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This paper proposes a novel data-aggregation approach for capacity planning of a wireless sensor network (WSN). The approach is based on incorporating the three sensor's tasks, which are sensing, processing and transmission, into a task flow graph (TFG); moreover all TFGs within WSN are merged into one super task flow graph (STFG). Also, we have modeled the execution time of transmission task as non-preemptive imprecise computation times. In real time systems, the insufficient number of gateways may partially terminate the transmission due to the deadline requirements in timings. This termination of transmission of any sensor node results in partial information of the sensed data resulting in imprecise computations. We have scheduled all tasks within STFG to determine the number and capacity of the gateways (base stations), where the aggregated data should be collected. We have utilized a Branch-and-Bound algorithm to perform scheduling, which is subject to concurrency in transmissions. We have analyzed the performance of 50 sensors within WSN by varying the availability of gateways. The computational results have provided excellent bounds on the number and capacity of gateways keeping in mind the trade-off in the quality of data-aggregation in the imprecise computations.