thm 8.1 Secure Computation Efficiency Maximization Algorithm 1: Initialization: the algorithm accuracy indicator and U2, set i = 0, give initial values for BQ. 2: while while|ak(j+1)-ak(j)l>u2do Solve for problem P4, obtain the intermediate optimal values {Nk}. Let tj=j+1. end while Let i = i + 1, update auxiliary variables Ak(i+1) and Bk(i+1) from (8.12) and (8.13). 8: end while 9: Output the optimal computation efficiency. 8.4 Numerical Results In this section, simulation results from our proposed scheme and algorithm are presented. Parameters for the simulation are given as follows. We assume the bandwidth for the system 8 Toward Green MEC Offloading with Security Enhancement is B = 200 KHz, number of users K = 2, time threshold T = 1s. For local computation, the CPU needs Ck = 1000 operations to process one bit of data. In addition, the scaling fac- tor for energy consumption Ek = 1 x 10-24, = CPU for each user has a maximum frequency fmax = 1 x 10 = Hz. For offloading, we assume the channel from user to the server to be h2/o2 = Hkh, where ho is the normal Gaussian variable. Similarly, 82/02 = Gkho. The value of Hk and Gk will be given later. We apply no bias to both users hence set W1 = W2 = 1. Lastly, the maximum allowed energy consumption is Ek = 1 Joule. In Figure 8.3, we show the convergence performance of our iterative algorithm. Here, we set H = 7,H2 = 5, G1 = G2 = 1, and L1 = L2 = {50,000,60,000}. As a typical case, we only present the result for optimal time allocation tk here. It can be seen that our iterative algo- rithm has a good convergence speed; it only takes around 6 iterations to achieve the optimal value. In fact, we test with different initial points and manage to get the same performance. The other observation from Figure 8.3 is that, when the required computation bits becomes larger, the transmission time is also longer. Intuitively, this explains that more offloading bits are required. In Figure 8.4, the computation efficiency under different Eves channels is presented. The x-axis is th