Network-aware communication-efficient fingerprint representation for resource-constrained IoT systems

Biometric authentication with Internet of Things (IoT) systems is constrained by low data bandwidth, packet size, and energy capabilities. The transmission of raw biometric data exceeds the maximum transmission unit (MTU) size of traditional IoT standards (e.g., IEEE 802.15.4), resulting in large packet fragmentation and a high frame collision probability. We present a network-aware payload optimization method to minimize the application layer payload. thereby reducing airtime (channel occupation time) and improving spectrum efficiency. By using skeleton bitmaps and minutiae vectors, the size of the data is decreased by 70%–98% compared with raw images. A smaller payload reduces the protocol header overhead and ARQ for lost packets in lossy wireless environments. We introduce a distributed edge computing architecture for offloading data-intensive tasks from the core network to the network edge, thereby reducing backhaul traffic. Performance tests with a network simulator (NS-3) in Wi-Fi 6 (IEEE 802.11ax) and IEEE 802.15.4 scenarios reveal that transmission times are significantly reduced from 420–520 to 150–190 ms and energy consumption from 110–160 to 65–95 mJ by reducing the payload size from 120 to 35 kB or even further down to 2.4 kB. These results indicate that network-aware, communication-efficient biometric data representations enable scalable and energy-efficient IoT authentication. This strategy emphasizes the importance of minimizing transmitted data volume through network performance metrics in limited wireless scenarios. The results provide architectural guidelines for designing secure and low-latency biometric-based authentication systems in smart homes, healthcare monitoring, and industrial IoT applications, emphasizing network-centric optimization for resource-constrained IoT networks.