In this letter, we consider a cooperative wireless network consisting of a source, multiple intermediate energy harvesting nodes, and a destination, in the presence of a passive eavesdropper. First, the intermediate nodes use the time switching-based relaying protocol to harvest energy from the source signal. Then, a pair out of intermediate nodes are selected as a relay and a jammer to transmit confidential and jamming signals to the destination and eavesdropper. Under these assumptions, we evaluate the system performance in terms of secrecy outage probability.

In this article, we consider a new unmanned ariel vehicles (UAV)-aided intelligent wireless sensing scheme, where the UAVs are deployed for smart sensing and collecting data from Internet-of-Things (IoT) devices. In particular, we propose optimal UAVs’ path planing approaches for minimizing the completion time and total energy consumption of the UAVs’ deployment for data collection. Two optimal schemes, namely, optimal energy consumption by peer-to-peer UAV-IoT sensing networks and optimal energy consumption by clustering UAV-IoT sensing networks, are considered. The low-complexity procedures of our advanced optimization techniques are suitably applied to disaster relief networks when the solving time must be strictly adhered to. Our real-time optimization algorithms result in low computational complexity with fast deployment and low processing time for solving the problem of tracking and gathering sensor data, i.e., in very short time (milliseconds). Through simulations results we demonstrate that our proposed approaches in UAV-aided intelligent IoT wireless sensing are suitable for time-critical mission applications such as emergency communications, public safety, and disaster relief networks.

With the advantage of flexible deployment in all terrains, unmanned aerial vehicles (UAV) equipped with lightweight computing module can bring mobile edge computing (MEC) service closer to both survivors and rescuers in disaster scenarios. However, the mismatch between the sudden computing demands of mobile users (MUs) and limited computation resources of UAVs cannot meet the high delay sensitivity requirements for computation offloading. In this letter, we aim to exploit the real-time prediction capability of digital twin (DT) to optimize the offloading decision under uncertainty. Considering the mapping deviations between the DT and the real network status, we formulate the computation offloading problem as an online matching problem in uncertain bipartite graph. Furthermore, we transform this matching problem into a multi-armed bandits (MAB) problem and finally solve it through an upper confidence bound (UCB)-based stable matching algorithm. The simulation results prove the superiority of our proposed method.

The expected explosion of video traffic in 5G ultradense networks will pose many challenges to the Internet service providers, e.g., degraded capacity and unfair quality of service (QoS). The most efficient solution is resource allocation that does not require any network architecture changes. This article proposes a social-aware spectrum sharing and caching helper selection (SSC) strategy that exploits the resources of the mobile users (MUs), i.e., downlink spectrum resources for sharing and caching storage resources for multicasting, to offload the videos in dense device-to-device (D2D) 5G networks. Particularly, we consider physical and social attributes of the MUs to formulate an SSC optimization problem. The SSC problem is solved to optimally control: which MUs, namely sharing mobile users (SUs), that share the downlink spectrum resources and which MUs, namely caching mobile helpers, that cache the requested videos for D2D multicast communications by reusing the shared downlink spectrum resources. The objective is to maximize the system capacity while satisfying the constraints on capacity fluctuation among the receiving mobile users (RUs) and target signal-to-interference-plus-noise ratio at the SUs, to guarantee the RUs and the SUs high QoS fairness. Simulation results are shown to demonstrate the benefits of the SSC strategy.