Optimized Contextual Data Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) is a new computing paradigm that moves computing resources closer to the user at the edge of the network. The aim is to have low-latency, high bandwidth, and to improve energy consumption when running computational tasks. The idea of deploying MEC servers near to the users along the 5G technology has led to open an interest in the field of Vehicular Network (VN). MEC servers can play significant roles in improving the performance of VN applications. In this environment, offloading computational tasks over collected contextual data by the mobile nodes (Autonomous Vehicles (AV)) meets the challenge of when & where to offload the collected data while on the move. In this work, we modeled the problem of offloading contextual data to the MEC servers as an optimal stopping problem. Our objectives are to offload to a MEC server with lower execution time and before the collected data get stale. We evaluated our model using real mobility trace with real servers' utilization; the results showed that the proposed model outperforms other offloading methods.