As unmanned aerial vehicles (UAVs) can provide flexible and efficient services concerning the sparse network distribution, we study a UAV-assisted mobile edge computing (MEC) network. To satisfy the freshness requirement of IoT applications, the age of information (AoI) is incorporated as an important performance metric. Then, the path planning problem is formulated to simultaneously minimize the AoIs of mobile devices and the energy consumption of the UAV, where the movement randomness of IoT devices are taken into account. Concerning the dimension explosion, the deep reinforcement learning (DRL) framework is exploited, and a double deep Q-learning network (DDQN) algorithm is proposed to realize the intelligent and freshness-aware path planning of the UAV. Extensive simulation results validate the effectiveness of the proposed freshness-aware path planning scheme and unveil the effects of the moving speed of devices and the UAV on the achieved AoI.

In this paper, resource allocation is studied for a fully sustainable cooperative IoT network, in which a relay powered by renewable energy forwards data to a destination while charging multiple IoT nodes by radio-frequency (RF) signals. An optimal joint time and power allocation problem is formulated to maximize the long-term sum-throughput of IoT devices, taking into consideration the bounded transmit power of IoT devices, the stochastic characteristic of energy harvesting (EH) process, and dynamic wireless channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different network settings. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Then, we extend our solution to a general network with asymmetric links. By employing Lyapunov optimization, an online stochastic resource allocation algorithm is proposed to obtain the maximum network throughput. It has been shown that the proposed algorithm can achieve close-to-optimal network throughput while maintaining the stability of the system. Finally, extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

In this paper, we study a buffer-aided collaborative relaying framework for cooperative communication system composed of one source node, multiple half-duplex DF relays with buffers, and one destination node. A two-phase adaptive relaying scheme is proposed, i.e. , the source transmits data and the relay buffers receive data in the first phase, and all relays collaboratively transmit the buffered data to the destination node in the second phase. To achieve higher temporal and spatial diversity gains, time slots are dynamically allocated according to the state information of wireless channel (CSI), each node’s energy consumption (ESI), and each relay’s buffer (BSI). Lyapunov optimization theory is utilized to maximize the average achievable throughput under buffer stability and power consumption constraints, and an online buffer-energy-aware adaptive (BEAA) scheduling scheme is proposed to jointly consider relay selection, power allocation, and time allocation. It is disclosed that the proposed BEAA scheduling scheme is able to achieve a higher average network throughput by adapting the transmissions according to the CSI, ESI, and BSI. Moreover, it is unveiled that there exists inherent tradeoff among the transmission delay, power consumption, and the achievable throughput. Extensive simulations are presented to validate the efficiency of the proposed adaptive collaborative relaying protocol.

In this article, we exploit nonorthogonal multiple access (NOMA) for simultaneous energy and information transfer in a wireless-powered Internet-of-Things (IoT) network. As double near–far problem causes severe unfairness, we propose a fairness-aware NOMA-based scheduling scheme to enhance the max–min fairness. Specifically, according to the channel conditions, we divide IoT devices into the interference and noninterference groups with relatively good and poor channel qualities, respectively. Energy transfer is concurrently scheduled with data transmissions of devices with good channels. Thus, devices can harvest more energy to achieve higher rates at the cost of reduced rates of devices with good channels due to the interfering energy signals. We then apply order statistics to theoretically analyze the achievable rates of ordered devices. Based on the analysis, devices are optimally categorized into the interference and noninterference groups to achieve the max–min fairness, i.e., the minimum rate of devices in both groups is maximized. An adaptive power allocation algorithm is also proposed to further improve the network fairness when the transmission power of the energy transmitter is controllable. Throughput-aware NOMA-based scheduling is also presented and compared with the fairness-aware NOMA-based scheduling to illustrate the performance tradeoff between the throughput and fairness. The simulation results validate that the proposed NOMA-based scheduling schemes significantly improve the fairness and throughput performance of wireless-powered IoT networks, compared with the existing solutions.

This paper considered an energy-harvesting based secure two-way relay (EH-STWR) network, where two users exchanged information with the assistance of one buffer-aided relay that harvested energy from two users. To realize the confidential message exchange between two users in the presence of a potential eavesdropper, a secure bidirectional relaying scheme based on time division broadcast (TDBC) was proposed, where one user sent artificial noise to suppress the eavesdropper and another user transmitted data to the relay. A secure sum-rate maximization problem was formulated subject to average and peak transmit power constraints, data buffer and energy storage causality, and transmission mode constraints. By employing the Lyapunov optimization framework, a security-aware adaptive transmission scheme was proposed to jointly adapt transmission mode selection, power allocation, and security rate allocation according to channel/buffer/energy state information (CSI/BSI/ESI). Analysis results showed that the average achievable secrecy rate region can be significantly improved and there exists an inherent trade-off among transmission delay, requirement of transmit power consumption, and achievable secure sum-rate. Moreover, the channel condition between the energy-constrained relay and the potential eavesdropper is a critical factor on the achievable long-term average secrecy rate performance.