All of the information gathered by the sensor nodes in wireless sensor networks (WSNs) is sent to a sink node. As a result, the location of the sink node greatly affects the amount of energy used and the lifespan of WSNs. This study examines the placement methods for sink nodes that are lifetime- and energy-oriented in single-hop and multiple-hop WSNs, respectively. The lifetime-oriented method places a far greater emphasis on the lifespan of the nodes that consume energy at the fastest rate than the energy-oriented strategy, which solely takes into account lowering network energy consumption overall. The aim is to investigate the effectiveness of an artificial intelligence based solution to the problem of identifying the optimal position of the sink node. The objective is to reduce the overall energy spent in data transmission and increase the network life time. The optimal location of a sink node will be determined using a gradient boosted regression model. The location of the sink node is estimated with reference to the nodes present in the network. It is assumed that the lifetime of the network completes immediately when the first node fails.

wireless sensor networks, sink node, energy consumption, gradient boosted regression, network lifetime,