# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 4 Results and Discussion
### Comparison to Existing Network-Planning Solutions  
[PERSON] et al. presented a hybrid framework for outdoor network-planning using different radio access technologies [19]. Compared to this framework, we need a more detailed description of the environment and network-planning results, that is, the distances between clients and APs are in the order of meters, rather than km for an outdoor scenario.  
Figure 12: Measured received signal strength information (RSSI) from clients 10 and 11, both connected to AP4, after a data stream is set up between the two clients. The attenuation at the RF port of client 11 is gradually increased to test the reconfiguration algorithm. (**a**) monitoring loop disabled. (**b**) monitoring loop active, a reconfiguration is triggered.  
On the other hand, the framework from [19] used a fixed user service demand to plan the network, whereas our framework does not need this a priori information.  
Compared to commercial WLAN network-planning solutions from Ekahau and Netspot, the main advantage of our framework is that it can be used for more wireless technologies, that is, it is not only WiFi is supported. Furthermore, the commercial network planners perform network-planning during the moment. Troubleshooting by an expert is needed when issues arise after network rollout, whereas the closed-loop monitoring system allows for automatic reconfiguration. The work from [PERSON] et al. also applies to WiFi technology only, and does not discuss transmission power control techniques, and is tailored on the specific MAC layer options that are available in IEEE 802.11 [20].  
The frameworks proposed by [PERSON] et al. [21] and [PERSON] et al. [22] only change frequencies when the network performance degrades. When the floor plan changes, or when the load on an AP increases, this will not be sufficient and other settings need to be modified as well.  
[PERSON] et al. discussed routing methodologies for a multi-hop network and presented simulation results. However, network-planning does not take into account the propagation characteristics of the environment, and only considers the graph theory. Graph theory is also used by [PERSON] et al. for the network-planning of SDN [25], but a feedback-control loop is developed for dynamic network reconfiguration. The use case, that is, backhaul network-planning, differs and the implementation does not relate to the wireless PHY layer. [PERSON] et al. used SDN in an industrial environment, but envisioned a fully redundant network [24]. Additionally, the heuristic developed by [PERSON] et al. is based on a fully redundant network [9], that is, every client can be served by at least two APs, which results in a much higher number of required APs. By using lower transmit powers in our framework, we also over-dimension the network, but this results in a lower number of total APs which leads to a lower installation cost, and less interference.  
In order to have accurate network-planning, a one-day on-site measurement campaign is proposed in [29] to obtain an accurate PL model that is used by the network planner. A one-day measurement campaign and automatic network-planning using the heuristic from [28] already requires less human interaction than performing network-planning via trial-and-error. By using measured RSSI values for the calibration of the calculated coverage map, there is no need for an on-site channel sounding measurement campaign to have accurate attenuation values for the specific objects in the envisioned environment, as the network will adapt to the environment. For any possible environment, we can use a generic PL model from the literature (e.g., [10]) which will automatically adapt to the specific site where the network is deployed based on measurement data.