A context-aware cognitive system is a prime requirement for a sensor rich smart home environment. In this paper, we discuss the development and evaluation of a self-sustaining cognitive architecture for the RUBICON (Robotic UBIquitous COgnitive Network) system which builds its knowledge as per the environmental situations. The proposed cognitive architecture consists of a reasoning module, a decision module, and a supporting memory module. An online sliding-window based self-organising fuzzy neural network (SOFNN), which explores relationships between the event inputs and desired reasoning outputs, is developed for the reasoning module. We also propose a prediction model based on event information to support the reasoning module for continuous training in the absence of external training data. The decision module generates control goals for the robots according to the status outputs from the reasoning module. We develop a MySQL based database for the memory module which supports the overall system by storing processed information about the states of the environment and providing historical information for enhanced understanding. The architecture is trained and tested with environmentally realistic synthesized data to show its adaptation capabilities. The results demonstrate that the proposed system can learn activities and track them within a smart home environment. This initial implementation also highlights the potential of the architecture and will serve as a very important test-bed for future work. We envisage that the proposed combination of the prediction model and the reasoning module will eventually result in a general purpose, self-sustaining, self-organising cognitive architecture for different applications and thus the proposed architecture enters into the sphere of the biologically inspired cognitive architecture (BICA) challenge.