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Thanks to the inherent characteristics of flexible mobility and autonomous operation, Unmanned Aerial Vehicles (UAV) will inevitably be integrated into 5G/B5G cellular networks to assist remote sensing for real-time assessment and monitoring applications. Most existing UAV-assisted data collection schemes focus on optimizing energy consumption and data collection throughput, which overlook the temporal value of collected data. In this paper, we employ age of information (AoI) as a performance metric to quantify the temporal correlation among data packets consecutively sampled by the IoT devices, and investigate an AoI-energy-aware data collection scheme for UAV assisted IoT networks. We aim to minimize the weighted sum of expected average AoI, propulsion energy of UAV, and the transmission energy at IoT devices, by jointly optimizing the UAV flight speed, hovering locations, and bandwidth allocation for data collection. Considering the system dynamics, the optimization problem is modeled as a Markov Decision Process. To cope with the multi-dimensional action space, we develop a Twin Delayed Deep Deterministic policy gradient (TD3)-based UAV trajectory planning algorithm (TD3-AUTP) by introducing the deep neural network (DNN) for feature extraction. Through simulation results, we demonstrate that our proposed scheme outperforms the deep Q-network and Actor-Critic based algorithms in terms of achievable AoI and energy efficiency.

The integration of mobile-edge computing (MEC) and energy harvesting (EH) can potentially improve the network performances and prolong the battery life of the device. In this article, we study the resource management problem in the device-to-device (D2D)-aided wireless powered MEC networks where one device can forward or execute computation data for other devices with its resources. Our problem seeks to optimize the computation offloading strategy, transmission power, energy transmit power, as well as CPU speed to maximize the long-term utility energy efficiency (UEE). UEE is defined as the achieved computation data per unit energy. Since the formulated problem is in fractional form and hard to solve, we employ the Dinkelbach algorithm to transform the problem into a parametric subtractive form. Furthermore, considering that the formulated problem is time varying and stochastic due to the dynamic task arrival rate and battery level, we transform the long-term problem into deterministic drift-plus-penalty subproblems for each time slot by introducing virtual queues and adopting the Lyapunov optimization theory. The proposed scheme can balance the optimal UEE and stable data queue by introducing the control parameter $V$ . Theoretically, we reveal the tradeoff between the UEE and stable queue length for wireless powered MEC systems as $[O(1/V), O(V)]$ . Finally, the simulations illustrate the efficiency of the proposed scheme compared with the existed work in terms of the UEE, stable queue length, and battery level.

Currently, the computing capability of smart mobile devices has been extremely improved. Exploiting computing resources of mobile devices to assist the network through offloading computation tasks will promisingly boost the fifth-generation (5G) and beyond heterogeneous networks. We investigate the user-assisted multi-task offloading scheme based on the mobile edge computing (MEC)-Cloud architecture to reduce the end-to-end computing latency. The offloading strategy, computing resource allocation and spectrum allocation are jointly optimized to minimize the computation latency while guaranteeing the energy available to the users. The formulated optimizing problem is a large-scale mixed-integer nonlinear optimizing problem which is hard to solve within a rational time. To overcome this problem, a low-complexity distributed framework based on the alternating direction method of multipliers algorithm is proposed to minimize the computing latency for all tasks. Compared with the existing schemes, the proposed scheme can reduce the computing latency and improve the performance efficiently. Simulation results illustrate the effectiveness of the proposed scheme in respect of latency reduction with different parameters.