The rapid growth of Internet of Things (IoT) devices has revolutionized various sectors, including healthcare, transportation, and smart homes, enabling seamless connectivity and automation. However, this expansion also brings significant security challenges, especially in safeguarding sensitive data on devices with limited resources. Cryptographic techniques, relying on secure and resource-efficient pseudo-random bit generators (PRBGs), are crucial for addressing these issues, to efficiently generate cryptographic keys and random numbers while minimizing resource consumption. However, conventional PRBGs often fall short in the context of IoT due to their resource-intensive nature, leading to potential vulnerabilities and performance issues. One-dimensional ( \(1\) D) discrete chaotic map-based PRBGs are highly suitable for IoT applications due to their lightweight structure, high randomness, and low computational overheads. This paper proposes a lightweight \(1\) D chaotic map-based PRBG for securing IoT devices. The PRBG has been comprehensively assessed for \(22\) one-dimensional discrete chaotic maps and implemented in MATLAB for software-based evaluation and further realized on the state-of-the-art ARM Cortex-M \(3\) -based LPC \(1768\) IoT development board. The implementations were assessed in terms of computational complexity, execution time, throughput, and statistical randomness. The results show that the proposed PRBGs achieved minimal operation counts, reduced execution times, and significantly higher throughput compared to existing generators. Randomness quality was verified using the NIST SP \(800-22\) statistical test suite, with all proposed generators achieving pass ratios above the recommended thresholds. Furthermore, the hardware comparative analysis of the PRBGs has been performed in terms of several lightweight metrics, including RAM and ROM memory footprint, execution speed, current, power, and energy consumption. The findings illustrate that the enhanced logistic map-based PRBG has smaller memory requirements and operates with reduced current and power consumption. Quadratic map-based PRBG demonstrates the fastest execution time while also maximizing energy efficiency. The overall findings demonstrate that the proposed \(1\) D chaotic map-based PRBGs combine computational efficiency, statistical robustness, and hardware practicality, thereby positioning the proposed PRBG as an optimal solution for resource-constrained IoT applications, delivering high performance across key metrics.
