Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments

Hong, Zicong; Chen, Wuhui; Huang, Huawei; Guo, Song; Zheng, Zibin

doi:10.1109/tpds.2019.2926979

Cited by 205 publications

(85 citation statements)

References 45 publications

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“…In the edge tier, there are one or more edge centers (short for edges), each of which is composed of one or more edge servers communicating with some user devices for performing offloaded tasks by corresponding APs. An edge server has a connection with some other edge servers [11], [16] or the cloud [16] tier for "borrowing" resources when it cannot complete all tasks offloaded to it. Due to the limitation of spaces and auxiliary equipments, an edge usually has only a few servers, and thus the cloud tier is needed for serving users as edge resources are not enough sometimes when user loads are high.…”

Section: A Edge-cloud Computing Environmentmentioning

confidence: 99%

“…A very few works concern the QoE as a metric directly. energy Gao et al [49] independent full cost Chen et al [50] independent full cost Chen et al [51] independent full profit Yuan et al [52] independent full profit Lin et al [53] independent full performance, energy Du et al [54] independent full performance, energy Duan et al [55] independent full performance, energy Mahmud et al [56] independent full performance, profit Li et al [57] independent full Performance, cost Sun et al [58] independent full performance, cost Adhikari et al [59] independent full performance, utilization Ma et al [60] independent full QoE, cost Miao et al [61] independent partial performance Kai et al [62] independent partial performance Guo et al [63] independent partial performance Meng et al [64], [65] independent partial performance hop-e Cui et al [66], [67] independent partial performance hop-d, hop-e Sarkar et al [68] independent partial performance hop-e Ouyang et al [69] independent partial performance Y Cheng et al [70] independent partial energy Xia et al [71] independent partial energy Zhang et al [72] independent partial cost Chabbouh et al [73] independent partial performance, balance Y Wang et al [74] independent partial performance, cost Zhao et al [75] independent partial performance, cost Khayyat et al [76] independent partial performance, energy Alshahrani et al [77] independent partial performance, energy Chen et al [78] independent partial performance, cost, energy Hong et al [16] independent partial performance, energy hop-d Sun et al [79] independent partial performance, energy Long et al [80] independent partial performance, energy Nguyen et al…”

Section: Optimization Objectivementioning

confidence: 99%

“…Multi-hop cooperative offloading can be applied in two scenarios of connected devices (hop-d) [98] and connected edges (hop-e) [28]. In the first scenario, a task of device A is offloaded to another device B instead of an edge or a cloud when B provides a better performance for the task [16], [98]. The concern of multi-hop cooperation may improve the performance of a task offloading while increase much more complexity for an optimal solution, and thus there are only a few related works concerning it.…”

Section: E Multi-hop Cooperative Offloadingmentioning

confidence: 99%

See 2 more Smart Citations

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Section: A Edge-cloud Computing Environmentmentioning

confidence: 99%

Section: Optimization Objectivementioning

confidence: 99%

Section: E Multi-hop Cooperative Offloadingmentioning

confidence: 99%

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A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

Wang

Huang

et al. 2020

IEEE Access

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“…In [28], the authors proposed a task offloading and resource allocation algorithm based on software defined network technology in ultra-dense network, the battery capacity of the devices was considered as an impact factor of task offloading decision. Hong et al [29] studied the computation offloading problem for sensing devices, the sensing devices could offload the tasks through multi-hop communication.…”

Section: Background and Related Workmentioning

confidence: 99%

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

Long

Luo

Zhu

et al. 2020

Preprint

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With the developing of Wireless Sensor Networks (WSNs), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

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“…In the pervasive edge computing environment, all mobile devices (including smartphones, tablets, laptops and other mobile devices) are edge nodes that can provide computing resources. In the pervasive edge computing environment, through computing offload technology, users' mobile devices can be used as tools to handle some tasks [14,27]. Generally speaking, better system performance can be achieved by using resources unused by mobile devices in pervasive edge computing.…”

Section: Introductionmentioning

confidence: 99%

DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

Sun

et al. 2020

Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure

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In recent years, edge computing transforms the network edge into an intelligent platform to provide better services for users by making storage, computing, control and network function closer to end-users, things, and sensors. However, due to the limitation of edge resources and the diversity of edge resource providers, there is no good mechanism to solve the resource allocation problem in the edge computing resource allocation market. Aiming at the problem of resource allocation in edge computing, this paper proposes a double auction scheme-DPODA. This scheme establishes the auction market of edge computing resources, which can improve the social welfare of auction to the maximum extent. Moreover, as the private information of users in the auction market may affect market transactions, in order to solve this problem, this paper uses differential privacy technology to effectively protect the privacy of both sides of the auction, so as to avoid the unfairness in the auction market. A large number of simulation experiments show that the algorithm proposed in this paper not only achieves the research purpose but also ensures the privacy information security of the auctioneer. CCS CONCEPTS • Security and privacy Human and societal aspects of security and privacy; • Networks Cloud computing; • Humancentered computing Ubiquitous and mobile computing; • Theory of computatio Online algorithms;

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Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments

Cited by 205 publications

References 45 publications

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

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

DPODA: Differential Privacy-based Online Double Auction for Pervasive Edge Computing Resource Allocation

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