Performance Characterization of Electromagnetic Nano- Networks Using Packet-Level Traffic Analysis
Hajar Ait Lamkademe, Ahmed Naddami
Progress in Signals and Telecommunication Engineering, Vol. 15, No. 1, 2026.
DOI: https://doi.org/10.6025/pste/2026/15/1/26-42
Link: https://www.dline.info/pste/fulltext/v15n1/pstev15n1_3.pdf
Abstract:
This study characterized electromagnetic nano-network performance through packet level traffic analysis
of 876 simulated transmission events within the Terahertz band (0.1-10 THz), offering critical insights for
Internet of Nano Things (IoNT) development. Key findings revealed that traditional networking assumptions
often fail at the nanoscale: packet size showed no significant correlation with latency (r=0.019, p=0.579),
indicating propagation and processing delays dominate over serialization time. Similarly, signal strength
did not predict error rates (r=-0.009, p=0.792), suggesting environmental factors like molecular absorption
or interference outweigh simple path loss models in THz communications. Bandwidth utilization demonstrated
minimal discriminatory power between normal and anomalous traffic, with only a 6.1% mean difference
lacking statistical significance (p=0.155), underscoring the need for multi feature machine learning
approaches in nano network security rather than single metric thresholds. Protocol comparison highlighted
distinct trade offs: TDMA achieved the lowest latency (2.717 ms) and highest bandwidth (5.764 Mbps), while
CSMA minimized error rates (0.478%). These results suggest protocol selection must align with specific
application requirements, such as latency sensitive healthcare monitoring versus error critical data
transmission. While based on simulated data, this work establishes a foundational understanding of nanocommunication
dynamics, emphasising that optimising electromagnetic nano networks requires embracing
the unique physical constraints of the Terahertz band. Future research should prioritize experimental
validation using physical testbeds and explore advanced multivariate models to enhance anomaly detection,
ultimately enabling robust, scalable IoNT ecosystems for next generation 6G healthcare applications and
beyond.