## 6 Discussion  
Analyzing the simulation results we can say that the predictor helps reduce the energy consumption of the nodes during the topology control process in all studied scenarios. In the square area the average reductions of the energy consumptions were the following: 30.1% when using Random Walk, 26.6% for SLAW, 14.7% for Nomadic 11.7% for RPGM. Clearly, when the nodes follow a Random Walk model, their motion behavior is independent of other nodes. Therefore the network becomes larger and a higher number of TC messages are disseminated. The energy consumption and its dispersion increases, and also increases the energy saved by the predictor. Figure 14: Energy consumption when nodes follow a RPGM model. Figure 15: Energy consumption _versus_ network degree when nodes follow a RPGM model. The SLAW model represents an interim point between a random and a group mobility model, because the nodes move randomly (in terms of speed and direction) taking into account their recent history. Therefore, they finally tend to move coordinately with some other network nodes, which reduce the number of changes in the network topology and also the energy consumption due to the dissemination of TC messages. For that reason the energy saved by the predictor in SLAW is less than in the Random Walk model. Following the same reasoning we can understand why in group motion models (_i.e._, in Nomadic and RPGM) the predictor can reduce between 11% and 15% of the energy consumed by a node during the topology control process. Here, each group behaves as a single large node (or network island) that moves randomly through the working area, producing sporadic encounters between groups. This situation becomes evident, observing the network degree in the previously presented scenarios. On the other hand, the reduction of the energy consumption produced by the predictor was low in the narrow area, as most nodes remain isolated due the shape of the simulated place. In the simulations that used the Random Walk and SLAW, the nodes become isolated, and in Nomadic and RPGM, the groups are isolated. The average reductions in this scenario were the following: 15.3% when using Random Walk, 12.6% for SLAW, 4.7% for Nomadic, and 3.6% for RPGM. The influence of the predictor is clearly different depending on the features of the interaction scenarios. In order to understand the limits of the TC message prediction strategy, we next present the preliminary answers to the stated research questions. Concerning the _RQ1_ (_How does the node density affect the energy consumption?_), we can say that the density is not an appropriate metric to understanding the energy consumption, due to reasons previously mentioned. In that sense the network degree or the density of the node communication areas are better predictors of this energy consumption. Evidently, the energy consumption per node increases with the network degree; however, the way in which it rises depends on other variables; e.g., the motion behavior of the nodes or the shape of the working area. More connected mobile nodes tend to produce more topology changes and therefore the predictor is able to save a higher amount of energy. This also answers the _RQ4_ (_How does the network degree influence the energy consumption of the nodes?_). This network degree seems to be independent of the node speed, and dependent on the density of the node communication areas. Concerning the _RQ2_ (_How does the speed of network nodes affect their energy consumption during the topology control process?_) the nodes adhering to a group motion model (_i.e._, Nomadic or RPGM) tend to increase their energy consumption with the speed, but just in scenarios where they have frequent encounters with other groups. This tendency is stronger in motion models where the nodes move more independently from other nodes (e.g., in the Random Walk model). If the nodes move slowly, the network topology will have few changes and therefore it becomes more predictable.