# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 6 Large-Scale Simulations
### Simulation Results  
Different values of \(L_{\text{max}}\): We tested the performance of the DML for different values of \(L_{\text{max}}\), in networks of 500 nodes, with \(m=0.4\cdot|S|\). The results are shown in Figure 7d. The red points represent the values for the maximum latency threshold \(L_{\text{max}}\) provided by the industrial operator. The average access latency achieved by the DML is always below the threshold, due to the provisioning of the ProxySelection+ algorithm. In fact, we can see that the more the value of \(L_{\text{max}}\) is increased,  
Figure 7: Network with \(n=500\), \(m=0.4\cdot|S|\).  
the larger the difference between \(\overline{L}\) and \(L_{\text{max}}\) becomes. This happens because for higher \(L_{\text{max}}\) values, the latency constraint is more relaxed, and lower \(\overline{L}\) can be achieved more easily.  
Number of proxies used: We compare the three methods with respect to the number of special nodes that they use. As usual, the DML is using proxies; the non-storing mode is using the controller \(C\); and the storing mode is using LCAs. As we mentioned earlier, the use of special nodes is wasteful of resources. In Figure 7(a), we can see that the DML is performing really well compared to the storing mode and uses much less special nodes. Of course, the non-storing mode is using just one special node for any network size, but this has a severe impact on the latency achieved.  
Different percentages of requesting nodes: Another possible factor that could affect the \(\overline{L}\) achieved is the percentage of consumers. In Figure 7(b), we can see that \(\overline{L}\) remains constant for any percentage of requesting nodes, in all three alternatives. This shows that DML is able to automatically adapt the number of proxies, so as to guarantee the latency constraints irrespective of the number of consumers.  
Average latency achieved: Figure 7(c) displays the results on the average access latency for the three alternatives, for different numbers of nodes in the network. We can see that the efficient management of proxies provided by the DML results in a better performance compared to the other two alternatives. \(\overline{L}\) achieved by the DML respects the latency constraint and always remains lower than \(L_{\text{max}}\) (red line).  
Energy consumption: Another aspect that we can easily evaluate in the simulation is the energy cost in terms of communication, related to data access. We evaluate this as the cost of transmissions required to serve consumers' requests. In order to obtain the desired results in units of energy, we transform the dBm units provided in the CC2420 datasheet [32] to mW and we multiply with the time that each node of the network is operational. Figure 7(d) displays the energy consumption in the entire network for the three alternatives, for different numbers of nodes. The energy consumption for  
Figure 8: Comparison of the three methods.  
communication is lower in the case of the DML because low latency comes with less transmissions in the network, resulting in fewer energy demands.