Integrating mobile edge computing (MEC) and wireless power transfer (WPT) is a promising technology that provide sustainable energy supply and cloud-like computing services at network edge. In this article, we propose a non-orthogonal multiple access (NOMA)-aided user cooperative scheme in a three-node WPT-MEC system, which is composed of a far user (FU), a near user (NU) and an access point (AP). What is innovative is that the FU employs NOMA so that it can simultaneously offload data to NU and AP which can overcome the doubly near-far effect of FU. We design an energy consumption minimization problem by jointly optimizing offloading time, task allocation and computation frequency. The optimal solution for the non-convex problem is obtained by some ingenious mathematical methods. Simulations are conducted to demonstrate that the proposed joint design has obvious improvement over other benchmark schemes.

With the development of emerging applications such as augmented reality, more and more computing tasks are sensitive to delay. Caching popular task computation results on the mobile edge computing (MEC) server is an effective solution to meet the latency requirements. When multiple users request the same task, if the computation result is cached on the MEC server, it will return the computation result directly to the user to reduce the delay for repeated computation. In this paper, we use the caching to assist the calculation. Non-orthogonal multiple access (NOMA) is used to further reduce the delay for computation offloading. The optimization problem is formulated as how to make caching and offloading decision to minimize the delay of whole system. In the case of unknown popularity, we use Gated Recurrent Unit (GRU) algorithm to predict the task popularity in time-varying system, and place the computing results of tasks with high popularity on the corresponding server. Based on the predicted popularity, a multi-agent Deep-Q-network (MADQN) algorithm is used to solve the caching and offloading problem. The simulation results show that the prediction error of GRU algorithm can be reduced by increasing the learning rate. Meanwhile, the proposed MADQN can effectively reduce the delay compared with other methods.

This letter investigates the proactive eavesdropping for multiple suspicious links either through interfering or assisting the links. Considering the power constraint at eavesdropper, our objective is to maximize weighted sum eavesdropping rate of multiple suspicious links via jointly optimizing their intervention strategies (jamming or relaying) and the corresponding transmit power at eavesdropper. The formulated problem is shown to be a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard in general. By identifying the separable structure of the formulated problem, we decouple the complex MINLP problem into two subproblems: 1) a jamming subproblem; and 2) a relaying subproblem. These two subproblems are then solved by further recasting them into a combinational problem and a typical concave optimization problem, respectively. Numerical simulations show that our proposed approach can achieve higher eavesdropping rate than conventional eavesdropping approaches.

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) can effectively improve the computing ability and energy sustainability of energy-constrained wireless devices in the Internet of Things (IoT) networks. Intelligent reflecting surface (IRS) has recently emerged as an effective technique to improve the performance of wireless systems by intelligently reconfiguring wireless environments. This paper studies the exploitation of IRS to improve the secure computation performance of WPT-MEC systems with a passive eavesdropper. A wireless access point (AP) first charge multiple users with the emitted energy signals, and then the users perform local computing and partial offloading to complete their computation tasks with the harvested energy in the presence of an eavesdropper, where the local computing can be executed during the whole process of WPT and offloading. Meanwhile, deploying IRS can improve the energy capture and secure offloading performance of the users. We maximize the secure computation task bits of users by jointly optimizing the AP energy transmit beamforming, the IRS phase shifts, the transmit power, users’ offloading time, and the local computation frequency of users, which are tangled with each other. An iterative optimal algorithm is developed to solve this non-convex problem by combining Taylor expansion method, semidefinite relaxation (SDR) algorithm, the Lagrange duality theory and Karush-Kuhn-Tucker (KKT) conditions. The numerical results show that the proposed scheme can effectively increase the secure computation task bits compared with other benchmark schemes, especially for the maximum transmit power of AP, the improvement is above 45 $\%$ .

This paper studies the wireless surveillance of multiple suspicious communication links. We propose a cooperative eavesdropping scheme, where a primary legitimate monitor (PLM) and an assistant legitimate monitor (ALM) cooperatively jam the suspicious links for an effective proactive eavesdropping. The objective is to maximize the eavesdropping energy efficiency (EEE) of our proposed cooperative eavesdropping scheme, by optimizing jamming power allocation and the cooperative strategy of legitimate monitors (LMs) subject to several constraints, i.e. energy transfer constraint and eavesdropping load balancing constraint. Furthermore, the formulated problem is a difficult mixed integer nonlinear programming (MINLP) problem and therefore, is non-convex and combinational. To solve the problem efficiently, a threshold-based policy of optimal jamming power allocation is proposed. Therefore, the original optimization problem is decoupled into two sub-problems: the selection of optimal eavesdropper under the given jammer and the whole EEE maximization over the selection of optimal jammer. Numerical results show that our proposed cooperative eavesdropping achieves higher EEE and eavesdropping rate than non-cooperative proactive eavesdropping.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) have attracted much attention as promising technologies for the next-generation mobile networks and Internet of Things (IoT). In this article, we investigate how to improve the security of the MEC system with the assistance of IRS and artificial noise (AN) in the IoT. By adjusting the phase of the IRS, the users’ signals can be enhanced and the eavesdroppers’ signal can be weakened. In addition, the full-duplex base station (FD-BS) transmits AN to destroy the eavesdroppers’ signal and further enhance the users’ security. We minimize the users’ secure energy consumption by jointly optimizing the base station receive beamforming vectors, AN covariance matrix, IRS phase shifts, users’ offloading time, transmit power, and local computation tasks. The formulated problem is a nonconvex problem that is hard to solve directly, so we decompose it into tractable subproblems and develop an alternating optimization approach by combining the semidefinite relaxation (SDR) algorithm and Dinkelbach’s method. The results show that the proposed scheme can greatly reduce the secure energy consumption compared with other benchmark scheme.