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In the era of the Internet of Things (IoT), blockchain is a promisingtechnology for improving the efficiency of healthcare systems, as it enablessecure storage, management, and sharing of real-time health data collected bythe IoT devices. As the implementations of blockchain-based healthcare systemsusually involve multiple conflicting metrics, it is essential to balance themaccording to the requirements of specific scenarios. In this paper, weformulate a joint optimization model with three metrics, namely latency,security, and computational cost, that are particularly important forIoT-enabled healthcare. However, it is computationally intractable to identifythe 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 itsroots in the classical Particle Swarm Optimization (PSO) algorithm, ourproposed ADPSA can effectively manage the numerous binary and integer variablesin the formulation. We demonstrate by extensive numerical experiments that theADPSA consistently outperforms existing benchmark approaches, including theoriginal PSO, exhaustive search and Simulated Annealing, in a wide range ofscenarios.

Modeling and Joint Optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems