Mobility-aware computation offloading in edge computing using prediction

Maleki, Erfan Farhangi; Mashayekhy, Lena

doi:10.1109/icfec50348.2020.00015

Cited by 13 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work presented in Reference 6 has addressed the problem of overload and minimizing delays at fog nodes. The authors have proposed a method that considers changing nature of mobile devices and lowers the turnaround time.…”

Section: Related Workmentioning

confidence: 99%

“…There is a shortage of storage when such devices sense data for a long time. Battery drainage, low computation power, and less storage make them inefficient in storing and analyzing such data 6 . There is a desperate need to process this data either locally or remotely.…”

Section: Introductionmentioning

confidence: 99%

“…Battery drainage, low computation power, and less storage make them inefficient in storing and analyzing such data. 6 There is a desperate need to process this data either locally or remotely. Local processing is limited due to the size and power of the device.…”

mentioning

confidence: 99%

See 2 more Smart Citations

e‐TOALB: An efficient task offloading in IoT‐fog networks

Lone,

Sofi

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummarySmart devices are concerned about the processing and computation of tasks due to their tiny nature. They prefer to offload their tasks to the cloud for processing and computation. Due to the huge amount of data being generated by smart devices, the cloud becomes inefficient in terms of huge delay. Thus, Processing tasks in the cloud can add latency and finally needs to be addressed. Thus, fog computing is an alternative to the latency issue. The tasks are offloaded to fog instead of the cloud. In this paper, e‐TOALB (enhanced task offloading and load balancing), a modified and enhanced nature‐inspired and meta‐heuristic ant colony optimization is used to offload tasks in a fog environment. The results obtained by the proposed method are compared with Particle swarm optimization (PSO), round robin (RR), and ant colony optimization. The numerical results clearly show an improvement in average response time and load sharing among all fog nodes. The results of the proposed model produce low response time, low average service time, and low standard deviation. The proposed scheme aims to find the best possible decision for offloading tasks to nearby fog devices and to find an optimal route for offloading with the least communication cost and average service time.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

e‐TOALB: An efficient task offloading in IoT‐fog networks

Lone,

Sofi

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…1: Proposed Edge Infrastructure architecture and scenario of these nodes as a complex optimization problem. The proposed strategies use the Lyapunov optimization method [6], [8] or Integer Linear Programming method [7] to optimize the dynamic placement of IoT services on the Edge. They show significant results in terms of placement reliability, QoS guarantee and energy consumption [9].…”

Section: Related Work a State-of-the-art Strategiesmentioning

confidence: 99%

Applying Fuzzy Logic to Efficiently Manage Shared Edge Infrastructure

Sylla,

Chalouf,

Mendiboure

et al. 2023

2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)

View full text Add to dashboard Cite

Edge Computing promise a bright future for Internet of Things (IoT). Edge Computing servers can be geographically distributed to be closer to users and ensure low latency communications and real-time data processing for the third-party applications using this infrastructure. However, user mobility and limited Edge servers capabilities (CPU, memory, bandwidth) may cause many services placement failures. That's why in this article we propose a new strategy aimed at enabling a fair sharing of available Edge resources through a dynamic and real-time IoT Service placement. Our strategy uses Fuzzy Logic to enable 1) quick and low-cost deployment of the solution and 2) real-time modification of the policies defined by the Edge operator. It takes advantage, among others, of a microservice decomposition to optimize the use of the Edge architecture. Experiments demonstrate the benefits of our approach in terms of placement reliability, execution time and resources utilization.

show abstract

“…Mobility prediction is different for humans and vehicles. Human mobility is identified is based on the location, time, contact, connectivity, temporal, spatial, and connectivity of the individual human models, and vehicular mobility is based on their geographical location, connectivity, RSU, and smart devices [5]. The general mobility prediction of humans is derived from the properties of the Generic mechanism like exploration and preferential method.…”

Section: Introductionmentioning

confidence: 99%

Effective Mobility Identification in Mobile Fog Environment with the Internet of Things

Deepa¹,

Jothi

2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Fog extends the cloud to be closer to the end devices so that it acts on IoT data within a millisecond. Almost 60% of data can be analyzed that is physically near to the IoT data. This proximity has various advantages, including reduced latency, which improves the user experience. However, because the distance to a fog service may vary as a user moves from one location to another, user mobility may restrict such benefits in practice. A fog service migration is based on a mitigation approach that allows the service to always be close enough to a user. Quality of Service is decreased because of the mobility of the user’s location. Predicting the future location in advance improves the efficiency of service provisioning. In this work, a dynamic mobility model is proposed to find the user location in advance. This experiment was carried out by LuST mobility data set collected by Luxembourg Simulation of Urban Mobility (SUMO) Traffic (LuST). This result is give better accuracy of location prediction up to 98.87% when compared with existing methods.

show abstract

Mobility-aware computation offloading in edge computing using prediction

Cited by 13 publications

References 15 publications

e‐TOALB: An efficient task offloading in IoT‐fog networks

e‐TOALB: An efficient task offloading in IoT‐fog networks

Applying Fuzzy Logic to Efficiently Manage Shared Edge Infrastructure

Effective Mobility Identification in Mobile Fog Environment with the Internet of Things

Contact Info

Product

Resources

About