e number of required total computation bits. The first observation is that, under all scenarios, computation efficiency decreases with the increasing data size. If we break down the two parts for computation efficiency, we can easily see that pure local computing efficiency is square proportionally decreasing with the increasing of data size. Also, if the bit size is large, offloading part is also decreasing, due to, in part of the circuit power in the denominator. Additionally, Figure 8.4 also shows the relationship between computation efficiency with security threads from Eve. Specifically, we set channels from Eve to users to be different. t1, L = 50,000 bits t2, L = 50,000 bits t1, L = 60,000 bits t2, L = 60,000 bits Iterations Figure 8.3 Iterative algorithm convergence analysis. 8.4 Numerical Results X 106 G1 = G2=1 G1= G2=0 G1 G2 = 0.5 Required computing data size (bits) X 104 Figure 8.4 Computation efficiency VS. required computation bits under different Eve channels. If the channel of Eve is stronger, we see a setback in the performance; this is due to the impact from offloading, where achievable data rate is smaller. Lastly, we compare our proposed joint offloading and local computation scheme with other two schemes: local computing only and offloading only. Here, we set G1 = G2 = 1 (Figure 8.5). Clearly the proposed scheme outperforms both other two in terms of Proposed joint scheme Offloading only Local computing only Required computing data size (bits) Figure 8.5 Computation efficiency vs. required computation bits under different computation scheme. 8 Toward Green MEC Offloading with Security Enhancement computation efficiency, which verifies the superiority of our scheme. Similar efficiency decreasing is also observed. 8.5 Summary In this work, we studied the computation efficiency for a joint offloading and local comput- ing scheme under possible Eve. We model the effect from Eve with physical layer security and mutual entropy. An optimization problem is formulated which considers some prac- tical 