A predictive ant colony optimization routing protocol for correlated link stability in wireless body area networks.
- 1Applied College, King Faisal University, Al-Ahsa, 31982, Saudi Arabia.
- 2Aden University, Aden, Yemen. [email protected].
Abstract
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.
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BibTeX
@article{soufiane2026predictive,
title = {{A predictive ant colony optimization routing protocol for correlated link stability in wireless body area networks.}},
author = {Ben Othman Soufiane and Naima Saeed},
journal = {BMC medical informatics and decision making},
year = {2026},
month = jul,
doi = {10.1186/s12911-026-03706-8},
url = {https://europepmc.org/article/MED/42894218},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A predictive ant colony optimization routing protocol for correlated link stability in wireless body area networks.
AU - Ben Othman Soufiane
AU - Naima Saeed
PY - 2026
DA - 2026/07/01
JO - BMC medical informatics and decision making
DO - 10.1186/s12911-026-03706-8
UR - https://europepmc.org/article/MED/42894218
AB - 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.
SN - 1472-6947
ER -