Allocation Protocol Research Articles

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

The use of digital twin for mobile robot swarm task allocation

Cyber-physical systems, with the advent of the Internet of Things (IoT), have paved the way for the rise of the Industrial Internet of Things (IIoT) in industrial environments. A significant manifestation of this synergy is the Digital Twin—a virtual representation of industrial entities, including robots. This paper examines the fusion of digital twins in swarm robotics, emphasizing communication, and task allocation among robots. This study employs several mobile robots interconnected via a wireless network, each equipped with a Raspberry Pi and specific sensors. A centralized server, acting as both a Docker host and a Docker registry, facilitates task allocation. The results indicate effective communication among robots, with the digital twin playing an instrumental role in safety assessment and task allocation, as well as deploying Docker containers using Portainer and GitLab APIs. Unity3D aids in visualizing IMU motion and orientation. The motivation behind this study stems from the need to enhance efficiency and safety in industrial settings through advanced automation and coordination in swarm robotics. This research not only demonstrates the feasibility of integrating digital twins with IoT-enabled robots but also sets the stage for future investigations into more complex task allocation and safety protocols in industrial environments. By leveraging digital twins in swarm robotics, we open new pathways for research into more adaptive, responsive, and efficient industrial systems.

Clinical opinion spotlight: A sustainable model for improving access to mobile mammography for underserved populations

Minority women face disproportionately higher risks of breast cancer diagnosis and mortality under 50. Mobile mammography vans enhance screening accessibility but face challenges like long-term funding and operational viability. This study assesses existing mobile mammography van programs and proposes a sustainable model through literature review and qualitative interviews at a tertiary care academic medical center. The proposed model emphasizes partnerships, sponsorships and long-term funding for ensuring workforce sustainability. Organizational structure, budget allocation, patient workflow and follow-up protocols are aimed at transitioning towards serving primarily uninsured patients while maintaining financial stability. This sustainable approach hopes to enhance screening rates and timely care for underserved women, suggesting scalability and potential to reduce late-stage diagnoses and mortality. Continued implementation and evaluation are essential for validating feasibility and effectiveness, ensuring long-term improvement in screening rates and program longevity.

Joint Traffic Admission, Resource Allocation and Mode Selection Protocol for NOMA-Based D2D Users Underlaying Cellular Network for 5G and Beyond Networks

Joint Traffic Admission, Resource Allocation and Mode Selection Protocol for NOMA-Based D2D Users Underlaying Cellular Network for 5G and Beyond Networks

Transplantation, bridging, and support technologies in pulmonary hypertension.

Despite the progress made in medical therapies for treating pulmonary hypertension (PH), a subset of patients remain susceptible to developing a maladaptive right ventricular phenotype. The effective management of end-stage PH presents substantial challenges, necessitating a multidisciplinary approach and early identification of patients prone to acute decompensation. Identifying potential transplant candidates and assessing the feasibility of such a procedure are pivotal tasks that should be undertaken early in the treatment algorithm. Inclusion on the transplant list is contingent upon a comprehensive risk assessment, also considering the specific type of PH and various factors affecting waiting times, all of which should inform the decision-making process. While bilateral lung transplantation is the preferred option, it demands expert intra- and post-operative management to mitigate the heightened risks of pulmonary oedema and primary graft dysfunction in PH patients.Despite the availability of risk assessment tools, the occurrence of acute PH decompensation episodes can be unpredictable, potentially leading to refractory right ventricular failure even with optimal medical intervention, necessitating the use of rescue therapies. Advancements in right ventricular assist techniques and adjustments to graft allocation protocols for the most critically ill patients have significantly enhanced the survival in intensive care, affording the opportunity to endure while awaiting an urgent transplant. Given the breadth of therapeutic options available, specialised centres capable of delivering comprehensive care have become indispensable for optimising patient outcomes. These centres are instrumental in providing holistic support and management tailored to the complex needs of PH patients, ultimately enhancing their chances of a successful transplant and improved long-term prognosis.

A novel dynamic channel allocation protocol based on data traffic characterization model in CR-IoT network

A novel dynamic channel allocation protocol based on data traffic characterization model in CR-IoT network

Case Article—Three Mountain Communications: Fairness Considerations for Workplace Task Allocation

The case study has two primary objectives: incorporating fairness considerations in managerial decision making and introducing task allocation using an optimization model. The case focuses on a call center that faces the issue of daily task allocation. The current self-selection-based allocation protocol has created friction among employees. To enable employee satisfaction and boost team morale, the call center needs to develop an allocation model that is perceived to be fair by employees. The case tends to elicit enthusiastic participation from students, especially on the theme of workplace fairness and the role of gender and individual constraints on employees’ ability to excel at the workplace. Overall, students believed the case was challenging and that it provided them skills to combine (i) optimization modeling and (ii) qualitative considerations for workplace fairness that they studied in management and leadership courses. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ited.2023.0044ca .

Thoracic and vertebral deformities in lung transplantation: perioperative complications and long-term prognoses

BackgroundLung transplantation (LTx) is a crucial therapeutic strategy for patients suffering from end-stage respiratory diseases, necessitating precise donor-recipient size matching to ensure optimal graft function. While standard allocation protocols rely on predicted lung capacity based on factors such as sex, age, and height, a subset of patients with respiratory diseases presents an additional challenge – thoracic or vertebral deformities. These deformities can complicate accurate volume predictions and may impact the success of lung transplantation.MethodsIn this retrospective cohort study of patients who underwent LTx at Tohoku University Hospital between January 2007 and April 2022, with follow-up until October 2022, the primary objective was to assess the influence of thoracic and vertebral deformities on perioperative complications, emphasizing interventions, such as volume reduction surgery. The secondary objective aimed to identify any noticeable impact on long-term prognoses in recipients with these deformities.ResultsOf 129 LTx recipients analyzed, 17.8% exhibited thoracic deformities, characterized by pectus excavatum, while 16.3% had vertebral deformities. Perioperative complications, requiring delayed chest closure, tracheostomy, and volume reduction surgery, were more prevalent in the deformity group. Thoracic deformities were notably associated with the need for volume reduction surgery. However, long-term prognoses did not differ significantly between patients with deformities and those without. Vertebral deformities did not appear to significantly impact perioperative or long-term outcomes.ConclusionsThis study highlights the prevalence of thoracic deformities in LTx recipients, correlating with increased perioperative complications, particularly the potential need for volume reduction surgery. Importantly, these deformities do not exert a significant impact on long-term prognoses. Additionally, patients with vertebral deformities, such as scoliosis and kyphosis, appear to be manageable in the context of LTx.

Ethical prioritization of critical care resources during COVID-19: perspectives from Italy and the United States.

This article examines some of the ethical challenges of prioritizing intensive care resources during the Covid-19 pandemic by comparing the Italian and United States contexts. After presenting an overview to the clinical, ethical, and public debates in Italy, the article will discuss the development of triage allocation protocols in United States hospitals. Resource allocation criteria underwent increased scrutiny and critique in both countries, which resulted in modified professional and expert guidance regarding healthcare ethics during times of emergency and resource scarcity.

A State-Interactive MAC Layer TDMA Protocol Based on Smart Antennas

Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay.

Coding–Decoding-Based Sliding Mode Control for Markovian Jump Systems Under Constrained Bit Rate: An Adaptive Quantizer Approach

This paper investigates the asynchronous sliding mode control (SMC) issue for uncertain hidden Markovian jump systems (MJSs) under constrained bit rate. An improved coding-decoding procedure with state-dependent adaptive quantization parameters is established to enable the controller to decode state and mode information from the system and the detector, respectively. Based on this procedure, a decoded-data-based asynchronous SMC strategy is provided and sufficient conditions are developed to ensure that the closed-loop dynamics are exponentially ultimately bounded and the system state is driven onto a sliding region simultaneously in the sense of mean square. The desired parameters of the quantizer and controller are obtained by solving an optimization problem, in which the objective function is constructed by using the trade-off between the ultimate state bound and the decay rate. Moreover, the integrative design of bit rate allocation protocol and quantizer as well as control parameters are converted into a nonlinear programming problem with integer constraint, which is solved by means of an improved sparrow search algorithm. Finally, a direct current motor system is employed to illustrate the effectiveness of the present approach under three different cases.

Dual Secret Key Virtualization Through TCC Based Wavelength Allocation and BB84 Protocol in QKD over Optical Networks

Dual Secret Key Virtualization Through TCC Based Wavelength Allocation and BB84 Protocol in QKD over Optical Networks

AFHO‐based link reliable routing and power‐assisted channel allocation protocol in cognitive radio networks for healthcare applications

AbstractIn the realm of cognitive networks, accommodating the increasing need for radio spectrum to facilitate both secondary and primary users stands as an intricate and demanding task. This study introduces a tailored framework for healthcare applications in Cognitive Radio (CR) networks for link reliable routing and power‐assisted channel allocation. Through the integration of Adaptive Fire Hawk Optimization for routing, the strategy addresses optimization complexities while prioritizing robust link quality via optimal hop selection for effective communication. A new routing optimization challenge is formulated, aiming to establish reliable communication pathways and extend link reliability. Moreover, the framework incorporates power‐assisted channel allocation to strategically enhance resource management and overall network performance. The rigorous evaluation demonstrates significant enhancements across key performance metrics, which showcase percentage improvements of 9.86%, 14.56%, 3.85%, 5.06%, 6.94%, and 17.80% for active nodes, delay, Packet Delivery Ratio (PDR), residual energy, and throughput, respectively. These advancements underscore the proposed approach's efficacy in enhancing CR network performance, particularly for healthcare applications.

Investigating ethical tradeoffs in crisis standards of care through simulation of ventilator allocation protocols.

Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic. A Monte Carlo simulation was used to estimate the number of lives saved and life-years saved by implementing clinical acuity-, comorbidity- and age-based CSC protocols under different shortage conditions. This model was populated with patient data from 3707 adult admissions requiring ventilator support in a New York hospital system between April 2020 and May 2021. To estimate lives and life-years saved by each protocol, we determined survival to discharge and estimated remaining life expectancy for each admission. The simulation demonstrated stronger performance for age-sensitive protocols. For a capacity of 1 bed per 2 patients, ranking by age bands saves approximately 29 lives and 3400 life-years per thousand patients. Proposed protocols from New York and Maryland which allocated without considering age saved the fewest lives (~13.2 and 8.5 lives) and life-years (~416 and 420 years). Unlike other protocols, the New York and Maryland algorithms did not generate significant disparities in lives saved and life-years saved between White non-Hispanic, Black non-Hispanic, and Hispanic sub-populations. For all protocols, we observed a positive correlation between lives saved and life-years saved, but also between lives saved overall and inequality in the number of lives saved in different race and ethnicity sub-populations. While there is significant variance in the number of lives saved and life-years saved, we did not find a tradeoff between saving the most lives and saving the most life-years. Moreover, concerns about racial discrimination in triage protocols require thinking carefully about the tradeoff between enforcing equality of survival rates and maximizing the lives saved in each sub-population.

Hybrid User Grouping With Heterogeneous Devices in NOMA-Enabled IoT Networks

In this paper, we consider an uplink Internet of Things (IoT) network consisting of time-sensitive devices and throughput-sensitive devices, focusing on effective coordination between different performance requirements. To improve the spectral efficiency, Non-Orthogonal Multiple Access (NOMA) is introduced to obtain a potential performance gain by assigning the spectrum resources to different types of devices with diverse performance requirements in a coordinative and resource-sharing manner. Considering the performance diversity of heterogeneous devices, we provide two NOMA user grouping strategies: Mixed Block Strategy (MBS) and Uniform Block Strategy (UBS) which configure NOMA user groups as those consisting of different types of devices and those containing only a single type of device, respectively. Then, we propose a hybrid resource allocation protocol by switching between these two strategies. The performance of a special case with two time-sensitive devices and two throughput-sensitive devices is first optimized to evaluate the proposed strategies, and then extended to compare with traditional strategy in the multi-user scenarios. Simulation results illustrate that MBS and UBS outperform the traditional strategy, i.e., lower Age of Information (AoI) and higher throughput, and have their own advantages at different system parameters. On the basis, the hybrid protocol can further improve the system performance.

Research on active user detection and channel estimation algorithms in massive access

Massive Access is a supporting technology for scenarios like the Internet of Things, and is also one of the typical scenarios defined by 5G. Unlike traditional multi-user communication, the data of these nodes is generally sudden, so only a small proportion of nodes are active at a certain time. If the traditional request resource allocation protocol mode is adopted, it requires authorized access to achieve dynamic resource allocation, which will result in excessive protocol overhead. Therefore, Grant-Free transmission is required. It is impossible to recover the data transmitted by active users during Grant-Free transmission because the base station is unaware of who is transmitting the data. Therefore, the system must first do active user detection and channel estimation. A channel estimation technique based on Compressed Sensing is presented in this paper. the EM-BG-AMP algorithm is reproduced and applied. By comparing the accuracy of the EM-BG-AMP algorithm in channel estimation under physical backgrounds, this essay gives the range of optimal received signal-to-noise ratio:100dB and more. Also, this essay demonstrates that the performance of the EM-BG-AMP algorithm weakens as sparsity increases.

ProMETHEUS: A Secure Lightweight Spectrum Allocation Protocol against SSDF Attacks in Cognitive Radio IoT Networks

ProMETHEUS: A Secure Lightweight Spectrum Allocation Protocol against SSDF Attacks in Cognitive Radio IoT Networks

Ethical guidelines for the allocation of scarce intensive care units during the COVID-19 pandemic: Discussing a Brazilian proposal.

In the context of a major health crisis, health professionals must first compare patients' recovery prospects, thus giving priority to the goal of saving the greatest number of lives. Critically evaluate a protocol for allocation of scarce intensive care units (ICU), which the authors proposed at the onset of the COVID-19 pandemic and originally published in two Brazilian newspapers; and compare that protocol with similar proposals, particularly with 2 successive protocols issued by the Brazilian Critical Care Association. The main objective is to highlight the advantages of the authors' approach and discuss some criticisms that has been levelled against the proposed protocol after its original publication in 2020. Comparative analysis of 3 different protocols (the authors' proposed protocol and 2 successive protocols issued by the Brazilian Critical Care Association) with regard to ethical principles. The main objective of a healthcare system is to ensure a fair patient triage process when it is impossible to grant admission to all patients in need of treatment. Decision-making regarding the impartial prioritization of ICU admissions must be based primarily on clinical criteria. The Sequential Organ Failure Assessment (SOFA) is, for ethical and technical reasons, a useful tool for clinical assessments of patients. Based on three ranges of SOFA scores, patients can be classified into a "high", a "medium", and a "low" priority group. In the case of ties, the life cycle principle must be the tiebreaker. If the tie persists, a draw must be used. The authors' proposed protocol has advantages over the other two protocols due to its greater practicality and capacity to account for egalitarian and consequentialist principles simultaneously. It aims at saving as many lives as possible within the constraints of fairness. Furthermore, the proposed protocol avoids discrimination against people with disabilities without, at the same time, promoting discrimination against the elderly.

A Transfer-Based Convolutional Neural Network Model with Multi-Signal Fusion and Hyperparameter Optimization for Pump Fault Diagnosis.

Pumps are one of the core components of drilling equipment, and their fault diagnosis is of great significance. The data-driven approach has made remarkable achievements in the field of pump fault diagnosis; however, most of them are easily affected by complex background conditions and usually suffer from data scarcity problems in real-industrial scenarios, which limit their application in practical engineering. To overcome the above shortcoming, a novel framework for a model named Hyperparameter Optimization Multiple-Signal Fusion Transfer Convolution Neural Network is proposed in this paper. A convolutional neural network model based on transfer learning is built to promote well-learned knowledge transfer over different background conditions, improve robustness, and generalize the model to cross-domain diagnosis tasks. The multi-signal fusion strategy is involved in capturing system state information for establishing the mapping relationship between the raw signal and fault pattern by integrating the multi-physical signal with the weight allocation protocol. The hyperparameter optimization method is explored in conjunction with the transfer-based model by integrating Grid Search with the Gradient Descent algorithm for further improvement of diagnosis performance. Results show that the proposed model can effectively realize the fault diagnosis of pumps under different background conditions, achieving 95% accuracy.

The pain lottery.

Moral challenges with addiction and overdosing have resulted from the abundance of opioids, but the coronavirus disease of 2019 has prompted reflection on ethical issues that could arise from a shortage. Driven by a duty to plan, some jurisdictions have formed committees to see if standard allocation considerations extend to cover a shortage of opioid pain medication. The problem, we argue, is that the standard allocation protocols do not apply to a shortage of opioids because prognosis only has limited relevance and the moral disvalue of pain is not dependent upon a patient's status as a frontline worker, age, or residence in a disadvantaged community. While the use of lotteries in allocation schemes has been deemphasized in standard allocation schema, we argue for and outline the details of a tiered lottery that first prioritizes opioids needed for emergent procedures and then moves on to allocate opioids based on the severity of a patient's pain. Additionally, we argue that some deception, in the form of withholding information from patients about the implementation and details of a pain lottery, is ethically permissible to address the unique moral tension between transparency and beneficence that arises for the treatment of pain in conditions of opioid scarcity.