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The authors present a novel graph-based model for aggregating sensors' data at the gateways within wireless sensor networks (WSNs), where the proposed graph can act as a mean of guiding and assessing the needed resources for data aggregation. In this study, the authors have modelled all sensors' tasks in a graph so that their collected data are to be smoothly aggregated (scheduled) at the gateway without losing or overlapping the collected data at the gateways. A typical WSN comprises of hundreds of sensors and few gateways; moreover, each sensor executes periodically and sequentially three tasks, which are sensing, processing and transmitting. The three tasks are modelled as a directed acyclic graph (DAG) per sensor, and then all DAGs are grouped into a super task-flow-graph (STFG). The data aggregation problem is solved by scheduling the tasks within STFG, where the authors have utilised three scheduling algorithms: as soon as possible, as late as possible and branch-and-bound. The simulation results for 50 sensors covering an area of 10'000'm2 utilising deterministic and stochastic execution models show a requirement of eight and six gateways, respectively, with minimal waiting time for aggregating the collected data from sensors to the gateways.