Wireless Body Area Networks (WBANs) provide a platform for continuous health monitoring through networks of wearable or implantable biosensors. Practical deployment in healthcare settings faces two main challenges: limiting energy consumption due to the limited battery capacity of sensors and handling traffic uncertainty caused by variable, often unpredictable biosensor data rates. This study addresses both issues through a network optimization framework for WBAN topology design that explicitly accounts for traffic uncertainty. This work introduces the first mathematical programming formulation that jointly optimizes relay placement and single-path routing under uncertain traffic, with the system represented as a binary linear program. To manage computational complexity, a novel metaheuristic algorithm, RuPDBAN, is proposed. The algorithm combines linear relaxations, randomized variable fixing, and an advanced neighborhood search inspired by genetic algorithms. Computational experiments on 30 realistic WBAN scenarios show that RuPDBAN consistently outperforms the state-of-the-art CPLEX solver. The method attains solutions with up to 25% smaller optimality gaps, lowers average energy consumption per bit, and reduces computation time by more than 90% in most cases. Qualitative analysis further indicates that the proposed hybrid design model reduces communication hops and packet delay compared with single- or multi-path protocols. These findings confirm the effectiveness of the proposed approach for energy-efficient and reliable WBAN design under real-world uncertainty, with direct relevance to medical monitoring applications.
Zhu et al. (Thu,) studied this question.