Enabling Green Mobile-Edge Computing for 5G-Based Healthcare Applications

Bishoyi, Pradyumna Kumar; Misra, Sudip

doi:10.1109/tgcn.2021.3075903

Cited by 50 publications

(11 citation statements)

References 31 publications

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“…Other add-ons performed include local storage, security, data analysis, data compression, and standardization. Reference [8] proposed a novel approach for an energy-efficient task offloading to edge servers. Interaction among edge servers and wireless body area network (WBAN) users was formulated as a Stackelberg game, since users compete for edge servers.…”

Section: Related Work 21 Healthcare Systems With Cloud Computing Appr...mentioning

confidence: 99%

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Lee¹,

Mhatre²,

Das³

et al. 2023

Computers, Materials &Amp; Continua

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Healthcare is a fundamental part of every individual's life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture. We compare them, to identify the strengths and weaknesses of each algorithm; and provide their comparative results, to show latency and energy consumption performance. Challenging issues for cloudbased healthcare systems are discussed in detail.

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Section: Related Work 21 Healthcare Systems With Cloud Computing Appr...mentioning

confidence: 99%

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Lee¹,

Mhatre²,

Das³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…To make the edge computing domain more dynamic, the researchers and developers use a co‐offloading policy, where an application is migrated or offloaded to another edge device due to lack of computing resources or hardware/software failure of the currently running edge device 59,60 . In practice, most of the edge devices have limited processing power and battery capacity.…”

Section: Overview Of Edge Computingmentioning

confidence: 99%

“…58 To make the edge computing domain more dynamic, the researchers and developers use a co-offloading policy, where an application is migrated or offloaded to another edge device due to lack of computing resources or hardware/software failure of the currently running edge device. 59,60 In practice, most of the edge devices have limited processing power and battery capacity. Thus, there may be a chance that the currently running edge device is unable to complete the processing of an application and the running application needs to be migrated partially to another active edge device for further processing.…”

Section: Edge Framework and Offloading Policiesmentioning

confidence: 99%

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

2021

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Driven by the vision of real-time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve.These challenges are dealing with solving complex optimization problems including scheduling and processing real-time applications. Nature-inspired meta-heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP-complete problems, from computationally tractable approximate solutions to real-time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.

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“…In contrast to older generations, the 5G wireless network brings in a truly digital society by achieving substantial advancements in latency, data rates, mobility, and the number of connected devices. The 5G network with integrated technologies, such as IoT, mobile edge computing (MEC), fog computing, and ML, spans different domains of society, including robotics [4], vehicular networks [5], healthcare [6], etc. 5G is predicted to reach its limit in a decade; hence, the academia and the standardization bodies are already working toward a new generation of wireless communications, named 6G [7].…”

Section: Introductionmentioning

confidence: 99%

Overview of Distributed Machine Learning Techniques for 6G Networks

2022

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The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to serve many heterogeneous wireless devices. Various machine learning (ML) techniques are expected to be deployed over the intelligent 6G wireless network that provide solutions to highly complex networking problems. In order to do this, various 6G nodes and devices are expected to generate tons of data through external sensors, and data analysis will be needed. With such massive and distributed data, and various innovations in computing hardware, distributed ML techniques are expected to play an important role in 6G. Though they have several advantages over the centralized ML techniques, implementing the distributed ML algorithms over resource-constrained wireless environments can be challenging. Therefore, it is important to select a proper ML algorithm based upon the characteristics of the wireless environment and the resource requirements of the learning process. In this work, we survey the recently introduced distributed ML techniques with their characteristics and possible benefits by focusing our attention on the most influential papers in the area. We finally give our perspective on the main challenges and advantages for telecommunication networks, along with the main scenarios that could eventuate.

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Enabling Green Mobile-Edge Computing for 5G-Based Healthcare Applications

Cited by 50 publications

References 31 publications

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

Overview of Distributed Machine Learning Techniques for 6G Networks

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