The context-aware computing paradigm introduces environments, known as smart spaces, which can unobtrusively and proactively assist their users. These systems are currently mostly implemented on mobile platforms considering various techniques, including ontology-driven multi-agent rule-based reasoning. Rule-based reasoning is a relatively simple model that can be adapted to different real-world problems. It can be developed considering a set of assertions, which collectively constitute the working memory, and a set of rules that specify how to act on the assertion set. However, the size of the working memory is crucial when developing context-aware systems in resource constrained devices such as smartphones and wearables. In this paper, we discuss rule-based context-aware systems and techniques for determining the required working memory size for a fixed set of rules.