RCoD: Reputation-Based Context-Aware Data Fusion for Mobile IoT.

The rapid development of mobile sensing technologies (e.g., smart devices embedded with various powerful sensors) has encouraged the proliferation of the Internet of Things (IoT). Although data reliability and accuracy are crucial in many sensor applications (e.g., air-quality monitoring), it is often difficult to ensure these properties. Mobile IoT's people-centric architecture allows for more inaccurate and corrupted data. In this manuscript, we are addressing the problem of how to predict data more accurately in the presence of malicious participants who inject false data to manipulate the system. Our goal is to recover those missing or imprecise data values from the correlated data streams. To do so, we propose a Reputation-Based Context-Aware Data-Fusion (RCoD) mechanism that is resilient against on-off and data-corruption attacks. Furthermore, the Contextual Hidden Markov Model-based data prediction facilitates more accurate real-time data prediction. We tested the scenarios where most participants were malicious, injecting false data at varied rates. Our method accurately identified the honest participants based on their reported data and context. We empirically evaluate the performance using Beijing's air-quality dataset. We compared the performance of our RCoD method against four state-of-the-art methods, and the results justify its superiority.