## 6 Conclusions and Future Work  
The high-level contributions of our paper are as follows: We have proposed a benchmarking algorithm to determine the minimum aggregation delay for data gathering trees in wireless sensor networks. We have shown that the performance of the maximum and minimum bottleneck node weight-based DG trees (and likewise for the maximum and minimum bottleneck link weight-based DG trees) to be almost the same with respect to the performance metrics (aggregation delay, diameter/hop count and distribution of child nodes/leaf nodes in the DG tree) evaluated in this paper, indicating that it would be sufficient to compare the performance with respect to just two categories of DG trees (bottleneck node weight _vs._ bottleneck link weight based; using either the maximum or minimum bottleneck weight-based DG tree from each category would just be sufficient).  
The simulation results confirm the diameter-aggregation delay tradeoff that was expected between the bottleneck node and bottleneck link weight-based data gathering trees owing to their design. The bottleneck node weight-based DG trees incur a smaller diameter and a larger number of child nodes per intermediate node (both contributing to the larger aggregation delay, especially in networks of high-very high node density), whereas the bottleneck link weight-based DG trees incur a larger diameter and a much smaller number of child nodes per intermediate node. The larger diameter makes the bottleneck link weight-based DG trees incur a data aggregation delay that is at most 40% larger than that incurred with the bottleneck node weight-based DG trees in networks of moderate node density operated under moderate transmission range values (25m). However, with increase in the node density, the aggregation delay incurred with the bottleneck node weight-based DG trees starts becoming significantly larger than the aggregation delay incurred with the bottleneck link weight-based DG trees (that do not show any significant impact of the increase in node density). We also anticipate observing a similar diameter-aggregation delay tradeoff even for any other complex network of high node density.  
The contributions and observations made throughout this paper could be used to focus future research on exploring strategies for maintaining the diameter of the bottleneck node weight-based DG trees at moderate values (and not allowing the number of child nodes per intermediate node to become significantly high) with increase in node density and thereby reducing the network-wide aggregation delay for data gathering. Note that the simulations assume the availability of a sufficient number of unique CDMA codes for the intermediate nodes at the same level or different levels to simultaneously aggregate data from their respective child nodes, if the latter are ready with the data. We will extend our simulation study by running the benchmarking algorithm on a wireless medium with a limited number of CDMA codes and evaluate the impact on the diameter-aggregation delay tradeoff. Likewise, we will also run the benchmarking algorithm under various medium access control protocols for wireless sensor networks [28] and evaluate the impact of collisions (expected to be significant in networks of high-very high node density) on the diameter-aggregation delay tradeoff.