Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

Abdullaev, Ilyоs; Prodanova, Natalia; Aruna Bhaskar, K.; Laxmi Lydia, E.; Kadry, Seifedine; Kim, Jungeun

doi:10.32604/cmc.2023.038417

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…Empathy quality can be improved by providing personal attention and considering the tourists' needs. Improving service quality can increase tourist loyalty and lead to successful business performance [29][30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Discussionmentioning

confidence: 99%

The Effects of Service Quality on Tourist Loyalty Towards Malaysian Budget Hotels

Abdul Latif,

Syed Nasir,

Matyakubov

et al. 2024

BIO Web Conf.

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The hotel industry plays a vital role in tourism development by providing visitor accommodation facilities. This paper focuses on the effects of service quality on the budget hotel industry in Malaysia. Despite the increasing number of tourists opting for budget hotels in recent years, issues related to service quality have affected tourist satisfaction and loyalty, posing significant challenges for the budget hotel sector to maintain its business. The study analyzed the effects of service quality on tourist loyalty towards budget hotels and identified the most influential factor in service quality that affects tourist loyalty. A quantitative approach was employed in this study, and the findings indicate that the responsiveness quality dimension has the most significant effect on tourist loyalty. In contrast, assurance and reliability have no significant effects. Budget hotels must improve their service quality, specifically responsiveness, to enhance tourist loyalty and maintain their business in the highly competitive hotel industry.

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Section: Discussionmentioning

confidence: 99%

The Effects of Service Quality on Tourist Loyalty Towards Malaysian Budget Hotels

Abdul Latif,

Syed Nasir,

Matyakubov

et al. 2024

BIO Web Conf.

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“…Many works combine ML-based solutions with other tools like meta-heuristics. For example, [3] uses DL with Seagull Optimization (TORA-DLSGO) algorithm aiming to minimize energy consumption subject to the latency requirements and restricted resources, using SGO algorithm for the parameter tuning of the Deep Belief Network (DBN) model, which is used for optimum offloading decision-making purposes. An objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.…”

Section: Ml-based Solutionsmentioning

confidence: 99%

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

Nieto,

de la Iglesia,

Lopez-Novoa

et al. 2024

J Cloud Comp

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The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not critical, but it is not useful when latency matters, so Multi-access Edge Computing (MEC) can be of use. In this work, we propose a distributed Deep Reinforcement Learning (DRL) tool to optimize the binary task offloading decision, this is, the independent decision of where to execute each computing task, depending on many factors. The optimization goal in this work is to maximize the Quality-of-Experience (QoE) when performing tasks, which is defined as a metric related to the battery level of the UE, but subject to satisfying tasks’ latency requirements. This distributed DRL approach, specifically an Actor-Critic (AC) algorithm running on each User Equipment (UE), is evaluated through the simulation of two distinct scenarios and outperforms other analyzed baselines in terms of QoE values and/or energy consumption in dynamic environments, also demonstrating that decisions need to be adapted to the environment’s evolution.

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“…It is also worth noting the impact of globalization on the market of new medical technologies [33][34][35][36][37]. This applies to digital technologies for monitoring human health [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Development of Medical Services Market in the Context of Model-Based Innovation Economy

Rakhimova,

Yusupova,

Andryushchenko

et al. 2024

BIO Web Conf.

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The relevance of the article is due to the current stage of worldwide medical services development. The global nature of innovation development and digital technologies have affected all spheres of human activity, including medicine. On the other hand, the increasing role of social aspects in economic development forms new tasks in the development of healthcare. It should become of a better quality and more accessible. The paper used general scientific and special research methods. Among the special research methods, we used mathematical methods: statistical research, correlation and regression analysis. This article presents the results of forecasting the development of the medical services market in the Khorezm region of the Republic of Uzbekistan in the mid-term perspective. The mathematical model of forecasting the development of the medical services market took into account such factors as the number of private clinics, the number of employees of medical organizations, the real cost of medical services in constant prices, the number of operating hospital facilities, real total income per capita in constant prices of organizations, total income per capita in constant prices. Correlation analysis showed a high, stable relationship between the indicators. Regression analysis allowed to determine the forecast parameters of the market of medical services in the Khorezm region of the Republic of Uzbekistan. The reliability of calculations was checked by Student’s t-test and Durbin-Watson statistic test. Increasing the quality of life of the population in the region is the basis for increasing demand for quality health services and will lead to the growth of the private sector of health services.

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

Cited by 12 publications

References 25 publications

The Effects of Service Quality on Tourist Loyalty Towards Malaysian Budget Hotels

The Effects of Service Quality on Tourist Loyalty Towards Malaysian Budget Hotels

Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum

Forecasting Development of Medical Services Market in the Context of Model-Based Innovation Economy

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