# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 5 Conclusions  
In this paper, a closed-loop algorithm is presented that allows for automatic modification of physical network parameters based on actual in situ measurements. After the initial network-planning and configuration, the network is continuously monitored, and physical network settings are optimised based on the measured QoS parameters on the clients. If the received signal strength is too low, TX power of the AP is increased. If the signal-to-noise ratio drops, another frequency is selected, and clients are connected to another AP when the load on one AP is too high. This allows for QoS to be provided without the need to significantly over-dimension the network. Based on simulations, it is found that a TX power of 6 dBm below the maximum TX power gives the best trade-off between coverage performance and the number of required APs. Even if the floor plan is significantly modified after the network has been rolled out, for example, by the addition of metallic racks, QoS is guaranteed. The algorithm is validated via simulations, and experimentally by implementing it in a real testbed. The measurements confirm that the network successfully reconfigures when the RSSI drops, or when the load on one AP increases. Reconfiguring the network based on measurement data eliminates the need for an on-site measurement campaign prior to the initial network-planning in order to fit the path loss model to the given room. It also allows for automatic maintenance to limit manual interventions in the network configuration. Because there is no single technology that fits all industrial use cases, multiple coexisting wireless technologies are of utmost importance. The proposed framework performs network-planning for different technologies with different levels of reconfiguration possibilities. This allows not only to perform network-planning in large and complex environments, but it also allows for monitoring of the network and reconfiguring it if necessary. There is support for legacy devices with no configurability, as well as for existing technologies with custom firmware and new technologies, such as SDR. This makes the tool viable for the design of wireless networks of FoF, where support for different wireless technologies is critical, given the multitude of existing sensors already available, and the growing number of new technologies. Future work includes running a field trial in a larger factory hall with more devices, and analyzing interference. Furthermore, RSSI-based localization of clients will be implemented, and more MAC-layer settings are configured by the network controller, including airtime and packet priorization. Conceptualization, B.D.B., W.J. and D.P. ; methodology, B.D.B. ; software, B.D.B. and D.P. ; validation, B.D.B. ; formal analysis, B.D.B., W.J. and D.P. ; investigation, B.D.B. ; resources, W.J. ; data curation, B.D.B. ; writing--original draft preparation, B.D.B. ; writing--review and editing, B.D.B., W.J. and D.P. ; visualization, B.D.B. ; supervision, W.J. ; project administration, W.J. ; funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This work was executed within the imec AAA project Better Than Wired and EOS project multi-service wireless network (MUSE-WINET). The authors would like to thank research group IDLab for the use of the IIoT lab and [PERSON], [PERSON], and [PERSON] for their collaboration in the Better Than Wired project. The authors declare no conflict of interest.