The Impact of Data Aggregation in Wireless Sensor Networks
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Most of the current research in wireless sensor network (WSN) focuses on energy related issues because of the fact that tiny sensor devices possess a limited power supply. Out of the three normally executed sensor's tasks (sense, process and transmit), data transmission consumes most of the power. In this paper, we propose a query-based data aggregation model that is based on the base stations within WSN that query the sensors to transmit their collected data due to special events. The worst-case scenario for a query-based activation would be that all sensors transmit their collected data simultaneously to the base station. This could lead to a loss of data due to the overlapping of transmissions at the base station. Therefore, we have embedded our query-based model within a Monte Carlo Simulator to explore the best- and worst-case scenarios for a base station to initiate its queries to all sensors. Monte Carlo Simulator is utilized to evaluate the throughput, which is the amount of data collected at the base station, under three schemes; contiguous aggregation, aggregation with overlapping of sensing tasks, and aggregation with overlapping of sensing and processing tasks. Our simulation results demonstrate that, for the WSN of 25 sensors with a single base station deployed within the WSN, the aggregation scheme with overlapping of sensing and processing tasks shows better performance by aggregating a minimum of 56% of the data in lower time duration in comparison to other schemes.