The low computational capacity of mobile devices has become the main performance bottleneck for emerging computing-intensive and delay-sensitive applications. In this paper, we propose a peer-to-peer (P2P) enhanced task scheduling framework to minimize the average task duration in device-to-device (D2D) enabled mobile edge computing (MEC) network by jointly optimizing the task scheduling decision and the computational resource allocation. Our proposed framework can work in different modes in different application scenarios. In the MEC mode, an iterative algorithm is presented based on alternating optimization technology and sorting algorithm, which can derive a near-optimal solution of the original mixed-integer non-linear programming (MINLP) optimization problem. In the P2P mode, we can reformulate the original problem to a typical one-to-one assignment problem, which is solved by a task scheduling algorithm based on the Hungarian method. In more challenging hybrid mode, we make some simplifications based on reformulation linearization technology. Then, the near-optimal solution is obtained by our proposed scheduling algorithm based on the alternating direction method of multipliers (ADMM) method and difference of convex functions (D.C.) programming. Numerical results show that our proposed framework has shorter average task duration under different scheduling modes compared with the other scheduling schemes.

In this article, we raise the concern on secure energy efficiency (SEE) over the wireless-powered untrusted relay networks with nonlinear energy harvesting (EH). To achieve data confidentiality and secrecy performance enhancement concurrently, the untrusted relay can enable simultaneous energy signal reception and confidential information forwarding during the second phase, where no dedicated energy transmission phase or relaying signal splitting is needed for the energy supply. In particular, with the help of destination jamming, the relay can also harvest energy from source signal and jamming signal during the first phase, which is different from conventional relaying protocol that only harvests energy from source and recycled energy. Considering the nonlinear EH efficiency, our objective is to maximize the SEE through piecewise region selection and power allocation. To address this intractable nonconvex problem, a two-layer algorithm based on optimization decoupling, fractional programming, Lagrange dual decomposition and difference of convex functions (DC) programming is proposed. The core idea of the proposed algorithm is to convert the primary problem into simple subproblems by sequentially exploiting the above-mentioned optimization methods. Simulation results reveal the superior secrecy performance of the proposed scheme. The proposed algorithm achieves near-optimal solutions with faster convergence rate.