Mobile edge computing (MEC) sinks the functions and services of cloud computing to the edge of the network to provide users with storage and computing resources. For workflow tasks, the interdependency and the sequence constraint being among the tasks make the offloading strategy more complicated. To obtain the optimal offloading and scheduling scheme for workflow tasks to minimize the total energy consumption of the system, a workflow task offloading and scheduling scheme based on an improved genetic algorithm is proposed in an MEC network with multiple users and multiple virtual machines (VMs). Firstly, the system model of the offloading and scheduling of workflow tasks in a multi-user and multi-VMs MEC network is built. Then, the problem of how to determine the optimal offloading and scheduling scheme of workflow to minimize the total energy consumption of the system while meeting the deadline constraint is formulated. To solve this problem, the improved genetic algorithm is adopted to obtain the optimal offloading strategy and scheduling. Finally, the simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes.

In this paper, we consider a backscatter communication (BackCom)-based cognitive network that consists of one primary transmitter, one primary receiver, multiple secondary transmitters (STs), and one secondary receiver (SR). Each ST operates in the BackCom or energy harvesting model. Our goal is to jointly optimize the energy harvesting and backscatter time, the transmit power of the primary transmitter, and the power reflection coefficient of each ST to maximize the sum throughput of all the STs under a nonlinear energy harvesting model while satisfying multiple constraints, i.e., the energy causality of each ST, the quality of service of the primary transmitter, etc. The formulated problem is nonconvex due to the coupled variables and is hard to solve. In order to address this problem, we decouple partially coupled variables by using the properties of the objective function and constructing auxiliary variables, and the remaining coupled variables are decoupled via successive convex approximation (SCA). On this basis, a SCA-based iterative algorithm is developed to solve the formulated problem. Simulation results are provided to support our work.