d by either scheme with respect to the whole task. It can be readily shown that for both UEs, the local computing amount (in percentage) will decrease with the increase of the preset data amount. By contrast, data offloading plays a more and more important when the data becomes large. This can further prove our point in Figure 7.1, where the proposed scheme adaptively adjusts the amount of data that go through local computing or offloading. Additionally, for UE 1, the trade-off point happens around L = 4 X 104 and for UE 2 around L2 = X 104. Since UE 1 has a better channel gain than UE 2, the influence from data offloading is more prominent; thus, the trade-off point is in an earlier position, while for UE 2, local computing continues to have a more influential role until the trade-off point L2 = 6 x 10 4. 7 Mobile Edge Computing in 5G Wireless Networks UE1 Local computing UE1 Offloading UE2 Local computing UE2 Offloading Required computing data size (bits) Figure 7.3 Trade-off between offloading and local computing. 7.5 Summary In this chapter, we present a new evaluation metric in MEC systems, i.e. the computa- tion efficiency. An optimization problem is formulated which aims at maximizing the total computation efficiency with weight factors. The problem is recognized as the sum-of-ratio problem and an iterative algorithm is applied in the outer loop. For the inner loop, the problem can be converted to standard convex optimization and, to gain a better insight, we propose to solve it via gradient descent method. Simulation results reveal the fundamental trade-off of two combined schemes: local computing and offloading. Toward Green MEC Offloading with Security Enhancement 8.1 Background In Chapter 7, our work proposed a new computation efficiency metric for evaluating MEC performance. Computation efficiency is similar to energy efficiency in wireless communi- cation, which is defined as the number of bits computed divided by the total energy consumed. Several subsequent studies have adopted our metric and studi