Memristors exhibiting both volatile and non‐volatile resistive switching (RS) behaviors hold significant potential for their utilization as artificial neurons and synapses in neuromorphic computing systems, respectively. Inkjet printing presents an economical, low‐material‐waste, and compatible process for the fabrication of solution‐based memristors, which are essential for flexible, wearable and biocompatible applications. However, inkjet‐printed memristors typically exhibit low endurance (below 10 ⁴ cycles) and high switching currents (above 100 µA), both of which represent critical limitations for the practical implementation and low‐power operation of neuromorphic computing systems. In this work, a hexagonal boron nitride (h‐BN) based memristor formulated via liquid phase exfoliation (LPE) and fabricated with inkjet printing demonstrates exceptional electrical performance with a remarkable endurance of 2.2 million cycles at very low currents (below 1 µA) in a volatile regime under a current‐driven approach. This result surpasses the highest endurance reported to date on inkjet‐printed‐based memristors, surpassing the threshold of one million cycles. At higher currents (above 10 µA), the same devices exhibit stable non‐volatile RS with an endurance of 70 000 cycles. This study not only demonstrates the remarkable performance of the designed memristors, but also opens new avenues for current‐driven memristive electrical characterization and neuromorphic applications.

The continuous expansion of the Internet of Things (IoT) has intensified the need to evaluate and guarantee the quality of entropy sources used in random number generation, an essential element in securing communications used in IoT ecosystems. This work presents an automated and web-based framework designed to execute and analyze the results of statistical tests defined in the NIST SP 800-22 standard, enabling systematic assessment of entropy sources and random numbers generators in IoT devices and environments. The proposed system integrates a Python-based backend built upon an optimized implementation of the original NIST suite, along with an intuitive web interface that facilitates configuration, monitoring, and parallel execution of tests through Representational State Transfer (REST) endpoints. Session management based on Redis ensures reliable and concurrent operation of multiple users or devices while maintaining isolation and data integrity. To demonstrate its applicability, an emulated IoT ecosystem was implemented in which multiple virtual devices periodically and asynchronously request real-time validation of their local random numbers generators. The obtained results confirm the system’s capability to detect deficiencies in pseudo random generators and validate true random number sources, highlighting its potential as a diagnostic and verification tool for distributed IoT security systems. The tool developed in this work is fully accessible to the public, allowing researchers, engineers, and practitioners to evaluate random number generators without requiring specialized hardware or proprietary software.