## 3 Proposed Solution
### Producer Location Prediction  
The prior knowledge of the producer's new PoA coordinates plays a crucial role in ensuring seamless mobility. As part of our approach, we propose utilizing the producer's predicted coordinates of the next location where the MP will be present when the handoff is complete as destination coordinates to receive the consumer's Input. These predicted coordinates represent the nearest location to the producer's new PoA. When a producer moves out of the range of its current PoA and needs to establish a connection with a different PoA, its network interface continuously monitors the RSS in the current channel. The RSS continues to degrade as the producer moves further away from the current PoA. In our strategy, when the RSS drops below a predefined threshold, the producer predicts the future position coordinates at the time \(T_{h}\), representing the time required to complete the handoff process. Subsequently, the producer sends the RP packet, which includes the producer's predicted coordinates, to notify the Anchor. Several methods have been proposed to predict the future location of a UAV. These prediction methods aim to estimate the UAV's position at a future time based on its current location and other relevant factors. Common approaches include trajectory extrapolation [34], sensor fusion [35], Kalman filtering [36], machine learning [37], and dynamic models [38]. In this paper, a Kalman Filter (KF) [39] method is used with the aid of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) sensor technologies for predicting the UAV's future position coordinate. The KF method is a popular method for predicting the future location of UAVs. It is a recursive algorithm that uses a state space model to estimate the UAV's current state and predict its future state based on the previous state and new measurements. This makes it more robust to noise and disturbances than other prediction methods, such as trajectory extrapolation. The KF operates through a two-step process: prediction step and update step, as shown in Figure 4. In the prediction step, the KF leverages the state transition model and current state (position and velocity) to predict the UAV's state (position) at the next time step. This prediction is associated with an uncertainty represented by the error covariance matrix. In the update step, the predicted state is compared with the actual measurements (obtained from a real-time GPS and IMU sensors) to compute the measurement residual. The filter then adjusts the state estimate and error covariance based on this residual and the measurement noise covariance. To predict the position of a UAV using the KF, we need to define the KF's components as follows:  
1. State vector \(X_{k}\) The state vector of a UAV at moment k consists of its position and velocity, therefore, \(\hat{X}\) is a vector of \(6\times 1\). \[\hat{X}_{k}=[x_{k},\ y_{k},\ z_{k},\ vx_{k},\ \vy_{k},\ vz_{k}]^{T}\] (1) where \([x_{k},\ y_{k},\ z_{k}]^{T}\) are the estimated position coordinates and \([vx_{k},\vy_{k},\ vz_{k}]^{T}\) are the estimated velocities along the \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively. State transition matrix (A) Since the position of the UAV changes over time from one time step to the next step, the new coordinate in the next time step can be approximated by Equation (2). \[ \begin{cases}x_{k+1}=x_{k}+vx_{k}\times\Delta t\\ y_{k+1}=y_{k}+vy_{k}\times\Delta t\\ z_{k+1}=z_{k}+vx_{k}\times\Delta t\end{cases} \] (2) The state transition matrix is typically defined as follows: \[A= \begin{bmatrix}1&0&0&\Delta t&0&0\\ 0&1&0&0&\Delta t&0\\ 0&0&1&0&0&\Delta t\\ 0&0&0&1&0&0\\ 0&0&0&0&1&0\\ 0&0&0&1&0&1\end{bmatrix} \] \(\Delta t\) represents the time interval between two consecutive time steps \(k\) and \(k+1\). 3. Measurement matrix \(H_{t}\) The measurement vector \(Z_{t}\) is a \(3\times 1\) vector representing the position measurements (\(x_{t},\ y_{t^{\prime}},\ z_{t}\)) obtained from a real-time GPS sensor.