Bivariate Regression Adaptive Wald’s Boost Energy Aware Routing for Wsn with IoT - et. al., G.Sathishkumar, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
WSN is a wireless network where the low powered devices known as sensor nodes which are deployed over the network to measure the environmental conditions. IoT is a model that connects WSN and the Internet through a medium depends on wireless Internet technology sensors. However, the large-scale acceptance of WSN for IoT is still facing the major challenges of routing. In order to improve the routing in WSN, an efficient technique called Bivariate Regressed Adaptive Wald’s Boost Energy Aware Routing (BRAWBEAR) is introduced. The main aim of BRAWBEAR technique is to improve the data delivery with minimum delay as well as routing overhead. Initially, the energy of the each sensor nodes is measured to improve the energy efficient routing in WSN. The Adaptive Wald’s Boost ensemble technique initially comprises the weak learners as a bivariate regression tree with the training samples as sensor nodes. The regression tree analyzes the node energy level with the threshold and categorizes the node into two classes such as higher energy and lesser energy. The Adaptive Wald’s Boost ensemble technique combines the weak learner results into make a strong one resulting increases the accurate sensor node classification with minimum error. After that, the route path discovery is carried out with the higher energy nodes and the sensor nodes with lesser energy are removed. The source node finds the neighboring energy efficient node by applying the time of arrival method. Then the route path is established from source to sink node via neighboring nodes. Followed by, the data packets are transmitted along the route path. Finally, the route maintenance is carried out when the link failure occurs by selecting the alternative energy efficient neighboring node to improve the data packet delivery with minimum time. The performance of the proposed BRAWBEAR technique is estimated using the metrics namely, energy consumption, packet delivery ratio, routing overhead, throughput and end to end delay. The comparative results discussion provides the improved performance in terms of minimum routing overhead and higher packet delivery as well as minimum energy consumption than the other well-known routing methods.