# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 7 Conclusions and Future Work  
This section summarizes the results of this work, followed by a brief outlook on possible future work based on this paper. This work addressed the issue of energy efficiency in NDN-based IoT networks by specifying a set of network parameters to facilitate the analysis of such networks. The key parameters were the graph size and topology, caching scheme, caching strategy, and forwarding strategy. A systematic approach to finding the best parameter combinations for a given scenario was presented with a hyperparameter optimization algorithm called grid search. These parameters were simulated in the network simulator ndnSIM, specifically created for the NDN protocol. In order to simplify the creation of parameter-based NDN network simulations, a framework was created in this work that automates major parts of the development process of such network simulations. The framework was used to perform grid search on the parameter combinations at hand. The parameters were split into three categories: constant, fixed, and variable. The focus was to find the impact of different variable parameter combinations given varioussets of fixed parameter combinations. Specifically, the analyzed variable parameters were the caching scheme, caching strategy, forwarding strategy, and packet size. One main result is that there is a clear hierarchy regarding how much influence each parameter has on energy efficiency: The most impactful parameter is the packet size, followed by the caching scheme, caching strategy, and the forwarding strategy. To allow for a qualitative, more useful, analysis, the ranks of the parameter values were compared to each other to understand the underlying patterns better. Although the relative deviation of the consumed energy among parameters was small, the results are still interesting. Implementing more realistic sleep behavior and running the network for several days or weeks may drastically increase the absolute amount of saved energy. Furthermore, the implementation of more energy-efficient caching algorithms is fairly simple. The presented framework may deliver the groundwork for more efficient analyses of specific NDN networking scenarios in ndnSIM. In this work, we analyzed a large amount of parameters, but using it for a concrete real-world network is also an interesting use case. For example, given a wireless sensor network of size 50 with repetitive packets sent, one would like to know which caching scheme and strategy are the best in that scenario by performing simulations. The framework simplifies a major part of the simulation process in ndnSIM, from the declaration of the network topology, the implementation of the caching algorithms, to the visualization of the results. Further work using the framework may be greatly accelerated due the abstractions it offers in many areas. We demonstrated how the caching scheme has a relatively large impact on energy consumption in our given setup. A novel approach to caching schemes is the _pCASTING_ scheme, which was introduced in [10]. Its idea is to make the caching probability based on different local node attributes, namely consumed energy in percent, Content Store occupancy, and freshness of data. It may be insightful to compare pCASTING to the probabilistic scheme and to find out which one performs better. Furthermore, it might be instructive to compare the NDN-based implementation to an IPv6-only implementation. Since many parameters are independent of the underlying network-layer protocol (as shown in Figure 1), the framework can also be used to set up IPv6 simulations more efficiently, not just NDN networks. ns-3 provides provides full support for IPv6, making it relatively straightforward to create an IPv6 network from our framework. Conceptualization, D.P., B.G., S.F. and M.A.H. ; methodology, D.P. and B.G. ; software, D.P. ; validation, D.P. ; formal analysis, D.P. ; investigation, D.P. ; writing--original draft preparation, D.P. and M.A.H. ; writing--review and editing, D.P., S.F. and M.A.H. ; visualization, D.P.