Implementation of Self-adaptive Middleware for Mobile Vehicle Tracking Applications on Edge Computing

Unstructured data gathered from various IoT sensors is rapidly increasing due to inexpensive electronic devices and high-speed networks. On the other hand, mobile edge computing (MEC) is an attractive data processing method that can shorten the communication distance and reduce the latency of computation-intensive tasks by distributing data to the edge servers close to the users, unlike processing data on clouds that are located far from users. In the present paper, we propose a specialized self-adaptive middleware for reconfiguration of image/video contents for adaptation to changes with the movement of a vehicle. The key concept behind this approach is to introduce the rule-based relocation of objects among sensor devices, edge servers, and existing clouds as a basic adaptation mechanism to recognize and track mobile vehicles. Experimental results show that tracking precision with a state-of-the-art tracker is up to 89% for MEC.