Enabling Sustainable Smart Environments Using Fog Computing

Zanafi, Sarah; Aknin, Noura; Giacobbe, Maurizio; Scarpa, Marco; Puliafito, Antonio

doi:10.1109/icecocs.2018.8610509

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…So in order to reduce the processing time and have a timely access of the information's the fog network at a closer distance to the user was developed, in order reduce the time of the access, eluding the gap that could cause the unauthorized access so Mahmud et al [9] presents the survey on the fog network that was developed to handle the latency sensitive applications of the real-time. The fog computing provides a sustainable environment by reducing the energy consumption of the service provisions, that would be a challenge in the cloud computing Zanafi et al [10] proposes the fog network that provides with the sustainability smart environments by reducing the energy utilization of the services. Zhang, et al [11] gives the capable resource allocation that maximizes the resource utilization, but results with the increased cost other method proposed by Lan, et al [12] gives the use of the fog network for offloading, and resource allocation in D2D aided fog computing networks with the mixed integer non-linear programming problem.…”

Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation and Qos Enhancements of Iot With Fog Network

2019

JISMAC

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The fog network that is the complementary for the cloud services, bring down the services of the cloud to its edge device with the easy and the early access of the information’s for the task that are time sensitive for the internet of things. The enormous big data flow through the internet of things from various tasks in the variety of application has paved way to seek the efficient ways of resource allocation of the tasks in the fog network. So efficient way of resources allocation entailed to enhances the quality of service for the internet of things and improve the network performance, is proposed in the paper. The efficient resource allocation with reduced energy consumption and maximum resources utilization in the fog network is performed for the information’s gained over the internet of things. The performance of the proposed method is validated using the network simulator to gain knowledge on the proficiency of the proposed method of resource allocation in the fog.

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Section: Related Workmentioning

confidence: 99%

Efficient Resource Allocation and Qos Enhancements of Iot With Fog Network

2019

JISMAC

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“…Therefore, edge computing [1][2][3][4] has achieved tremendous development as a supplementary computing paradigm to overcome the above challenges. Among them, mobile edge computing (MEC), as a product of the integration of the Internet and Communication Technology (ICT), has become a key technology for the next generation of mobile networks [5,6] enabling mobile devices to distribute their computing tasks to servers located on the "edge" of the radio access network [7], and it creates more practical value while reducing core network congestion, such as lower latency, location awareness, and network context information. [8,9].…”

Section: Introductionmentioning

confidence: 99%

Virtual Machine Migration Strategy Based on Markov Decision and Greedy Algorithm in Edge Computing Environment

Gao

2023

Wireless Communications and Mobile Computing

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In view of the problem of long migration time and low availability of VM (virtual machine) migration in MD (mobile devices) in the MEC (mobile edge computing) environment, this paper designs a MD2N (Mobile Device to Network) model. This model no longer uploads requests to the cloud center, but centers on the edge server, scheduling and selecting migration services through the communication between the servers. And, a virtual machine migration strategy based on Markov decision and greedy algorithm is proposed. Model the edge server based on its geographic location, imitating the traditional cellular network method, and then the improved greedy algorithm is used to dynamically select the best edge device to migrate VM. Simulation and comparison experiments show that the proposed scheme has small migration fluctuations, stable migration status, short migration time, low average time delay, and high virtual machine migration availability after migration.

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“…In particular, Mobile Edge Computing (MEC) is becoming a key flexible and cost-effective mechanism that enables the mobile devices to offload their computation tasks to servers residing at the ''edge'' of the radio access networks [5], [6]. MEC reduces the computational effort of the devices, which are usually characterized by limited memory, computational capability, and battery life.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Data Offloading in Mobile Edge Computing for Internet of Things

2020

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Data offloading to Mobile Edge Computing (MEC) servers is an attractive choice for resource-constrained Internet of Things (IoT) devices, towards reducing their computational effort. In this paper, we investigate the potential of partial data offloading to MEC servers, under the perspective of users' cognitive IoT devices presenting loss averse and gain seeking behavior. Due to the sharing nature of the access environment and the MEC server's computational characteristics, we treat the MEC server option as a common pool of resources with uncertain payoff returned to the users, while the local computation capability is treated as a safe option for each user. Following the properties of Prospect Theory, users' prospect-theoretic utilities are formulated exploiting the local computing and offloading overhead options under probabilistic uncertainty. Such a modeling allows for the infusion of human awareness, inherent cognitive biases and behavioral characteristics into the devices' operation, their data offloading decisions and the edge computing environment that the devices are interacting with. Accordingly, each user's optimal offloaded data to the MEC server is obtained as the outcome of a non-cooperative game, with users attempting to maximize their own utilities. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven under the probabilistic nature of the respective payoff functions, while a distributed algorithm that convergences to the PNE is designed. Numerical results are provided that demonstrate the operation and superiority of the proposed framework under different IoT scenarios and behaviors, considering both homogeneous and heterogeneous users. INDEX TERMS Intelligent data offloading, mobile edge computing, Internet of Things, risk-based behavior modeling, cognitive decision making, probabilistic uncertainty.

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Enabling Sustainable Smart Environments Using Fog Computing

Cited by 6 publications

References 8 publications

Efficient Resource Allocation and Qos Enhancements of Iot With Fog Network

Efficient Resource Allocation and Qos Enhancements of Iot With Fog Network

Virtual Machine Migration Strategy Based on Markov Decision and Greedy Algorithm in Edge Computing Environment

Cognitive Data Offloading in Mobile Edge Computing for Internet of Things

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