This paper proposes a novel framework for the joint optimization of 3D deployment, mobility, device association, and uplink power control of multiple unmanned aerial vehicles (UAVs) to enable energy-efficient communication for Internet of Things (IoT) devices. The framework aims to minimize the total transmit power of IoT devices while ensuring reliable uplink communications. The key contributions include the development of an efficient algorithm for jointly optimizing UAV locations, device-UAV associations, and uplink power control, as well as the analysis of UAV mobility and update times based on the activation patterns of IoT devices. The system model considers a scenario where UAVs act as aerial base stations to collect data from ground IoT devices. The paper introduces a framework that dynamically updates UAV locations based on the activation of IoT devices, ensuring efficient data collection while minimizing energy consumption. The framework is composed of two main steps: first, optimizing the deployment and association of UAVs given the locations of active IoT devices; second, analyzing the UAVs' mobility and update times based on the time-varying activation process of IoT devices. The paper presents a detailed analysis of the ground-to-air path loss model, including expressions for the probability of line-of-sight (LoS) and non-line-of-sight (NLoS) links, and the corresponding path loss models. It also discusses the activation models for IoT devices, which can be either random or deterministic, depending on the application. The paper then formulates an optimization problem to find the optimal UAV locations, device associations, and transmit power of IoT devices at each update time, subject to SINR constraints and maximum transmit power limits. The optimization problem is decomposed into two subproblems, which are solved iteratively. The first subproblem involves finding the optimal device-UAV association and transmit power given the UAV locations, while the second subproblem determines the optimal UAV locations given the device associations. The solution to the first subproblem is used to update the UAV locations in the second subproblem, and this process is repeated until convergence. The paper also presents a detailed analysis of the interference scenario and the interference-free scenario, providing tractable solutions for both cases. In the interference scenario, the optimization problem is highly non-linear and non-convex, requiring advanced techniques such as sequential quadratic programming (SQP) for solution. In the interference-free scenario, the problem is simplified, and the optimal UAV locations can be determined using convex optimization techniques. Simulation results show that the proposed framework significantly reduces the total transmit power of IoT devices compared to stationary UAVs, with a 45% reduction in transmit power and up to 28% improvement in system reliability. The results also reveal an inherent trade-off between the number of update times, UAV mobility, and transmit power of IoT devices. The framework is shown to be effective in dynamically adapting to the changing activation patterns of IoT devices, ensuring efficient and reliable communication.This paper proposes a novel framework for the joint optimization of 3D deployment, mobility, device association, and uplink power control of multiple unmanned aerial vehicles (UAVs) to enable energy-efficient communication for Internet of Things (IoT) devices. The framework aims to minimize the total transmit power of IoT devices while ensuring reliable uplink communications. The key contributions include the development of an efficient algorithm for jointly optimizing UAV locations, device-UAV associations, and uplink power control, as well as the analysis of UAV mobility and update times based on the activation patterns of IoT devices. The system model considers a scenario where UAVs act as aerial base stations to collect data from ground IoT devices. The paper introduces a framework that dynamically updates UAV locations based on the activation of IoT devices, ensuring efficient data collection while minimizing energy consumption. The framework is composed of two main steps: first, optimizing the deployment and association of UAVs given the locations of active IoT devices; second, analyzing the UAVs' mobility and update times based on the time-varying activation process of IoT devices. The paper presents a detailed analysis of the ground-to-air path loss model, including expressions for the probability of line-of-sight (LoS) and non-line-of-sight (NLoS) links, and the corresponding path loss models. It also discusses the activation models for IoT devices, which can be either random or deterministic, depending on the application. The paper then formulates an optimization problem to find the optimal UAV locations, device associations, and transmit power of IoT devices at each update time, subject to SINR constraints and maximum transmit power limits. The optimization problem is decomposed into two subproblems, which are solved iteratively. The first subproblem involves finding the optimal device-UAV association and transmit power given the UAV locations, while the second subproblem determines the optimal UAV locations given the device associations. The solution to the first subproblem is used to update the UAV locations in the second subproblem, and this process is repeated until convergence. The paper also presents a detailed analysis of the interference scenario and the interference-free scenario, providing tractable solutions for both cases. In the interference scenario, the optimization problem is highly non-linear and non-convex, requiring advanced techniques such as sequential quadratic programming (SQP) for solution. In the interference-free scenario, the problem is simplified, and the optimal UAV locations can be determined using convex optimization techniques. Simulation results show that the proposed framework significantly reduces the total transmit power of IoT devices compared to stationary UAVs, with a 45% reduction in transmit power and up to 28% improvement in system reliability. The results also reveal an inherent trade-off between the number of update times, UAV mobility, and transmit power of IoT devices. The framework is shown to be effective in dynamically adapting to the changing activation patterns of IoT devices, ensuring efficient and reliable communication.