Real-time bidding optimization in AdTech using edge-embedded systems

Gupta, Rahul (2025) Real-time bidding optimization in AdTech using edge-embedded systems. World Journal of Advanced Research and Reviews, 26 (1). pp. 216-225. ISSN 2581-9615

Integrating edge-embedded systems into real-time bidding workflows represents a transformative advancement in programmatic advertising. This architectural paradigm significantly reduces latency and enhances decision-making speed in the time-sensitive digital advertising ecosystem by decentralizing computations to local edge nodes positioned closer to data sources. Traditional RTB architectures relying on centralized data centers face inherent limitations that negatively impact campaign performance, particularly during high-volume periods and for geographically distant users. Edge-embedded approaches address these challenges through distributed processing frameworks that maintain linear scalability while improving bid response times, optimizing infrastructure efficiency, and facilitating compliance with evolving privacy regulations. The multi-tier architecture—comprising edge nodes, regional processing hubs, and a central coordination layer—enables rapid local decisioning while preserving global orchestration benefits. Beyond performance advantages, this decentralized structure offers inherent privacy benefits through reduced data transit, granular access controls, and region-specific processing capabilities. As hardware capabilities evolve, further opportunities emerge through edge-based model training, hybrid decision systems, and cross-platform coordination strategies.