Machine Learning-Based Silent Entity Localization Using Molecular Diffusion

Molecular communications has recently emerged as a new form of information transfer that uses chemical signals as information carriers. Alongside their novel applications in communications theory, chemical signals may also be utilized for various other applications, such as abnormality detection, direction finding, and entity localization. Among localization tasks, current literature mainly focuses on locating active entities that emanate chemicals, whereas the localization of a silent entity (e.g., an eavesdropper) is rarely considered. Exploiting the fact that different positions of a silent entity yields different received signals at the sensing device, this letter introduces a machine learning-based approach to detect the presence of a silent entity and localize it. Overall, the study shows that such a localization task is also achievable in cases where a clear analytical formula characterizing the received signal is not available, and provides a framework for further research on silent entity localization approaches.