Resource 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.

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

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.

The Ethics of Implementing Emergency Resource Allocation Protocols.

AbstractWe explore the various ethical challenges that arise during the practical implementation of an emergency resource allocation protocol. We argue that to implement an allocation plan in a crisis, a hospital system must complete five tasks: (1) formulate a set of general principles for allocation, (2) apply those principles to the disease at hand to create a concrete protocol, (3) collect the data required to apply the protocol, (4) construct a system to implement triage decisions with those data, and (5) create a system for managing the consequences of implementing the protocol, including the effects on those who must carry out the plan, the medical staff, and the general public. Here we illustrate the complexities of each task and provide tentative solutions, by describing the experiences of the Coronavirus Ethics Response Group, an interdisciplinary team formed to address the ethical issues in pandemic resource planning at the University of Rochester Medical Center. While the plan was never put into operation, the process of preparing for emergency implementation exposed ethical issues that require attention.

Enhancement in Quality-of-Services using 5G cellular network using resource reservation protocol

Mobile verbal exchange is viewed as one of the essential problem in the area of cellular networks. A mobile community apparatuses the proper public model to contribute packet of the data services on excessive speed. The packets of data service model are mandatory to improve the quality of service (QoS). This lookup exertion offers the Improvement of QoS and radio get right of entry to 5G Mobile Networks. Towards improve built-in services, a useful resource allocation protocol is applied at “Radio Access Network (RAN)” layer to improve the QoS in 5G. QoS labeling is a form of organizational communication that enables a local hub or exit station to communicate with or signal its neighbors to request special adjusting to of favorable traffic. QoS highlighting is helpful to provide ways for site visitors to adjust. ISP perform well and show full potential of the pervasive network by utilizing the simulation model in 5G’s ultra-low latency, real-time connectivity, significantly increased capacity, and quick speed. This will help to make device connections faster, more efficient, and less prone to delays. Lower, medium, and high frequency bands can be used to categories 5G. Mid-bandwidth enables better cell broadband connections or greater machine-to-machine communication and provides faster connectivity than 4G at close range.

Adding a Correction Factor to the Allocation of Scarce Life-saving Resources in a Pandemic

Adding a Correction Factor to the Allocation of Scarce Life-saving Resources in a Pandemic

Protecting our Elders and Developing Equitable Policies During Public Health Emergencies: Ethical Recommendations.

Purpose of the ReviewExperiences of patients, families, healthcare workers and health systems during the COVID-19 pandemic and recent national focus on racial justice have forced a reconsideration of policies and processes of providing care in crisis situations when resources are scarce. The purpose of this review is to present recent developments in conceptualizing ethical crisis standards.Recent findingsSeveral recent papers have raised concerns that “objective” scarce resource allocation protocols will serve to exacerbate underlying social inequities. Older adults and their formal and informal caregivers suffered from intersecting planning failures including lack of adequate stockpiling of personal protective equipment, failure to protect essential workers, neglect of long-term care facilities and homecare in disaster planning and de-prioiritization in triage algorithms.SummaryRevision of disaster planning guidelines is urgent. The time is now to apply lessons learned from COVID-19 before another disaster occurs. We present several suggestions for future plans.

An energy-efficient centralized dynamic time scheduling for internet of healthcare things

Due to the limited energy source in the sensor nodes, the conservation of energy in these nodes is of utmost importance for Internet of Things (IoT) systems used in monitoring patients. The significant factors that cause power consumption in an Internet of Healthcare Things (IoHT) network are network interference, resource allocation, and time spent idly by body sensors. Moreover, complex encoding, decoding, and encryption processes could increase the processing time in both Media Access Control (MAC) and Physical (PHY) layers. Hence, a novel MAC resource allocation protocol called Centralized Dynamic Time Scheduling (CDTS) is designed to overcome these issues. The CDTS system helps to avoid congestion and network traffic in the IoHT network. The main objective of this algorithm is to reduce the active time of each sensor in the network. We also reduced the total bit error rate in the network by introducing a dynamic bit interleaving method with a feedback loop. It is known that In the PHY layer, encoding and decoding are implemented with the help of a passive modulation scheme to reduce the delay in the network. In the encryption process, the Elliptic curve cryptography(ECC) method is used, which reduces the number of iterations required for encrypting the data. This proposed approach is compared with a fuzzy-based technique to measure efficiency. It is observed that our proposed technique reduced power consumption by up to 20% and is suitable for real-time healthcare applications.

Variation in COVID-19 Resource Allocation Protocols and Potential Implementation in the Chicago Metropolitan Area

Background Scarce resource allocation policies vary across the United States. Little is known about regional variation in resource allocation protocols and variation in their application. We sought to evaluate how Covid-19 scarce resource allocation policies vary throughout the Chicago metropolitan area and whether there are differences in policy application within hospitals when prioritizing hypothetical patients who need critical care resources. Methods Two cross-sectional surveys were distributed to Chicago metropolitan area hospital representatives and triage officers. Survey responses and categorical variables are described by frequency of occurrence. Intra- and interhospital variation in ranking of hypothetical patients was assessed using Fleiss’s Kappa coefficients. Results Eight Chicago-area hospitals responded to the survey assessing scarce resource allocation protocols (N = 8/18, response rate 44%). For hospitals willing to describe their ventilator allocation protocol (N = 7), most used the sequential organ failure assessment (SOFA) score (N = 6/7, 86%) and medical comorbidities (N = 4/7, 57%) for initial scoring of patients. A majority gave priority in initial scoring to pre-defined groups (N = 5/7, 71%), all discussed withdrawal of mechanical ventilation for adult patients (N = 7/7, 100%), and a minority had exclusion criteria (N = 3/7, 43%). Forty-nine triage officers from nine hospitals responded to the second survey (N = 9/10 hospitals, response rate 90%). Their rankings of hypothetical patients showed only slight agreement amongst all hospitals (Kappa 0.158) and fair agreement within two hospitals with the most respondents (Kappa 0.21 and 0.25). Almost half used tiebreakers to rank patients (N = 23/49, 47%). Conclusions Although most respondents from Chicago-area hospitals described policies for resource allocation during the COVID-19 pandemic, the substance and application of these protocols varied. There was little agreement when prioritizing hypothetical patients to receive scarce resources, even among people from the same hospital. Variations in resource allocation protocols and their application could lead to inequitable distribution of resources, further exacerbating community distrust and disparities in health. Supplemental data for this article is available online at https://doi.org/10.1080/23294515.2021.1983667

Should healthcare workers be prioritised during the COVID-19 pandemic? A view from Madrid and New York

While COVID-19 has generated a massive burden of illness worldwide, healthcare workers (HCWs) have been disproportionately exposed to SARS-CoV-2 coronavirus infection. During the so-called ‘first wave’, infection rates among this...

A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment

An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.

Energy Efficient Congestion Aware Resource Allocation and Routing Protocol for IoT Network using Hybrid Optimization Techniques

The idea of Smart City incorporates a few ideas being technology, economy, governance, people, management, and infrastructure. This implies a Smart City can have distinctive communication needs. Wireless technologies, for example, WiFi, Zig Bee, Bluetooth, WiMax, 4G or LTE have introduced themselves as a solution for the communication in Smart City activities. Nonetheless, as the majority of them utilize unlicensed interference, coexistence and bands issues are increasing. So to solve the problem IoT is used in smart cities. This paper addresses the issues of both resource allocation and routing to propose an energy efficient, congestion aware resource allocation and routing protocol (ECRR) for IoT network based on hybrid optimization techniques. The first contribution of proposed ECRR technique is to employ the data clustering and metaheuristic algorithm for allocate the large-scale devices and gateways of IoT to reduce the total congestion between them. The second contribution is to propose a queue based swarm optimization algorithm for select a better route for future route based on multiple constraints, which improves the route discovering mechanism. The proposed ECRR technique is implemented in Network Simulator (NS-2) tool and the simulation results are compared with the existing state-of-art techniques in terms of energy consumption, node lifetime, throughput, end-to-end delay, packet delivery ratio and packet overheads.

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective $5^{th}$ generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The $3^{rd}$ generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

Allocating Resources Across the Life Span During COVID-19—Integrating Neonates and Children Into Crisis Standards of Care Protocols

This Viewpoint discusses the need to account for neonates and children—who are typically disproportionally impacted during pandemics—by implementing hospital resource allocation protocols that ensure equity across the life span.

Neurologists' Duties in Planning for Triage of Critical Care Resources during the COVID‐19 Pandemic

Nothing to report.

AAN position statement: The COVID-19 pandemic and the ethical duties of the neurologist.

Patients, clinicians, and hospitals have undergone monumental changes during the coronavirus disease 2019 (COVID-19) pandemic. This crisis has forced us to consider the obligations that we neurologists have to our individual patients as well as the greater community. By returning to our fundamental understanding of these duties, we can ensure that we are providing the most ethically appropriate contingency and crisis care possible. We recommend specific adaptations to both the inpatient and outpatient settings, as well as changes to medical and trainee education. Furthermore, we explore the daunting but potentially necessary implementation of scare resource allocation protocols. As the pandemic evolves, we will need to adapt continuously to these rapidly changing circumstances and consider both national and regional standards and variation.

D2D Enabled Cellular Network Spectrum Allocation Scheme Based on the Cooperative Bargaining Solution

In 5G cellular networks, device-to-device (D2D) communication has undoubtedly become a general trend to increase spectral efficiency while reducing communication delay. In particular, D2D technology is able to bear more and more services, which help user-centred and personalized services in future wireless networks. However, several technical issues and challenges are associated with the deployment of D2D communications. In this paper, we tackle the spectrum allocation problem of D2D enabled cellular networks. By employing the major ideas of cooperative game theory, we design a novel two-stage resource allocation protocol based on the weighted utilitarian and meta bargaining solutions. The purpose of bargaining solutions is to clarify what could be the best solution when game players share a surplus that they can jointly generate. According to the main advantages of step-by-step interactive bargaining mechanism, our proposed solution takes various benefits in a rational way. Some simulation results and numerical analysis are provided to confirm the effectiveness of our two-stage bargaining approach and validate the accuracy of the proposed spectrum allocation scheme. Finally, we address some challenges and identify research areas for the future study.

We need to target trauma: A prospective observational study in Eastern Cape Province, South Africa.

Emergency medicine accounts for a large proportion of medical care in many low- and middle-income countries. A better understanding of the burden of disease will guide training and resource allocation priorities, but lack of electronic medical records and standardised data collection systems makes it difficult to obtain this information. To draw attention to the proportionally large burden of trauma in emergency centres (ECs) throughout Eastern Cape Province, South Africa (SA), in the hope of influencing resource allocation and medical provider training protocols accordingly. A secondary data analysis was performed from information gathered in HIV testing studies in two large tertiary care centres and one regional hospital in the Eastern Cape region of SA. All patients presenting to the ECs during the 6-week study period who met the inclusion criteria were approached and requested to provide consent for point-of-care HIV testing and collection of demographic information. Information collected included patient demographics, presenting complaints and final diagnoses. Simple descriptive statistics were used to analyse the data. Data were collected from 4 271 patients across three study sites: Frere Hospital (n=2391), Nelson Mandela Academic Hospital (n=622) and Mthatha Regional Hospital (n=1258). At the two tertiary care centres, most patients were between the ages of 18 and 30 years (41.2% and 32.6%, respectively) and male (57.8% and 60.2%), and 70.4% and 41.5% had traumatic injuries. The most common complaints were stab/gunshot wounds (18.3% and 20.2%). At the district hospital, the majority of patients were female (57.2%), 40.1% were between 18 and 30 years old, and 27.3% presented with traumatic injuries. Stab/gunshot wounds were the second most common complaint (7.2%) after lower respiratory tract infections (8.7%). From the proportion of presenting individuals sampled, we can conclude that a large proportion of care delivered in ECs in the Eastern Cape is for trauma. Local clinical capacitation efforts must focus on trauma training.

Utility-Aware Optimal Resource Allocation Protocol for UAV-Assisted Small Cells With Heterogeneous Coverage Demands

In this paper, we consider a UAV-assisted small-cell having heterogeneous users with different data rate and coverage demands. Specifically, we propose a novel utility-aware resource-allocation protocol to maximize the utility of UAV by allowing it to simultaneously serve the highest possible number of heterogeneous users with available energy resources. In this regard, first we derive a closed-form expression for rate-coverage probability of a user considering Rician fading to incorporate the strong line of sight (LoS) component in UAV communication. Next since this UAV utility maximization problem is non-convex and combinatorial, to obtain the global optimal resource allocation policy we propose an iterative feasibility checking method for fixed integers ranging from lower to upper bound on the number of users that can be served by UAV. To further reduce the complexity, we formulate an equivalent problem aimed at minimizing per user energy consumption, where tight analytical relaxation on rate-coverage probability constraint is used along with semi-closed expressions for joint-optimal power and time allocation. Lastly, via detailed numerical investigation, we validate our analytical claims, present insights on the impact of key system parameters, and demonstrate that 60% more users can be served using the proposed scheme as compared to relevant benchmarks.