Survey of Context Caching Techniques for IoT: Challenges, and Future Directions

The proliferation of context-aware IoT applications has intensified the need for efficient strategies for caching context. The novel contribution of this survey is a taxonomy of context caching parameters that maps every lifecycle decision (placement, refresh, replacement, eviction) to the variables that drive it. Unlike traditional caching, which often handles static content, context caching must cope with challenges such as high data volatility, short validity windows, heterogeneous data sources, and application-specific freshness requirements. As IoT applications scale in complexity, their demand for timely and relevant context grows accordingly. The survey critically examines the current state of the art in context caching for IoT by analyzing how caching techniques must evolve to handle semantically inferred, multi-source context under constraints of freshness, utility, and cost. To structure the review, we adopt a lifecycle-based approach comprising four stages, namely placement, refresh, replacement, and eviction. The proposed taxonomy enables systematic decision-making at each lifecycle stage and encompasses parameters such as context volatility (how frequently context changes), access probability (likelihood of future requests), semantic relevance, and Quality of Context (QoC), providing a unified framework for monitoring, prioritizing, and evaluating cached context. Building on this taxonomy, we contribute a comparative analysis of recent context caching works (2019–2025) mapped to lifecycle stages, monitored parameters, evaluation metrics, and adaptivity mechanisms, and discuss the privacy, security, and multi-tenancy concerns that traditional caching surveys do not address. Our analysis shows that traditional IoT data caching methods, although designed for dynamic data, cannot adequately address the challenges of caching context, which is transient and semantically inferred from multiple IoT data sources. Finally, we identify unresolved challenges in context cache management and present them as open research questions.