Providing healthcare services on-the-fly using multi-player cooperation game theory in Internet of Vehicles (IoV) environment

Kumar, Neeraj; Kaur, Kuljeet; Jindal, Anish; Rodrigues, Joel J. P. C.

doi:10.1016/j.dcan.2015.05.001

Cited by 46 publications

(16 citation statements)

References 41 publications

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“…Dasgupta et al [24] describe a mixed reality-based testbed and a framework for studying social interactions using a game theory approach and present a case study for exposure therapy treatment of individuals with Social Anxiety Disorder using the proposed testbed. Based on multi-player cooperation game theory, Kumar et al [25] propose a novel E-healthcare model coupled with cloud computing platform to provide health related services in IoV (Internet of Vehicles) environment on-the-fly. Through these studies, game theory has the potential to provide a new conceptual and theoretical basis for future empirical work on the interaction between doctors and their patients.…”

Section: Game Theory In Healthmentioning

confidence: 99%

Tripartite Data Analysis for Optimizing Telemedicine Operations: Evidence from Guizhou Province in China

Zhang

Liu

et al. 2020

IJERPH

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Telemedicine is an innovative approach that helps alleviate the health disparity in developing countries and improve health service accessibility, affordability, and quality. Few studies have focused on the social and organizational issues involved in telemedicine, despite in-depth studies of and significant improvements in these technologies. This paper used evolutionary game theory to analyze behavioral strategies and their dynamic evolution in the implementation and operation of telemedicine. Further, numerical simulation was carried out to develop management strategies for promoting telemedicine as a new way of delivering health services. The results showed that: (1) When the benefits are greater than the costs, the higher medical institutions (HMIs), primary medical institutions (PMIs), and patients positively promote telemedicine with benign interactions; (2) when the costs are greater than the benefits, the stability strategy of HMIs, PMIs, and patients is, respectively, ‘no efforts’, ‘no efforts’, and ‘non-acceptance’; and (3) promotion of telemedicine is influenced by the initial probability of the ‘HMI efforts’, ‘PMI efforts’, and ‘patients’ acceptance’ strategy chosen by the three stakeholders, telemedicine costs, and the reimbursement ratio of such costs. Based on theoretical analysis, in order to verify the theoretical model, this paper introduces the case study of a telemedicine system integrated with health resources at provincial, municipal, county, and township level in Guizhou. The findings of the case study were consistent with the theoretical analysis. Therefore, the central Chinese government and local governments should pay attention to the running cost of MIs and provide financial support when the costs are greater than the benefits. At the same time, the government should raise awareness of telemedicine and increase participation by all three stakeholders. Lastly, in order to promote telemedicine effectively, it is recommended that telemedicine services are incorporated within the scope of medical insurance and the optimal reimbursement ratio is used.

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Section: Game Theory In Healthmentioning

confidence: 99%

Tripartite Data Analysis for Optimizing Telemedicine Operations: Evidence from Guizhou Province in China

Zhang

Liu

et al. 2020

IJERPH

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“…Machine learning algorithms have got wide use in resource management problems. In Reference 33, a LA‐based game theory model is proposed for the virtual machine scheduling in cloud. This method test in healthcare service, and it could have better results than others in delay, throughput, and packet delivery metrics.…”

Section: Related Workmentioning

confidence: 99%

Multiuser context‐aware computation offloading in mobile edge computing based on Bayesian learning automata

Farahbakhsh

Shahidinejad

Ghobaei‐Arani

2020

Trans Emerging Tel Tech

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Today a lot of data sensed from the environment by the Internet of things applications. These data need to process with the lowest delay. Mobile devices (MDs) as ubiquitous tools are end devices in the network. These devices with limited resources cannot process all computations locally. Mobile edge computing (MEC) is a good architecture for processing computations in the network's edge. It solves the cloud challenges such as delay, energy, and cost. If MDs could not process the computations, they will offload tasks to the edge or cloud. Research shows that ignoring context information of application, requests, sensors, resources, and network tools cause to not complete the offloading method. In this article, we consider Bayesian learning automata (BLA) with considering context‐aware offloading in MEC with multiuser. BLA learns all states and actions in the network and helps us to improve the offloading algorithm. The contexts are collected using autonomous management as the monitor‐analysis‐plan‐execution loop in all offloading processes. The simulation results indicate that our method is superior to local computing and offload without considering context‐aware algorithms in some metrics such as energy consumption, execution cost, network usage, delay, and fairness.

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“…Some studies recommended on selecting nearby and suitable hospitals or physicians according to patients' conditions [162,163]. A study focused on providing healthcare services and treatment while travelling vehicles on the fly [164]. Another study presented the existence of first-aid services in an ambulance [165].…”

Section: Server Based (Tier 3)mentioning

confidence: 99%

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

et al. 2019

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Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decisionmaking theory to address the current problems in the prioritisation of patients with MCDs.

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Providing healthcare services on-the-fly using multi-player cooperation game theory in Internet of Vehicles (IoV) environment

Cited by 46 publications

References 41 publications

Tripartite Data Analysis for Optimizing Telemedicine Operations: Evidence from Guizhou Province in China

Tripartite Data Analysis for Optimizing Telemedicine Operations: Evidence from Guizhou Province in China

Multiuser context‐aware computation offloading in mobile edge computing based on Bayesian learning automata

Real-Time Remote-Health Monitoring Systems: a Review on Patients Prioritisation for Multiple-Chronic Diseases, Taxonomy Analysis, Concerns and Solution Procedure

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