On a multisensor knowledge fusion heuristic for the Internet of Things.

Internet of Things (IoT) is envisioned as the interconnection of the Internet with sensing and actuating devices. IoT systems are usually designed to collect massive amounts of data from multiple and possibly conflicting sources. Nevertheless, data must be refined before being stored in a repository, so as information can be correctly extracted for further uses. Knowledge fusion is an important technique to identify and eliminate erroneous data from compromised sources or any mistakes that might have occurred during the extraction process. We propose a new multisensor knowledge fusion heuristic (MKFH) for IoT supporting the knowledge extraction and transfer needed to further knowledge management, also discuss the role of reinforcement learning over integration on a multi-application wireless sensor/actuator network (WSAN). Results shows that the proposed multisensor knowledge fusion heuristic is compatible with the IoT paradigm and enhances integration.