Timely Probabilistic Data Preprocessing in Mobile Edge Computing

Abstract

A combination of mobile edge computing (MEC) and cloud computing paradigms has the potential to greatly alleviate the challenges facing Internet of Things (IoT). We consider a tiered IoT infrastructure in which data generated by an IoT sensor/device is delivered to a data center for processing through an intermediate MEC server. The MEC server can either directly transmit the data to the data center or pre-process the data and then transmit it to the data center over a shared channel. The goal is to maintain the freshness of the data delivered to the data center. In this paper, we assume a probabilistic model for pre-processing by the MEC server. Sensor data is assumed to be generated as a Poisson process and the transmission times over the two paths are assumed to have general distributions.We use Age of Information (AoI) as a measure of data freshness at the data center. We perform stationary distribution analysis in this system and obtain closed form expressions for average AoI and average peak AoI. We focus on selecting the offloading probabilities in conjunction with the mean service times for each server for optimal operation determined by average AoI and peak AoI. Our numerical results show the effect of path diversity in the selection of best offloading probability and service times.