Computation offloading through mobile vehicles in IoT-edge-cloud network

Long, Jun; Luo, Yueyi; Zhu, Xiaoyu; Luo, Entao; Huang, Ming‐Feng

doi:10.1186/s13638-020-01848-5

Cited by 22 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With more attention having been given to the offloading problem in MCE, researchers give new directions to the scheduling targets, such as cost, security, and so on [1], [2], [14], [19], [20], [24]. Those are not related to our paper.…”

Section: Related Workmentioning

confidence: 98%

“…In Algorithm 2, 𝑒𝑒𝑇𝑇𝑛𝑛𝑛𝑛𝑠𝑠𝑖𝑖 (Line 1, Algorithm 2; same in the following paragraph) is used to record the maximizing Sufferage [42] of Formula (19). Lines 4 and 5 find the lowest 𝑒𝑒𝑒𝑒𝑛𝑛 𝑛𝑛𝑛𝑛 𝑖𝑖 and the second lowest 𝑒𝑒𝑒𝑒𝑛𝑛 𝑛𝑛𝑛𝑛 𝑖𝑖 , denoted by 𝑒𝑒𝑒𝑒𝑛𝑛1 𝑛𝑛𝑛𝑛 𝑖𝑖 and 𝑒𝑒𝑒𝑒𝑛𝑛2 𝑛𝑛𝑛𝑛 𝑖𝑖 .…”

Section: : Endwhilementioning

confidence: 99%

See 1 more Smart Citation

A Sufferage offloading tasks method for multiple edge servers

2022

KSII TIIS

View full text Add to dashboard Cite

The offloading method is important when there are multiple mobile nodes and multiple edge servers. In the environment, those mobile nodes connect with edge servers with different bandwidths, thus taking different time and energy for offloading tasks. Considering the system load of edge servers and the attributes (the number of instructions, the size of files, deadlines, and so on) of tasks, the energy-aware offloading problem becomes difficult under our mobile edge environment (MCE). Most of the past work mainly offloads tasks by judging where the job consumes less energy. But sometimes, one task needs more energy because the preferred edge servers have been overloaded. Those methods always do not pay attention to the influence of the scheduling on the future tasks. In this paper, first, we try to execute the job locally when the job costs a lower energy consumption executed on the MD. We suppose that every task is submitted to the mobile server which has the highest bandwidth efficiency. Bandwidth efficiency is defined by the sending ratio, the receiving ratio, and their related power consumption. We sort the task in the descending order of the ratio between the energy consumption executed on the mobile server node and on the MD. Then, we give a "suffrage" definition for the energy consumption executed on different mobile servers for offloading tasks. The task selects the mobile server with the largest suffrage. Simulations show that our method reduces the execution time and the related energy consumption, while keeping a lower value in the number of uncompleted tasks.

show abstract

Section: Related Workmentioning

confidence: 98%

Section: : Endwhilementioning

confidence: 99%

A Sufferage offloading tasks method for multiple edge servers

2022

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…As a result, vehicles would continuously upload the data collected by their local sensors to the closest edge device. 60 , 61 Hence, energy and power consumption at the edge should be considered to avoid service disruptions and quality of service (QoS) loss. 62 , 63 Furthermore, various situations need substantial QoS improvement to cope with occasional high traffic loads like severe traffic congestion, unpredicted weather conditions, or unexpected road construction works.…”

Section: Challenges Of V2x and Ecmentioning

confidence: 99%

Edge computing for Vehicle to Everything: a short review

Abdullah,

Yogarayan,

Abdul Razak

et al. 2023

F1000Res

View full text Add to dashboard Cite

Vehicle-to-everything (V2X) communications and services have sparked considerable interest as a potential component of future Intelligent Transportation Systems. However, the large amount of data generated by V2X communications can pose challenges for processing and distribution. Edge computing (EC) is a promising solution to these challenges, as it allows part or all of the data processing to be performed at the edge of the network, closer to the data sources. This paper reviews the existing literature on EC for V2X communications and services. We focus on several methods for implementing EC, including mobile edge computing, cloudlet, and fog computing. We compare them according to their applicability to V2X. The findings of this work indicate that EC can be a feasible solution for V2X communications and services. However, there are still some challenges that need to be addressed, such as the need for more efficient data processing algorithms and the need for a more comprehensive understanding of the trade-offs between EC and other solutions. The outcome of this work could considerably help other researchers better characterize EC applicability for V2X communications and services.

show abstract

“…As a result, vehicles would continuously upload the data collected by their local sensors to the closest edge device. 55,56 Hence, energy and power consumption at the edge should be considered to avoid service disruptions and quality of service (QoS) loss. 57,58 Furthermore, various situations need substantial QoS improvement to cope with occasional high traffic loads like severe traffic congestion, unpredicted weather conditions, or unexpected road construction works.…”

Section: Performancesmentioning

confidence: 99%

Edge computing for Vehicle to Everything: a short review

Abdullah

Yogarayan²,

Razak³

et al. 2022

F1000Res

View full text Add to dashboard Cite

Vehicle to Everything (V2X) communications and services have sparked considerable interest as a potential component of future Intelligent Transportation Systems. V2X serves to organise communication and interaction between vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrians (V2P), and vehicle to networks (V2N). However, having multiple communication channels can generate a vast amount of data for processing and distribution. In addition, V2X services may be subject to performance requirements relating to dynamic handover and low latency communication channels. Good throughput, lower delay, and reliable packet delivery are the core requirements for V2X services. Edge Computing (EC) may be a feasible option to address the challenge of dynamic handover and low latency to allow V2X information to be transmitted across vehicles. Currently, existing comparative studies do not cover the applicability of EC for V2X. This review explores EC approaches to determine the relevance for V2X communication and services. EC allows devices to carry out part or all of the data processing at the point where data is collected. The emphasis of this review is on several methods identified in the literature for implementing effective EC. We describe each method individually and compare them according to their applicability. The findings of this work indicate that most methods can simulate the EC positioning under predefined scenarios. These include the use of Mobile Edge Computing, Cloudlet, and Fog Computing. However, since most studies are carried out using simulation tools, there is a potential limitation in that crucial data in the search for EC positioning may be overlooked and ignored for bandwidth reduction. The EC approaches considered in this work are limited to the literature on the successful implementation of V2X communication and services. The outcome of this work could considerably help other researchers better characterise EC applicability for V2X communications and services.

show abstract

Computation offloading through mobile vehicles in IoT-edge-cloud network

Cited by 22 publications

References 48 publications

A Sufferage offloading tasks method for multiple edge servers

A Sufferage offloading tasks method for multiple edge servers

Edge computing for Vehicle to Everything: a short review

Edge computing for Vehicle to Everything: a short review

Contact Info

Product

Resources

About