Delivering Augmented Reality to the Edge: An Approach Toward Object Recognition through the In-Network Computing Paradigm

Recent advancements in 5G technology have enabled significant breakthroughs in applications requiring high data throughput and low latency, such as Mobile Augmented Reality (MAR). Nevertheless, mobile devices tend to be computationally and energy-constrained, limiting their capability to provide synchronous interactions between the real environment and visual augmentations. Thus, in this work, we propose a collaborative inference method to run compute-intensive AR tasks, such as object recognition, on heterogeneous devices at the network edge. This approach would benefit both end-users and service providers by reducing end-to-end latency and operating costs, respectively.