Mobile Edge Computing (MEC) is a new technology that facilitates low-latency cloud services to mobile devices (MDs) by pushing mobile computing, storage and network control to the network edge, thereby prolonging the battery lifetime of MDs. Besides, MEC aims to reduce latency and permit delay-sensitive applications in 4G communications. There is, therefore, a push to test MEC performance on existing cellular systems. With the recently available mobile platform for academia SINET, NII can now connect MDs to ESs through 4G. This project focuses on the implementation of a physical 4G-based MEC System for task offloading, in which with the goal of achieving face detection, MD partially offload tasks to the ES under the instructions dictated by the offloading algorithms. Accordingly, the objectives of this thesis are to prove the efficiency of LTE based MEC systems in the real world focusing on its performance in terms of latency and battery consumption.

Mobile Edge Computing (MEC) is a new technology that facilitates low-latency cloud services to mobile devices (MDs) by pushing mobile computing, storage and network control to the network edge (closer to MDs), thereby prolonging the battery lifetime of MDs. One of the main objectives of MEC is to reduce latency and permit delay-sensitive applications in 4G and in the future, 5G communications. To achieve this feat, MEC aims to build up a computing platform by deploying edge servers (ESs) on the network edge. There is, therefore, a push to test the MEC performance on existin