kly. The combined results follow a similar trend as IoT mobility, and as expected, the combined mobility offers a shorter sustainable link time. In Fig. 9.9, the user is on a higher mobility model, hence performs worse than IoT. We see a similar pattern in this case as well, where the combined mobility serves less than the user mobility. From these results, this is clear that the most efficient pairing would be IoT devices and users in the same mobility group; otherwise, the system will need to find a new device for pairing as one of the devices goes out of range. In both Figs. 9.10 and 9.11, the Manhattan mobility model is used for IoT mobility while we used different mobility models for user mobility. In Fig. 9.10, again, just as in the case of the random waypoint mobility model, the user stays Feasibility of LDM to Serve User-IoT Pairs in the Future Wireless Network User mobility loT mobility Combined mobility Transmission Beamwidth (Degree) Fig. 9.8 Impact of the LDM pair mobility on the sustainable link time for random waypoint mobility model User mobily loT mobility Combined mobility Transmission Beamwidth (Degree) Fig. 9.9 Impact of the LDM pair mobility on the sustainable link time for random waypoint model for IoT and Manhattan model for user within the transmission area for a significantly longer period. At a 120-degree transmission angle, user mobility offers almost double link sustainability time than IoT mobility. Figure 9.11, on the other hand, shows similar results with one exception; unlike the other results here, the combined mobility is almost the same as IoT mobility. This behavior is due to the gap in the link sustainability time of user and IoT mobility. As user mobility offers a much higher link sustainable time, the M. S. Islam et al. User mobility loT mobily Combined mobility Transmission Beamwidth (Degree) Fig. 9.10 Impact of the LDM pair mobility on the sustainable link time for Manhattan mobility model User mobily loT mobility Combined mobility Transmission Beamwidth (Degree) Fig. 9.11 