e area. As the transmission is directed toward the user, we need to sort out the boundary condition for the IoT device that will keep it within the transmission angle. To simplify, we are assuming the base station location as (0,0); we get the initial distance between BS and IoT as Linitial Vx?+y2. (9.25) Using (9.16) and (9.25), we can derive the following relation cos(A1) x2+y2+x2-y2 Little (9.26) For the IoT device's initial position to be within the transmission area, the maximum value of A1 can be Omax = Beamwidth/2 For this Omax angle, we can calculate the maximum value of the initial y position from a given X position using (9.26) and derive the following relation. Linitial M. S. Islam et al. (9.27) Equation (9.27) gives the maximum limit on the initial y position for IoT device for any given X value and transmission beamwidth. 9.6.3 Mobility Model In the proposed scenario, we have considered IoT devices attached to human usages, such as smartwatches, car sensors, and similar devices. So the mobility model of these devices is similar to the human mobility model. We have considered the following three mobility models for IoT movement. 9.6.3.1 Random Waypoint Mobility Model This mobility model represents static to downward movement in urban and rural areas. This movement model applies to nonmotorized movements such as walking, running, and cycling. The speed of the users in this model is assumed to be within the range of 0-10kph, and the angle is between 0 and 360° The movement pattern is random both in terms of speed and direction as both can be changed randomly at any time [21]. For simplicity, we have assumed that the devices can alter their speed after every t time interval and direction after every 100 m. 9.6.3.2 Manhattan Mobility Model This model refers to the urban street movement for motorized vehicles [21]. We assume a grid-like design for all urban roads resulting in a discrete perpendicular or parallel change in direction. We assume the block lengths to be 200 m, which means the user can change d