A Survey of Mobile Computation Offloading: Applications, Approaches and Challenges

Kumar, Sandeep; Tyagi, Manan; Khanna, Abhirup; Fore, Vivudh

doi:10.1109/icacce.2018.8441740

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…With cloud computing and mobile cloud computing, References [14][15][16] formulate an advanced task offloading model from resource allocation, data availability, scalability, performance augmentation, energy saving, and security and privacy. How to manage the exponential data traffic increase is always a key challenge to improving network performance.…”

Section: Related Workmentioning

confidence: 99%

“…The specific operations on multi-user task offloading are elaborated in vehicular networks as follows: the ESs M send out the overall received power to the vehicles N . The vehicles N can get wireless channel interference according to Equation (15). Then, these vehicles decide whether to change current offloading decision by Equation ( 16) and send out a request message to the ES.…”

Section: The Proposed Task Offloading Algorithmmentioning

confidence: 99%

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Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

2021

Electronics

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Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to solve this problem at the edge of network for multiple mobile users. In this paper, we propose the multi-user task offloading strategy based on game theory to reduce the computational complexity and improve system performance. The task offloading decision making as a multi-user task offloading game is formulated to demonstrate how to achieve the Nash equilibrium (NE). Additionally, a task offloading algorithm is designed to achieve a NE, which represents an optimal or sub-optimal system overhead. In addition, the vehicular communication simulation frameworks Veins, SUMO model and OMNeT++ are adopted to run the proposed task offloading strategy. Numerical results show that the system overhead of the proposed task offloading strategy can degrade about 24.19% and 33.76%, respectively, in different scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: The Proposed Task Offloading Algorithmmentioning

confidence: 99%

Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

2021

Electronics

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“…Computation offloading is recognized as a promising solution by migrating a part or an entire application to a remote server in order to be executed there. Various models and frameworks have been proposed to offload resource-intensive components of applications for more efficient execution [3][4][5]. However, these solutions rely on the concept of offloading to the cloud.…”

Section: Introductionmentioning

confidence: 99%

Enabling Processing Power Scalability with Internet of Things (IoT) Clusters

Coelho

Nogueira

2021

Electronics

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Internet of things (IoT) devices play a crucial role in the design of state-of-the-art infrastructures, with an increasing demand to support more complex services and applications. However, IoT devices are known for having limited computational capacities. Traditional approaches used to offload applications to the cloud to ease the burden on end-user devices, at the expense of a greater latency and increased network traffic. Our goal is to optimize the use of IoT devices, particularly those being underutilized. In this paper, we propose a pragmatic solution, built upon the Erlang programming language, that allows a group of IoT devices to collectively execute services, using their spare resources with minimal interference, and achieving a level of performance that otherwise would not be met by individual execution.

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A Comparative Review on Different Techniques of Computation Offloading in Mobile Cloud Computing

Sen,

Islam,

Pandit

et al. 2024

Fog Computing for Intelligent Cloud IoT Systems

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A Survey of Mobile Computation Offloading: Applications, Approaches and Challenges

Cited by 12 publications

References 18 publications

Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

Enabling Processing Power Scalability with Internet of Things (IoT) Clusters

A Comparative Review on Different Techniques of Computation Offloading in Mobile Cloud Computing

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