Reliable Mobile Edge Service Offloading Based on P2P Distributed Networks

Tang, Lujie; Tang, Bing; Tang, Luyu; Guo, Feiyan; Zhang, Jiaming

doi:10.3390/sym12050821

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Ref. [18] regards edge devices as symmetrical P2P (peer-to-peer) networks, and proposes an efficient and fault-tolerant computing offload strategy, which provides the optimal node to interact with the vehicle and reduces the task completion delay. These studies address the communication delay problem in asymmetric vehicular networks by optimizing the task allocation between edge devices or by facilitating collaborative computation among edge devices.…”

Section: Related Workmentioning

confidence: 99%

“…Data security [12] Machine Learning Machine Learning Platform [13] RBF neural network Radial Basis Function neural network sliding mode controller [14] Fast R-CNN Multi-strategy region proposal network algorithm [15] Data fusion approaches Data collection framework Asymmetric network [16] Blockchain-as-a-Service multi-preference matching [17] Deep reinforcement learning Cloud-edge cooperative content-delivery strategy [18] Mobile edge computing Task offloading and fault tolerance algorithm traditional consensus algorithms [19] PoW [20] PoS [21] DPoS consensus algorithms in IoV [23] PoW,PoS Decentralized trust management system [24] PoW,PBFT Blockchain-based TM with conditional privacy-preserving scheme [25] DPoS Soft security solution [26] PBFT,EigenTrust T-PBFT…”

Section: Literature Basic Algorithm Innovationmentioning

confidence: 99%

See 1 more Smart Citation

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Sun

Wang

et al. 2023

Symmetry

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With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof of Stake), only consider the node features, which is hard to adapt to the immediacy and flexibility of vehicles. On the other hand, classical consensus algorithms often require mass computing, which undoubtedly increases the communication overhead, resulting in the inability to achieve collaborative IoV under asymmetric networks. Therefore, proposing a low failure rate consensus algorithm that takes into account running time and energy consumption becomes a major challenge in IoV applications. This paper proposes an AI-enabled consensus algorithm with vehicle features, combining vehicle-based metrics and neural networks. First, we introduce vehicle-based metrics such as vehicle online time, performance, and behavior. Then, we propose an integral model and a hierarchical classification method, which combine with a BP neural network to obtain the optimal solution for interconnection. Among them, we also use Informer to predict the future online duration of vehicles, which effectively solves the situation that the primary node vehicle drops off in collaborative IoV. Finally, the experimentations show that the vehicle-based metrics eliminate the problem of the primary node vehicle being offline, which realizes the collaborative IoV considering vehicle features. Meanwhile, it reduces the vehicle network system delay and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Literature Basic Algorithm Innovationmentioning

confidence: 99%

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Sun

Wang

et al. 2023

Symmetry

View full text Add to dashboard Cite

show abstract

“…Mobile edge computing has been emerged as a new paradigm of distributed systems [1,2]. There are many research topics about MEC, for example, server placement [6], application placement, task offloading [7,8], network architecture, and so on [9]. In order to use the services provided by the edge network, how mobile devices offload tasks to the MEC server so as to obtain the minimum total task processing cost and obtain a reasonable offloading decision is a popular research direction of MEC.…”

Section: Mobile Edge Computingmentioning

confidence: 99%

Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

You

Tang

2021

J Cloud Comp

Self Cite

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As a new form of computing based on the core technology of cloud computing and built on edge infrastructure, edge computing can handle computing-intensive and delay-sensitive tasks. In mobile edge computing (MEC) assisted by 5G technology, offloading computing tasks of edge devices to the edge servers in edge network can effectively reduce delay. Designing a reasonable task offloading strategy in a resource-constrained multi-user and multi-MEC system to meet users’ needs is a challenge issue. In industrial internet of things (IIoT) environment, considering the rapid increase of industrial edge devices and the heterogenous edge servers, a particle swarm optimization (PSO)-based task offloading strategy is proposed to offload tasks from resource-constrained edge devices to edge servers with energy efficiency and low delay style. A multi-objective optimization problem that considers time delay, energy consumption and task execution cost is proposed. The fitness function of the particle represents the total cost of offloading all tasks to different MEC servers. The offloading strategy based on PSO is compared with the genetic algorithm (GA) and the simulated annealing algorithm (SA) through simulation experiments. The experimental results show that the task offloading strategy based on PSO can reduce the delay of the MEC server, balance the energy consumption of the MEC server, and effectively realize the reasonable resource allocation.

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“…Tang et al [70], proposed a reliable mobile edge service offloading (RMESO) mechanism to optimize task offloading ratio in MEC. This work minimizes task execution latency by considering a peer-to-peer (P2P) network strategy.…”

Section: ) Rmesomentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

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The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

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Reliable Mobile Edge Service Offloading Based on P2P Distributed Networks

Cited by 14 publications

References 38 publications

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

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