# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 6 Discussion
Regarding BPL, efforts have been traditionally focused on indoor communication networks (e.g., for in-home multimedia communications). Thus, reference [43] presents a channel simulator for this kind of network that considers cable characteristics, cable lengths, and loads. Recently, attention has been also paid to other emerging applications. As a token of this, reference [44] focuses on the application of BPL to IoT, paying special attention to PHY aspects, and references [45; 46] focus on applications of BPL for Smart Grids, thus being very related to SimBPL. In particular, reference [45] aims to evaluate by means of simulating how the throughput of BPL is influenced by channel conditions and the physical characteristics of the grid. Regarding the differences between this study and Sim BPL, although both focus on AMI, reference [45] focuses on BPL over low-voltage grids and SimBPL focuses on BPL over medium-voltage grids. In addition, reference [45] implements G.hn in the ns-3 network simulator, whereas SimBPL relies on an implementation of IEEE 1901 in Python. Regarding the similarities, both use license-free platforms, and the parameters that [45] conclude significantly influence the throughput (namely background noise, line length, number of branches, branch tap length, and terminal load branch impedance) are also considered in SimBPL. The authors of [46] aim to evaluate by means of simulation the impact of repeaters in the linear topology of distribution substations on BPL performance. To do so, laboratory and in-field tests are carried out, and the obtained results are used as inputs for the simulations. Reference [46] focuses on medium voltage and IEEE 1901, as SimBPL, although it uses ns-3 as a network simulator. Again, to the best of the authors' knowledge, all these tools do not provide a web-based interface to enable interacting easily with them remotely and are not designed for scaling up, as in the case of SimBPL. Finally, monitoring, analysis, and grid conditioning in PLC operational networks were identified as key by industry since the earlier stages [47]. For some time now, much research has been carried out to monitor grid conditions (e.g., cable aging [48], fault detection [49; 50]) based on measurements from the PLC communication networks already deployed. Even more recently, machine learning has been proposed to improve such grid monitoring mechanisms [51; 52; 53; 54]. PRIME Analytics differ from these proposals in that it does not use PLC channel measurements to diagnose problems in the electrical grid, but it does use higher-level information from the PLC network, such as traffic traces and standard reports available in the concentrators, to diagnose problems in the PLC network itself. Thus, PRIME Analytics does not represent an alternative to such proposals, but it can be used in combination with them, especially when the communication problem is due to a grid problem (e.g., a cable cut). PRIME Analytics allows an operator to both detect and identify non-persistent problems in PRIME networks by analyzing a set of graphical and tabulated information, supporting programming and optimizing possible repairs in the field with a very small margin of error, since it locates the error in the logical segment of the PRIME network. Although PRIME Analytics has been used so far mainly for carrying out forensic analysis focused on the performance of PRIME networks in relevant scenarios for DSOs such as to assess the impact of different sources of noise [55], it has also been used to detect cybersecurity problems in PRIME networks [56], presenting a great research potential in this line. Overall, we believe that the toolkit presented in this paper stands out for its comprehenseness and versatility, in that it covers not only simulation tools but also analysis tools for both PRIME and BPL, as well as for the scalability and usability provided by the web-based software architecture and interfaces.