The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

In order to improve the security performance of multiuser visible light communication (VLC) and facilitate the secure application of optical wireless communication technology in Internet-of-Things, we investigate the physical-layer security in a multiuser VLC system with non-orthogonal multiple access (NOMA). When the light-emitting diode (LED) transmitter communicates with multiple legitimate users by downlink NOMA, both single eavesdropper and multi-eavesdropper scenarios are considered. In the presence of single eavesdropper, based on transmission characteristics of the optical wireless channel, with known instantaneous channel state information (CSI) of the NOMA legitimate channels and statistical CSI of the eavesdropper channel, an exact expression of secrecy outage probability (SOP) is derived, which acts as a benchmark of the security performance to guide selecting or optimizing parameters of the LED transmitter and the photodiode receiver of NOMA legitimate users. In the multi-eavesdropper case, based on the spatial distribution of legitimate users and eavesdroppers, the SOP is obtained via a stochastic geometry theory, so as to guide the NOMA legitimate users to keep away from the area with high eavesdropper density. For typical parameters of the indoor LED transmitter and the PD receiver, simulation results show that the SOP performance improves with the increasing of LED transmission power or transmission signal-to-noise ratio (SNR) in both scenarios. Specifically, in the single eavesdropper case, enlarging the channel condition difference of user groups or deviating the eavesdropper from the given user group can improve the SOP performance, and for a given NOMA legitimate user, the SOP eventually settles around 0.2 while the semiangle at half illuminance of the LED varies between 15° to 60°. In the multi-eavesdropper case, we can get a better SOP performance when reducing the eavesdropper density or the semi-angle at half illuminance of the LED for a given eavesdropper density.

In two-hop relay-aided cellular networks, both half-duplex (HD) and full-duplex (FD) transmission have been extensively studied. FD transmission can achieve twice throughput of HD transmission, but suffers from strong self-interference (SI), which may not be perfectly cancelled. In this paper, a hybrid HD/FD transmission scheme in a relay-aided cellular network is proposed, which not only utilizes the throughput advantage of FD but also weakens the negative effect of SI. The key idea is that a relay station (RS) switches between the HD and the FD modes based on the received signal-to-interference-plus-noise ratio (SINR). If the information rate of the base station (BS)-to-RS link is lower than a preset threshold, the RSs adopt the FD mode to guarantee the throughput; otherwise, the RSs adopt the HD mode to reduce the negative effect of SI. As FD transmission consumes more power than HD transmission, we try to maximize the system energy efficiency under the system spectral efficiency constraint and the transmit power constraints. It is difficult to solve this optimization problem directly, so the alternate optimization method is adopted to solve it, i.e., optimizing the transmit power of the BS and the transmit power of RSs in turn. Simulation results show that under perfect SI cancellation, the hybrid scheme can achieve higher energy efficiency than the HD mode by taking the throughput advantage of FD; while under poor SI cancellation, the hybrid scheme can greatly weaken the negative effect of SI and achieve higher energy efficiency than the FD mode.

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving. It exploits the ubiquitous service coverage and enables tasks to be offloaded to various components, such as high-altitude platform (HAP), unmanned aerial vehicle (UAV), and roadside unit (RSU). In this article, we address the challenge of minimizing the overall task offloading delay in the air-ground integrated VEC network through a joint multicomputation equipment selection and multidimensional resource allocation (JCESRA) problem. Considering the nonconvexity inherent in the problem, we employ the fundamental idea of the block coordinate descent (BCD) method to tackle it. Initially, we exclude the HAP and decompose the primal problem into three subproblems: 1) low-altitude computation equipment selection; 2) joint bandwidth and computation resource allocation; and 3) UAV trajectory design. The first subproblem, which involves integer programming, is solved by using the many-to-one matching method. Meanwhile, we utilize the CVX and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize the coalition game method to deal with it. Based on the solutions of the three subproblems, the JCESRA algorithm without considering the HAP has been proposed. Subsequently, we consider the HAP into the problem. Because the task offloading decision and computation resource allocation of the HAP problem can be viewed as a knapsack problem, we utilize the dynamic programming method to solve it. Because some tasks are offloaded to the HAP, there are some redundant computation resources in UAVs and RSU. We reallocate the computation resources of UAVs and RSU to further reduce the task offloading delay. At last, we present the complete JCESRA algorithm. The simulation results unequivocally indicate that the proposed JCESRA algorithm outperforms other algorithms by significantly reducing the task offloading delay.