Multisensor-Driven Entropy Algorithm for TRB Generation in Cryptographic Applications of IoT Devices

Abstract

This study proposes two efficient algorithms for generating true random bits (TRBs) in Internet of Things (IoT) devices, utilizing linear and non-linear congruence equations in combination with sensor-derived inputs. The sensor data, including signals from light-dependent resistors, sound, temperature, and image sensors, are harnessed to introduce high-entropy, unpredictable variables into the generation process, thereby enhancing the randomness of the resulting bitstreams. The algorithms leverage commonly available IoT sensors to capture environmental noise, significantly improving security by reducing TRB predictability. Performance and random quality of the generated sequences are rigorously validated using the NIST SP 800-22 Statistical Test Suite, with all essential tests successfully passed. These results demonstrate that the proposed approach provides a cost-effective and resource-efficient solution for secure, robust TRB generation in constrained IoT environments, advancing the practical deployment of cryptographic systems in diverse, sensor-based low-power applications. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.