  In the era of the Internet of Things (IoT), blockchain is a promising
technology for improving the efficiency of healthcare systems, as it enables
secure storage, management, and sharing of real-time health data collected by
the IoT devices. As the implementations of blockchain-based healthcare systems
usually involve multiple conflicting metrics, it is essential to balance them
according to the requirements of specific scenarios. In this paper, we
formulate a joint optimization model with three metrics, namely latency,
security, and computational cost, that are particularly important for
IoT-enabled healthcare. However, it is computationally intractable to identify
the exact optimal solution of this problem for practical sized systems. Thus,
we propose an algorithm called the Adaptive Discrete Particle Swarm Algorithm
(ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its
roots in the classical Particle Swarm Optimization (PSO) algorithm, our
proposed ADPSA can effectively manage the numerous binary and integer variables
in the formulation. We demonstrate by extensive numerical experiments that the
ADPSA consistently outperforms existing benchmark approaches, including the
original PSO, exhaustive search and Simulated Annealing, in a wide range of
scenarios.
