  Context information is in demand more than ever with the rapid increase in
the number of context-aware Internet of Things applications developed
worldwide. Research in context and context-awareness is being conducted to
broaden its applicability in light of many practical and technical challenges.
One of the challenges is improving performance when responding to a large
number of context queries. Context Management Platforms that infer and deliver
context to applications measure this problem using Quality of Service (QoS)
parameters. Although caching is a proven way to improve QoS, transiency of
context and features such as variability and heterogeneity of context queries
pose an additional real-time cost management problem. This paper presents a
critical survey of the state-of-the-art in adaptive data caching with the
objective of developing a body of knowledge in cost- and performance-efficient
adaptive caching strategies. We comprehensively survey a large number of
research publications and evaluate, compare, and contrast different techniques,
policies, approaches, and schemes in adaptive caching. Our critical analysis is
motivated by the focus on adaptively caching context as a core research
problem. A formal definition for adaptive context caching is then proposed,
followed by identified features and requirements of a well-designed, objective
optimal adaptive context caching strategy.
