Abstract: Wireless Sensor Networks (WSNs) are critically vital in real-time data transmission and acquisition. Unfortunately, they are often afflicted with high latency, because of inefficient approaches for data routing and aggregation. To resolve the above issues, we propose the Geographical Energy-Conscious Data Aggregation Using Mobile Sinks (GEADAMS) Algorithm for maximal data aggregation with minimal delay. GEADAMS invokes geographic information and energy-conscious routing for dynamically choosing rendezvous points so as to reduce long-distance transmission and share energy consumption among sensor nodes. Adding the mobile sinks, introduced by this approach, greatly facilitates efficient data collection in handling data collected from several sources, thus preventing the congestion and thereby ensuring seamless delivery of data. The protocol has been compared with other existing protocols, viz., LEACH, GEAR, and RPM, through performance parameters like throughput, energy consumption, Packet Delivery Ratio (PDR), and Latency. Simulation results confirm that GEADAMS outperforms other approaches under similar testing scenarios with high throughput, energy savings, and low latency working efficiently without compromising on reliability. The novel method increases overall network life through reduced selection of nodes and reduced transmission flooding. The research paves the way for making the WSN even more efficient for high-speed routing in more dynamic environments such as environment monitoring, rescue during disasters, and smart cities. Future work will investigate machine learning approaches that will assist further in improvements in adaptive routing design in dynamic WSN networks.

Key words: data aggregation, energy efficiency, geographical energy, transmission delays, wireless sensor networks