&lt;italic&gt;Chimera&lt;/italic&gt;: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications

Pu, Lingjun; Xu, Chen; Mao, Guoqiang; Xie, Qinyi; Xu, Jingdong

doi:10.1109/jiot.2018.2872436

Cited by 90 publications

(51 citation statements)

References 45 publications

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“…In edge-cloud computing, different task offloading methods require various system information, and the global system information is needed for making optimal task offloading decisions [158]. While, the edge-cloud system scales become more and more large, which increases the challenge in the system information monitoring.…”

Section: J Information Monitoringmentioning

confidence: 99%

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

et al. 2020

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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: J Information Monitoringmentioning

confidence: 99%

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

et al. 2020

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“…and processing units, mobile vehicles can cluster together to provide location-based services, which is considered as mobile crowdsensing. In the introduced computing architecture, these clustered vehicles work as opportunistic edge nodes to sense, process, and upload the real-time data, contributing to an efficient EVN operation [132], [133]. For instance, the real-time vehicular travelling data not only helps the operator track the on-road traffic condition [134], but also contributes to accurate EV travelling pattern prediction [135].…”

Section: ) Vehicles and Uavs As Opportunistic Edge Nodesmentioning

confidence: 99%

Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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“…Ma et al [9] proposed two privacy preserving reputation management schemes: Basic Privacy Preserving Reputation Management (B-PPRM) and Advanced Privacy Preserving Reputation Management (A-PPRM), for edge computing enhanced MCS to simultaneously preserve privacy and deal with malicious participants. Pu et al [10] presented a novel hybrid edge computing framework integrated with the emerging edge cloud radio access network, called Chimera, and formulated a novel multivehicle and multitask offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. Yang et al [11] presented a novel edge-mediated mobile crowd sensing system, namely EdgeSense, which works on top of a secured peer-to-peer network consisting of participants.…”

Section: Mobile Corwdsensing With the Edge Computing Paradigmmentioning

confidence: 99%

“…Recently, edge computing based mobile crowdsensing architecture has been proposed [7][8][9][10][11] to solve above issues. The main benefits of edge computing based architecture for large-scale mobile crowdsensing are as follows:…”

Section: Introductionmentioning

confidence: 99%

“…There have already been some studies of mobile crowdsensing, which use edge computing to achieve some goals, e.g., edge computing based data processing [7,15], privacy-preserving [8,16,17], reputation management [9], task offloading of vehicular crowdsensing applications [10] and extraction of the environmental information [11]. However, there is no truth discovery and incentive mechanism design in the state of the art.…”

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confidence: 99%

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Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Yang

Lü

et al. 2020

Sensors

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The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.

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<italic>Chimera</italic>: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications

Cited by 90 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

Energy and Information Management of Electric Vehicular Network: A Survey

Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

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