Proffering context awareness facilities to end-users is a long-term objective of Ambient Intelligence (AmI). A context-aware system has to know the context information and understand what exactly is happening in a particular environment in order to build and provide distinct capabilities to the users and the occupants of the environment. However there are multiple challenges in precisely capturing the context or activity details from sensors and actuators deployed in the environment. There are possibilities for aggregating wrong data due to various reasons at different levels (sensor, network, etc.). Several technologies and mathematical concepts are being recommended to substantially enhance the quality of the sensor/actuator data so that the decisions being derived out of the data collected are right. The concept of fuzzy finite automata is used by most of the researchers to solve these issues (trustworthiness and timeliness of the sensor data). But it has turned out to be a complex affair. In this paper, we have empowered Deterministic Finite Automata (DFA) with threading to enable correct decision-making out of data from multiple sensors/devices in any environment. In short, considering the multiplicity and heterogeneity of devices in our personal as well as professional environments, DFA with the parallelization capability is going to be the novelty and game-changer for precision-centric contextaware computing. The suggested method is verified and validated through a freely obtainable data set and the results obtained prove the simplicity and the correctness of our solution approach.