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Mobile Augmented Reality (MAR) applications face performance challenges dueto their high computational demands and need for low-latency responses.Traditional approaches like on-device storage or reactive data fetching fromthe cloud often result in limited AR experiences or unacceptable lag. Edgecaching, which caches AR objects closer to the user, provides a promisingsolution. However, existing edge caching approaches do not consider AR-specificfeatures such as AR object sizes, user interactions, and physical location.This paper investigates how to further optimize edge caching by employingAR-aware prefetching techniques. We present SPAARC, a Spatial Proximity andAssociation-based Prefetching policy specifically designed for MAR Caches.SPAARC intelligently prioritizes the caching of virtual objects based on theirassociation with other similar objects and the user's proximity to them. Italso considers the recency of associations and uses a lazy fetching strategy toefficiently manage edge resources and maximize Quality of Experience (QoE).  Through extensive evaluation using both synthetic and real-world workloads,we demonstrate that SPAARC significantly improves cache hit rates compared tostandard caching algorithms, achieving gains ranging from 3% to 40% whilereducing the need for on-demand data retrieval from the cloud. Further, wepresent an adaptive tuning algorithm that automatically tunes SPAARC parametersto achieve optimal performance. Our findings demonstrate the potential ofSPAARC to substantially enhance the user experience in MAR applications byensuring the timely availability of virtual objects.

SPAARC: Spatial Proximity and Association based prefetching for Augmented Reality in edge Cache