An Optimization and Auction-Based Incentive Mechanism to Maximize Social Welfare for Mobile Crowdsourcing

Wang, Yingjie; Cai, Zhipeng; Zhan, Zhi‐Hui; Gong, Yue-Jiao; Tong, Xiangrong

doi:10.1109/tcss.2019.2907059

Cited by 123 publications

(35 citation statements)

References 51 publications

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“…Because of the development of cloud computing and big data analytics [43][44][45][46][47][48], the Web services ecosystem is becoming more complex and large [4,[49][50][51][52][53][54][55][56]. For further studies, we will do research on how to ensure and improve the efficiency of UIQPCA in large-scale scenarios on the Spark platform.…”

Section: Resultsmentioning

confidence: 99%

Quality Prediction of Web Services Based on a Covering Algorithm

2020

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Service-oriented architecture (SOA) is widely used, which has fueled the rapid growth of Web services and the deployment of tremendous Web services over the last decades. It becomes challenging but crucial to find the proper Web services because of the increasing amount of Web services. However, it proves unfeasible to inspect all the Web services to check their quality values since it will consume a lot of resources. Thus, developing effective and efficient approaches for predicting the quality values of Web services has become an important research issue. In this paper, we propose UIQPCA, a novel approach for hybrid User and Item-based Quality Prediction with Covering Algorithm. UIQPCA integrates information of both users and Web services on the basis of users’ ideas on the quality of coinvoked Web services. After the integration, users and Web services which are similar to the target user and the target Web service are selected. Then, considering the result of integration, UIQPCA makes predictions on how a target user will appraise a target Web service. Broad experiments on WS-Dream, a web service dataset which is widely used in real world, are conducted to evaluate the reliability of UIQPCA. According to the results of experiment, UIQPCA is far better than former approaches, including item-based, user-based, hybrid, and cluster-based approaches.

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Section: Resultsmentioning

confidence: 99%

Quality Prediction of Web Services Based on a Covering Algorithm

2020

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“…indicates the weight of a tree is 1 in the DP model owing to only covering one keyword node [27]. Formula (2) indicates that T Pmin (v, K ′ ) is obtained by using the following two operations: tree growth operation (i.e., formulas (3) and 4) and tree merging operation (i.e., formulas (5) and 6). In Figure 6(a), tree growth operation generates new T Pmin (v, K ′ ) by adding new node u (i.e., one of v's neighbors) to T Pmin (v, K ′ ).…”

Section: Pr Keyword+pop Approachmentioning

confidence: 99%

“…e first phase is entering keywords; users analyze their research requirements and enter all query keywords (e.g., k 1 , k 2 , k 3 , and k 6 ) to a recommender system. e second phase is paper discovery [3]; the recommender system automatically identifies diverse sets of candidate papers. e third phase is paper selection [4,5]; the recommender system recommends candidate papers containing query keywords to users.…”

Section: Introductionmentioning

confidence: 99%

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Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

Kou

Yan

2020

Complexity

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Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.

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“…To tackle this issue, auction theory has been an invaluable tool for MCS-based systems to discover users' truthful costs and effectively control the payment for the selected users. In the literature, many researchers have studied the auction-based incentive mechanisms for MCS-based applications [20]- [24]. For example, Zhao et al [20] investigated an online auctionbased incentive mechanism for mobile crowdsourced sensing and Ji and Wang [21] proposed an auction-based incentive mechanism to study the correlated tasks allocation problem.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Aware Sensing-Quality-Based Budget Feasible Incentive Mechanism for Crowdsourcing Fingerprint Collection

Zhang

Tanaka

2020

IEEE Access

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Mobile crowdsourcing (MCS) has shown great potential in received signal strength (RSS) fingerprint collection, in which an incentive mechanism plays a critical role to motivate users' participation. However, how to quantify the quality of the gathered fingerprint data is still not addressed well in the design of incentive mechanism for MCS-based fingerprint collection. In this paper, a sensing quality metric is proposed to characterize the joint impact of users' privacy protection and the spatial coverage of the submitted data. Given a limited budget, a basic incentive mechanism is devised to recruit appropriate users to maximize sensing quality. Considering that the cost of each user is regarded as private information and users may be attempted to misreport their costs to increase the revenue. Hence, an auction-based incentive mechanism is proposed to achieve the truthfulness of users' costs, which is truthful, individually rational, computationally efficient and budget feasible. Simulation results show that our proposed schemes outperform the baseline schemes and the experiment with real-world data is carried out to evaluate the performance of our proposed basic incentive mechanism. INDEX TERMS Local differential privacy, incentive mechanism, auction theory, crowdsourced fingerprint collection.

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An Optimization and Auction-Based Incentive Mechanism to Maximize Social Welfare for Mobile Crowdsourcing

Cited by 123 publications

References 51 publications

Quality Prediction of Web Services Based on a Covering Algorithm

Quality Prediction of Web Services Based on a Covering Algorithm

Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

Privacy-Aware Sensing-Quality-Based Budget Feasible Incentive Mechanism for Crowdsourcing Fingerprint Collection

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