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

TL;DR

This paper presents a joint optimization model for balancing security, latency, and computational cost in blockchain-based healthcare systems, proposing an efficient algorithm to find near-optimal solutions amidst conflicting metrics.

Contribution

It introduces a novel joint optimization framework and the Adaptive Discrete Particle Swarm Algorithm (ADPSA) for efficient solution finding in complex blockchain healthcare scenarios.

Findings

ADPSA outperforms benchmark algorithms in numerical experiments.

The model effectively balances conflicting metrics in IoT-enabled healthcare.

Extensive tests show ADPSA's robustness and efficiency.

Abstract

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.