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

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

Asymptotically Optimal Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover

We study energy-efficient offloading strategies in a large-scale MEC system with heterogeneous mobile users and network components. The system is considered with enabled user-task handovers that capture the mobility of various mobile users. We focus on a long-run objective and online algorithms that are applicable to realistic systems. The problem is significantly complicated by the large problem size, the heterogeneity of user tasks and network components, and the mobility of the users, for which conventional optimizers cannot reach optimum with a reasonable amount of computational and storage power. We formulate the problem in the vein of the restless multi-armed bandit process that enables the decomposition of high-dimensional state spaces and then achieves near-optimal algorithms applicable to realistically large problems in an online manner. Following the restless bandit technique, we propose two offloading policies by prioritizing the least marginal costs of selecting the corresponding computing and communication resources in the edge and cloud networks. This coincides with selecting the resources with the highest energy efficiency. Both policies are scalable to the offloading problem with a great potential to achieve proved asymptotic optimality - approach optimality as the problem size tends to infinity. With extensive numerical simulations, the proposed policies are demonstrated to clearly outperform baseline policies with respect to power conservation and robust to the tested heavy-tailed lifespan distributions of the offloaded tasks.Comment: 15 pages, 22 figure

A Study of a Loss System with Priorities

The Erlang loss formula, also known as the Erlang B formula, has been known for over a century and has been used in a wide range of applications, from telephony to hospital intensive care unit management. It provides the blocking probability of arriving customers to a loss system involving a finite number of servers without a waiting room. Because of the need to introduce priorities in many services, an extension of the Erlang B formula to the case of a loss system with preemptive priority is valuable and essential. This paper analytically establishes the consistency between the global balance (steady state) equations for a loss system with preemptive priorities and a known result obtained using traffic loss arguments for the same problem. This paper, for the first time, derives this known result directly from the global balance equations based on the relevant multidimensional Markov chain. The paper also addresses the question of whether or not the well-known insensitivity property of the Erlang loss system is also applicable to the case of a loss system with preemptive priorities, provides explanations, and demonstrates through simulations that, except for the blocking probability of the highest priority customers, the blocking probabilities of the other customers are sensitive to the holding time distributions and that a higher variance of the service time distribution leads to a lower blocking probability of the lower priority traffic

Cost-Efficient Millimeter Wave Base Station Deployment in Manhattan-Type Geometry

This work is licensed under a Creative Commons Attribution 4.0 International License.Urban millimeter wave (mmWave) communications are limited by link outage due to frequent blockages by obstacles. One approach to this problem is to increase the density of base stations (BSs) to achieve macro diversity gains. Dense BS deployment, however, incurs the increased BS installation cost as well as power consumption. In this work, we propose a framework for connectivity-constrained minimum cost mmWave BS deployment in Manhattan-type geometry (MTG). A closed-form expression of network connectivity is characterized as a function of various factors such as obstacle sizes, BS transmit power, and the densities of obstacles and BSs. Optimization that attains the minimum cost is made possible by incorporating a tight lower bound of the analyzed connectivity expression. A low-complexity algorithm is devised to effectively find an optimal tradeoff between the BS density and transmit power that results in the minimum BS deployment cost while guaranteeing network connectivity. Numerical simulations corroborate our analysis and quantify the best tradeoff of the BS density and transmit power. The proposed BS deployment strategies are evaluated in different network cost configurations, providing useful insights in mmWave network planning and dimensioning

DiGAN breakthrough: advancing diabetic data analysis with innovative GAN-based imbalance correction techniques

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics

Kinetics and Mechanism Study of Competitive Inhibition of Jack-Bean Urease by Baicalin

Baicalin (BA) is the principal component of Radix Scutellariae responsible for its pharmacological activity. In this study, kinetics and mechanism of inhibition by BA against jack-bean urease were investigated for its therapeutic potential. It was revealed that the IC50 of BA against jack-bean urease was 2.74 ± 0.51 mM, which was proved to be a competitive and concentration-dependent inhibition with slow-binding progress curves. The rapid formation of initial BA-urease complex with an inhibition constant of Ki=3.89 × 10−3 mM was followed by a slow isomerization into the final complex with an overall inhibition constant of Ki*=1.47×10-4 mM. High effectiveness of thiol protectors against BA inhibition indicated that the strategic role of the active-site sulfhydryl group of the urease was involved in the blocking process. Moreover, the inhibition of BA was proved to be reversible due to the fact that urease could be reactivated by dithiothreitol but not reactant dilution. Molecular docking assay suggested that BA made contacts with the important activating sulfhydryl group Cys-592 residues and restricted the mobility of the active-site flap. Taken together, it could be deduced that BA was a competitive inhibitor targeting thiol groups of urease in a slow-binding manner both reversibly and concentration-dependently, serving as a promising urease inhibitor for treatments on urease-related diseases

Association Between Adherence Measurements of Metoprolol and Health Care Utilization in Older Patients with Heart Failure*

Data from electronic dosing monitors and published pharmacokinetic parameters were used to derive medication adherence measures for immediate-release metoprolol and examine their association with health care utilization of outpatients aged 50 years or older with heart failure