of communication to balance the energy depletion of all SNs and thus reduce overall energy consumption to prolong the total sensor lifetime. Estimating and selecting proximity parameter φ and augment threshold parameter χ for uniform distribution of CHs to balance the sensor system's energy depletion. Integration of the proposed technique SOPCH for data aggregation in the existing cluster‐based sensor network.