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.

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.

This article investigates how to exploit the cooperative mechanism between non-orthogonal multiple access (NOMA) user pairs to enhance the security of the mobile-edge computing (MEC) system. Considering the different delay requirements of Internet of Things (IoT) users, we propose a two-slot hybrid cooperative NOMA security (THCNS) scheme that utilizes the cooperative interference between NOMA user pairs to enhance the security of offloading. In the first time slot, the interference to the eavesdropper comes from the task signals (TSs) transmitted by the two users, while in the second time slot comes from jamming signal (JS) transmitted by the user who has completed the offloading tasks. The weighted sum secrecy outage probability (wSOP) and secrecy computation probability (SCP) are utilized to measure the security performance of users in the proposed scheme. To confirm the feasibility and applicability of the proposed scheme, we analyze the impact of some key parameters on users' security, including the users' local computation time and offloading time, transmit power, task allocation coefficients of two time slots. The analysis results indicate that the proposed scheme is more suitable for the case where the wiretap channel outperforms the main channel, and it can enhance the system security when selecting the optimal parameters. Finally, the numerical simulation results reveal how the analysis parameters affect the security performance of users and validate the theoretical analysis, it provides good insights about how to design parameters of IoT users to enhance their security performance in MEC system.

The security energy efficiency (SEE) is investigated in a two way-full duplex(TW-FD) relay assisted cognitive radio non-orthogonal multiple access (CR-NOMA) network. In order to improve system energy efficiency, the self-interference(SI) of FD can be regarded as potential source for relay to harvest energy. Our objective is to maximize security energy efficiency of the secondary user (SU) system subject to harvested energy and the quality of service (QoS) of the users. Specifically, the multi-objective optimization problem is decomposed into three subproblems, i.e. the optimization of transmit covariance matrix, power allocation and power splitting ratio, respectively. The problem with optimizing transmit covariance matrix is non-convex, hence the semi-determined relaxation algorithm based on the first-order Taylor series expansion function is proposed to solve it. Besides, the multi-objective iterative algorithm (MOIA) is further proposed to achieve joint optimal solution. The simulation results show that under the same constrains of power allocation and energy harvested, the proposed scheme of two way-full duplex self-interference harvest has significant performance gain on security energy efficiency over the self-interference cancellation.

Wireless communications are vulnerable to cyber attackers, which now have the flexibility to choose their type of attack. In this paper, combined with intelligent reflect surface (IRS), we jointly optimize base station beamforming and IRS reflected beamforming to counter smart attackers, thereby improving system security. Considering that attackers can flexibly choose their attack methods, such as jamming or eavesdropping, we make the base station intelligent by using reinforcement learning, which can predict the attack methods of attackers and choose whether to add artificial noise into the transmitted signals. At the same time, the interaction between the base station and the smart attackers are established as a non-cooperative game, the Nash equilibrium of the game is derived. Based on this, the base station anti-smart attackers strategy based on Deep Q-learning (DQN) is proposed, which can restrain the attack of the attacker to improve the security of the system. It can be verified from the simulation results that the proposed anti-smart attackers strategy can effectively enhance the secrecy rate of the wireless communication system, resist the attacker’s attack, and intelligently transmit artificial noise to improve system security.