Search arXivSearch

arXiv · 2608.29036

SmartBAN on Silicon by Structured Behavioral Modeling

Abstract

Wireless body area networks (WBANs) are a key enabling technology for the Internet of Medical Things (IoMT). SmartBAN, standardized by ETSI and later adopted as an IEC international standard, defines a lightweight WBAN protocol with time-division multiple access (TDMA)-based physical (PHY) and media access control (MAC) layers, yet no implementation on commercial hardware has been reported. The standard specifies frame formats and channel structure but leaves internal device behaviors unspecified: phase control and connection lifecycle lack transition logic, while slot-level timing and scheduling policy lack parametric guidance. This paper addresses these omissions through structured behavioral modeling and model-driven implementation. Two Mealy-type finite automata -- one for the Hub (3 states, 5 transitions), one for each Node (5 states, 8 transitions) -- capture phase control and connection lifecycle as a hardware-independent design blueprint whose transition tables map directly to firmware dispatch logic; slot-level timing and scheduling policy are resolved through realization on the nRF54L15, a commercial Arm Cortex-M33 wireless system-on-chip (SoC) running Zephyr real-time operating system (RTOS). Experiments with sixteen concurrently scheduled sensor nodes over 25 hours validate the design for the initial connection and uplink data paths: all 13 modeled transitions were exercised with sub-millisecond per-slot timing jitter ($P_{99} <$ 754 $μ$s, slot-independent across all 16 slots), 99.99% packet delivery, and autonomous disconnection recovery. A same-SoC Bluetooth Low Energy (BLE) comparison quantifies the determinism-efficiency tradeoff: SmartBAN achieves substantially lower timing jitter at higher energy cost, the majority of which is attributable to software radio processing rather than the protocol-level duty cycle.

Explore related subjects

Keep this discovery

BibTeXRIS

Masato Yoshimi, Takahiro Ito, Kento Tanaka, Hirokazu Tanaka. 2026-08-29. SmartBAN on Silicon by Structured Behavioral Modeling. https://arxiv.org/abs/2608.29036

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

AEGIS: Risk-Budgeted Online Scheduling for Resilient Continuous Edge Inference

Continuous edge inference requires sustained wireless and computing support across successive service instances. Under recurring channel degradation, transient edge overload, and multi-user contention, isolated deadline misses may accumulate into persistent service degradation. Existing schedulers mainly optimize instantaneous latency or per-timeslot utility and provide limited control over such cross-time effects. To address this issue, we propose AEGIS (Adaptive Exposure-Governed Inference Scheduling), a risk-budgeted online framework for service-level operational resilience. AEGIS regulates predicted deadline-risk exposure through dynamically replenished per-user risk budgets and establishes an explicit finite-horizon bound on cumulative admitted-risk exposure. One-step state estimation supports anticipatory delay and risk assessment, while the centralized bandwidth--computing allocation is transformed into an exact-potential formulation and solved through asynchronous coordinate updates. Simulation results demonstrate that AEGIS enhances timely-service continuity, contains persistent deadline violations, and improves post-stress recovery through adaptive cross-time risk regulation. Meanwhile, it effectively controls predicted-risk exposure while preserving competitive service performance, achieving a favorable balance between service resilience and risk control.

cs.NI

Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions

Sovereign network functions, e.g., routing protocols, are becoming increasingly complex and susceptible to failures arising from protocol configuration anomalies and anomalous configurations. This paper interprets the protocol configuration anomaly detection problem as detection of structural inconsistencies of connected nodes and edges in a bipartite graph that captures both physical network entities and logical protocol states. This graph structural inconsistency detector (GSID) model is proposed to solve the problem efficiently. To handle the heterogeneous nature of protocol configuration parameters, GSID employs an adaptive configuration encoder (ACE) that dynamically selects encoding strategies per parameter to preserve fine-grained numerical discrepancies. To expose the subtle inconsistencies of connected nodes and edges in the bipartite graph, GSID uses an inconsistency dynamic attention (IDA) mechanism that scores edges by drawing asymmetric attentions from both ends, rule compliance from one end and route connectivity from the other. It is demonstrated experimentally that GSID outperforms state-of-the-art baselines by threefold in F1 score and by 23.2% in accuracy. Ablation studies validate the effectiveness of both the ACE and IDA modules. Tests on unseen network scales and real-world network topologies show the superior adaptability of our GSID, compared to the baselines.

cs.NI