A predictive ant colony optimization routing protocol for correlated link stability in wireless body area networks.

BACKGROUND

Wireless Body Area Networks (WBANs) are essential for continuous health monitoring but face critical challenges due to dynamic link instability caused by body movements and strict bio-safety requirements. The fluctuating wireless channel leads to high packet loss, while radio frequency (RF) energy absorption, measured as Specific Absorption Rate (SAR), must remain below 1.6 W/kg to prevent tissue damage. Existing routing protocols often react to failures rather than predict them and rarely co-optimize reliability with thermal safety.

METHODS

This paper proposes PACO-CLS (Predictive Ant Colony Optimization with Correlated Link Stability), a novel routing protocol that proactively addresses both network dynamics and bio-safety. It leverages tri-axial accelerometer data from sensor nodes to classify body posture using a Gaussian Mixture Model (GMM). This posture state conditions a Vector Autoregressive (VAR) model, which predicts future link quality metrics (SNR, distance, SAR) over a 0.5-second horizon. These predictions drive an enhanced Ant Colony Optimization (ACO) algorithm, where path selection is guided by a multi-objective cost function. Crucially, the pheromone evaporation rate is dynamically modulated by the cumulative SAR, calculated via the Pennes bio-heat equation, to enforce thermal safety.

RESULTS

Extensive simulations in NS-3.40, calibrated with empirical data from the MySignals HW V3 platform, demonstrate that PACO-CLS significantly outperforms QC-TriL, LEACH-WBAN, and Q-Learning-based routing. It achieves a network lifetime of [Formula: see text] hours (a 40.3% improvement), an energy efficiency of [Formula: see text] Mb/J, a packet delivery ratio of [Formula: see text], and end-to-end latency of [Formula: see text] ms. Critically, it maintains an average SAR of [Formula: see text] W/kg, well below the safety threshold across all tested scenarios, including high interference ([Formula: see text] sources/m2) and mobility ([Formula: see text] m/s).

CONCLUSIONS

By integrating predictive analytics with thermal-aware mechanisms, PACO-CLS establishes a new benchmark for intelligent WBAN routing. Its ability to proactively avoid unstable links based on correlated motion analytics and simultaneously enforce bio-safety constraints makes it a robust and clinically viable solution. PACO-CLS consistently meets stringent clinical requirements for real-time monitoring (PDR > 95%, [Formula: see text][Formula: see text] ms, SAR [Formula: see text] W/kg, [Formula: see text] h), demonstrating its potential for next-generation, safe, and reliable healthcare applications.