ioned, we define computation efficiency as the total number of calcu- lated bits divided by total energy consumed. Thus, [173], where B is the bandwidth for offloading. Figure 8.2 Time sharing offloading scheduling. Offloading Computing results downloading 8 Toward Green MEC Offloading with Security Enhancement 8.3 Computation Efficiency Maximization with Active Eavesdropper We consider a green MEC system where the objective is to maximize the computation effi- ciency; the optimization problem is formulated as follows. (8.4a) s.t. C1 24st, (8.4b) (8.4c) (8.4d) (8.4e) C6 : 0 AK (8.4f) Our objective is to find the maximum value of the weighted summation for each UE's computation efficiency, Wk is the weight for UE k. The variables to be optimized here are the transmitted time for each UE tk, the CPU frequency fk, and the transmitted power for each user Pk (8.4b) is the time constraint which requires the whole process ends before time T, (8.4c) combined with (8.4d) is the requirement for offloading and local computing rate. Furthermore, (8.4e) is the energy consumption constraint for each UE, where Eth is the maximum allowed energy. Lastly, fk,tk, and Pk should be non-negative variables, which is defined in (8.4f).¹ Clearly, the formulated problem is non-convex, due to its sum-of-ratio objective function and C2, C4, C5, especially the coupling variables Pk and tk. In the following, we tackle each non-convex term, mainly with successive convex approximation (SCA). 8.3.1 SCA-Based Optimization Algorithm Firstly, we transform (8.4c) and (8.4e). Notice that we group these two constraints since they both involve with coupling variables. Let = Pktk, then = log(1+. For a function in the form of f(x,y) it represents the entropy between x and y, and it is a concave function. Thus, Rsec is still non-convex due to the difference of concave and convex functions. For approximation, we apply SCA 1 Theoretically, mk should be an integer value, and (8.4d) becomes mk E 1, Lk} However, we simplify this and allow mk to be fractional v