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Researching quality in care transitions : international perspectives
\"This book is concerned with the complexities of achieving quality in care transitions. The organization and accomplishment of high quality care transitions relies upon the coordination of multiple professionals, working within and across multiple care processes, settings and organizations, each with their own distinct ways of working, profile of resources, and modes of organizing. In short, care transitions might easily be regarded as complex activities that take place within complex systems, which can make accomplishing high quality care challenging. As a subject of enquiry, care transitions are approached from many research, improvement and policy perspectives: from group psychology and human factors to social and political theory; from applied process re-engineering projects to exploratory ethnographic studies; from large-scale policy innovations to local improvements initiatives. This collection will provide a unique cross-disciplinary and multi-level analysis, where each chapter presents a particular depth of insight and analysis, and together offer a holistic and detail understand of care transitions.\"-- Provided by publisher.
Book
Joint call admission control and load-balancing in ultra-dense cellular networks: a proactive approach
Cellular networks adopt call admission control (CAC) algorithms to prevent network congestion and guarantee quality of service (QoS) for user equipments (UEs). Conventional CAC algorithms accept or reject incoming calls based on radio resource availability. Those rejected calls may lead to network performance degradation. Collaboration of CAC and load-balancing can overcome such a problem. However, conventional joint schemes are reactive and proven time consuming; hence, inefficient in real-world scenarios. To overcome such problems as well as to improve network performance, we propose a proactive approach for joint call admission control and load balancing. The algorithm mitigates the number of rejected calls by performing proactive offloading. To that end, the algorithm identifies potential incoming UEs in a cell prior to handover. If a cell is fully occupied and an incoming UE is detected, the proposed algorithm hands over some of the edge UEs from that particular cell to its neighboring cells. As a result, the cell has enough resources to accept the incoming call. Simulation results show that the proposed algorithm reduces the number of unsatisfied UEs and maximizes network throughput by 11.04%, compared to a network without a CAC algorithm.
Journal Article
Three types of calls admission control methods considering seek‐bar operation
Many network devices, such as a smartphones and tablets, and streaming services, such as YouTube and Netflix, have become widespread. This causes network‐bandwidth congestion, so users require stable and consistent communication quality. Call admission control (CAC) has been proposed to solve these bandwidth resource problems. Some CACs have been proposed in order to maintain QoS. However, these CAC does not assume user behaviour. If CAC assumes some user behaviour, optimal control parameter derived conventional CAC may change because traffic load change by considering user behaviour. This article assumes that arriving types of flows are of three types: narrowband flows, broadband flow without seek‐bar operation, and broadband flows with seek‐bar operation. Under this assumpution, two CAC methods with single threshold or two thresholds are proposed. With these methods, it is assumed that all arriving flows result in the same user satisfaction when accommodated in a network. Under this assumption, the maximum of each user satisfaction is the same as the maximum number of accommodated flows. Using the call loss probability characteristics of Method 1, the objective function in Method 2 is modified. By using the modified objective function, CAC can be realised while properly accommodating broadband users who have been excessively rejected. Many network devices, such as a smartphones and tablets, and streaming services, such as YouTube and Netflix, have become widespread. This causes network‐bandwidth congestion, so users require stable and consistent communication quality. Call admission control (CAC) has been proposed to solve these bandwidth resource problems. This paper focuses on selfish user behaviour, that is, using a seek‐bar to reduce video‐watching time, which increases the number of buffering operations. Two CAC methods are modelled by using queueing theory for guaranteeing communication quality. Moreover, the effectiveness of this CAC is shown.
Journal Article
Three-layer data center-based intelligent slice admission control algorithm for C-RAN using approximate reinforcement learning
C-RAN (Cloud Radio Access Network) is a 5G architecture that consists of sites and three-layer Data Centers (DCs), which include the central office DC, local DC, and regional DC. Network slicing, which enables infrastructure providers (InP) to create independent logical networks, is essential in this architecture. By utilizing this technology, InPs can maximize the utility of the network by providing slices to service providers in response to their slice requests. However, almost all of the recent research on slice admission control (SAC) schemes has only considered one or two layers of DCs, which limits the efficiency of the slicing process and decreases network utility. To address these issues, this paper proposes an intelligent SAC scheme called ISAC that considers all three-layer DCs. Instead of relying on reinforcement learning algorithms like Q-learning, which are effective in discrete environments with limited state space but give poor performance in continuous environments, ISAC employs the Approximate Reinforcement Learning (ARL) algorithm. ARL is better suited for 5G network modeling because it can adapt to continuous environments, allowing for a more accurate representation of the underlying physical processes. Extensive simulation studies demonstrate that ISAC significantly improves performance in terms of slice request rejection rates, InP revenue, accepting more slices, and optimizing resource utilization.
Journal Article
Comparative analysis of call admission control techniques for efficient resource utilization and QoS in IEEE 802.16e network
In order to provide solution to limited network resources in heterogeneous wireless networks supporting different applications with distinct quality of service (QoS) requirements, call admission control (CAC) schemes are implemented. This work is aimed at investigating three mostpopular CAC schemes employedin mobile worldwide interoperability for microwave access (WiMAX), namely dynamic CAC with bandwidth reservation (DCACBR), QoS-aware CAC (QoSACAC), and QoS guaranteed CAC (QoSGCAC) to identify their shortfalls which will form the focus of future research. A general platform is developed and simulated. The simulation was based on the following KPIs: blocking rate, dropping rate, and throughput for new and handoff connections. Simulation results for new connection shows that QoSGCAC outperforms scheme DCACBR and QoSACAC, having 26.9% and 8.56% improvements in throughput and 63.11% and 24.17% in blocking rate respectively. For handoff connection, QoSACAC showed the best performance having 13.25% and 47.84% improvements in throughput and 6.8% and 49.3% in blocking rate as compared to the DCACBR and QoSGCAC, respectively. Result analysis shows that QoSACAC has the best performance however, it admits new connections and degrade existing connections but failed to consider the delay-intolerant service classes. It is recommended that the QoSACAC be further improved.
Journal Article
Admission control policy and key agreement based on anonymous identity in cloud computing
Cloud computing has completely revolutionized the concept of computing by providing users with always-accessible resources. In terms of computational, storage, bandwidth, and transmission costs, cloud technology offers its users an entirely new set of advantages and cost savings. Cross-cloud data migration, required whenever a user switches providers, is one of the most common issues the users encounter. Due to smartphones’ limited local storage and computational power, it is often difficult for users to back up all data from the original cloud servers to their mobile phones to upload and download the data to the new cloud provider. Additionally, the user must remember numerous tokens and passwords for different applications. In many instances, the anonymity of users who access any or all services provided by this architecture must be ensured. Outsourcing IT resources carries risks, particularly regarding security and privacy, because cloud service providers manage and control all data and resources stored in the cloud. However, cloud users would prefer that cloud service providers not know the services they employ or the frequency of their use. Consequently, developing privacy protections takes a lot of work. We devised a system of binding agreements and anonymous identities to address this problem. Based on a binding contract and admission control policy (ACP), the proposed model facilitates cross-cloud data migration by fostering cloud provider trust. Finally, Multi-Agent Reinforcement Learning Algorithm (MARL) is applied to identify and classify anonymity in the cloud by conducting various pre-processing techniques, feature selection, and dimensionality reduction.
Journal Article
Optimal Decision of Dynamic Bed Allocation and Patient Admission with Buffer Wards during an Epidemic
To effectively prevent patients from nosocomial cross-infection and secondary infections, buffer wards for screening infectious patients who cannot be detected due to the incubation period are established in public hospitals in addition to isolation wards and general wards. In this paper, we consider two control mechanisms for three types of wards and patients: one is the dynamic bed allocation to balance the resource utilization among isolation, buffer, and general wards; the other is to effectively control the admission of arriving patients according to the evolution process of the epidemic to reduce mortality for COVID-19, emergency, and elective patients. Taking the COVID-19 pandemic as an example, we first develop a mixed-integer programming (MIP) model to study the joint optimization problem for dynamic bed allocation and patient admission control. Then, we propose a biogeography-based optimization for dynamic bed and patient admission (BBO-DBPA) algorithm to obtain the optimal decision scheme. Furthermore, some numerical experiments are presented to discuss the optimal decision scheme and provide some sensitivity analysis. Finally, the performance of the proposed optimal policy is discussed in comparison with the other different benchmark policies. The results show that adopting the dynamic bed allocation and admission control policy could significantly reduce the total operating cost during an epidemic. The findings can give some decision support for hospital managers in avoiding nosocomial cross-infection, improving bed utilization, and overall patient survival during an epidemic.
Journal Article
Analysis of BMAP/PH/N-Type Queueing System with Flexible Retrials Admission Control
This research examines a multi-server retrial queueing system with a batch Markov arrival process and a phase-type service time distribution. The system’s distinguishing feature is its ability to control the admission of retrial customers. An attempt by a customer to retry is successful only if the number of busy servers does not exceed certain threshold values, which may depend on the state of the fundamental process of the primary customer’s arrival. Impatient retrying customers may abandon the system without obtaining service. A group of primary customers that arrives while the number of available servers is fewer than the group size is either entirely rejected or occupies all available servers, while the remainder of the group transitions to the orbit. The system’s behavior, under a defined set of thresholds, is characterized by a multidimensional Markov chain classified as asymptotically quasi-Toeplitz. This enables the acquisition of the ergodicity condition and the computation of the steady-state distribution of the Markov chain and the system’s performance measures. The presented numerical examples demonstrate the impact of threshold value variation. An example of solving an optimization problem is presented. The importance of the account of the batch arrivals is shown.
Journal Article
Performance Modeling of Call Admission Control Policy and Handover Management in 5G Ultra-dense Cellular Network
5G networks are expected to deliver very high data rates with enhanced quality of service (QoS) at a very low cost. To meet the demands of high data rates and improved QoS, ultra-densification is the key solution used in 5G networks. In this paper we propose a call admission control (CAC) policy by considering the main components of an ultra-dense 5G network, namely, macrocell, picocell and femtocell. The calls in the network are accepted based on the SNIR ranges which help in deciding whether the call will be connected to a macrocell or picocell or femtocell. We also come up with a handover management scheme which regulates the handover of calls between macrocell to macrocell (picocell or femtocell), picocell to macrocell (picocell or femtocell) and femtocell to macrocell (picocell or femtocell). The performance analysis of the proposed CAC policy and the handover management scheme is also conducted in the paper. For this, we construct analytical models with the help of continuous-time Markov chains for the macrocell, picocell and femtocell network to obtain the performance metrics such as blocking probability of new calls and dropping probability of handover calls. The analytical results are further investigated to establish the importance of ultra-densification in 5G network. The proposed analytical models are also validated via simulation. A comparative analysis is also performed between the proposed CAC policy and an existing CAC policy in literature which considers only macrocells and femtocells in the network.
Journal Article
Multi-Objective Optimization of Joint Power and Admission Control in Cognitive Radio Networks Using Enhanced Swarm Intelligence
The problem of joint power and admission control (JPAC) is a critical issue encountered in underlay cognitive radio networks (CRNs). Moving forward towards the realization of Fifth Generation (5G) and beyond, where optimization is envisioned to take place in multiple performance dimensions, it is crucially desirable to achieve high sum throughput with low power consumption. In this work, a multi-objective JPAC optimization problem that jointly maximizes the sum throughput and minimizes power consumption in underlay CRNs is formulated. An enhanced swarm intelligence algorithm has been developed by hybridizing two new enhanced Particle Swarm Optimization (PSO) variants, namely two-phase PSO (TPPSO) and diversity global position binary PSO (DGP-BPSO) variants employed to optimize the multi-objective JPAC problem. The performance of the enhanced swarm intelligence algorithm in terms of convergence speed and stability, while optimizing both the sum throughput and power consumption, is investigated under three different operational scenarios defined by their single objective priorities, which translate to sum throughput and power consumption preferences. Simulation results have proven the effectiveness of the enhanced swarm intelligence algorithm in achieving high sum throughput and low power consumption under the three operational scenarios when the network includes an arbitrary number of primary and secondary users. Comparing the hybrid SPSO approach and the proposed approach, the proposed scheme has shown its effectiveness in increasing the sum throughput to 7%, 16%, and 31% under the multimedia, balanced and power saving operational scenarios, respectively. In addition, the proposed approach is more power efficient as it can provide additional power savings of 3.58 W, 2.48 W, and 1.6741 W under the aforementioned operational scenarios, respectively.
Journal Article