Impact of the LDM pair mobility on the sustainable link time for Manhattan model for IoT and random waypoint model for user pair needs to be broken almost every time due to the IoT device's network condition. This pattern can help the BS monitor one of the device's conditions with higher frequency based on their mobility model, lowering the computation load at the BS with higher efficiency. In Fig. 9.12, both IoT device and user are moving as per the freeway mobility model. Both devices are moving at a higher speed, and hence the sustainable time is much lower than the urban scenario. However, the gap in performance between IoT Feasibility of LDM to Serve User-IoT Pairs in the Future Wireless Network User mobily loT mobility Combined mobility Transmission Beamwidth (Degree) Fig. 9.12 Impact of the LDM pair mobility on the sustainable link time for freeway mobility model Table 9.2 Combined performance comparison for 60-degree transmission angle Mobility model Ratio IoT Ratio user Random waypoint Random waypoint Manhattan Manhattan Manhattan random waypoint Freeway and user devices is lower in this scenario. In future work, other technologies such as beam following can be applied to improve the performance in this scenario as the movement is unidirectional and predictable. Table 9.2 shows the comparison of link sustainability at 60-degree transmission angle for all five transmission scenarios. When both devices are in the same mobility group, IoT devices are more likely to go out of range before the user. As we can see, when the gap between user and IoT sustainability is too significant, the gap between IoT and combined mobility performance becomes smaller. The BS can use this knowledge of known patterns during LDM pairing and make a more efficient IoT-user LDM pair that can improve the LDM performance in future wireless networks. 9.8 Conclusion The analysis presented in this chapter focuses on the performance of LDM and its sustainability when both receivers have various degrees of mobility. LDM can M. S. Islam et