联合区域热度和社交属性感知的移动群智感知参与者选择机制

向罗勇; 陈文; 张陆洋

doi:10.11959/j.issn.1000-0801.2020050

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联合区域热度和社交属性感知的移动群智感知参与者选择机制

研究与开发 | 更新时间：2024-06-05

- 联合区域热度和社交属性感知的移动群智感知参与者选择机制
- Regional heat and social attribute aware participant selection mechanis in mobile crowd sensing
- 电信科学 2020年36卷第2期页码：24-34
- 作者机构：
- 作者简介：
  
  [ "向罗勇（1986- ），重庆信息通信研究院（中国信息通信研究院西部分院）中级工程师，主要研究方向为无线通信检测、无线电频谱管理、移动通信天线设计与检测" ]
  [ "陈文（1990- ），重庆信息通信研究院（中国信息通信研究院西部分院）中级工程师，主要研究方向为无线电传输技术以及无线电产品测试认证" ]
  [ "张陆洋（1988- ），重庆信息通信研究院（中国信息通信研究院西部分院）中级工程师，主要研究方向为无线通信电磁兼容、工业互联网节点标识解析" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020050
  中图分类号： TP393
- 网络出版日期：2020-02，
  
  纸质出版日期：2020-02-20
- 稿件说明：
移动端阅览
向罗勇, 陈文, 张陆洋. 联合区域热度和社交属性感知的移动群智感知参与者选择机制[J]. 电信科学, 2020,36(2):24-34.

Luoyong XIANG, Wen CHEN, Luyang ZHANG. Regional heat and social attribute aware participant selection mechanis in mobile crowd sensing[J]. Telecommunications science, 2020, 36(2): 24-34.
向罗勇, 陈文, 张陆洋. 联合区域热度和社交属性感知的移动群智感知参与者选择机制[J]. 电信科学, 2020,36(2):24-34. DOI： 10.11959/j.issn.1000-0801.2020050.

Luoyong XIANG, Wen CHEN, Luyang ZHANG. Regional heat and social attribute aware participant selection mechanis in mobile crowd sensing[J]. Telecommunications science, 2020, 36(2): 24-34. DOI： 10.11959/j.issn.1000-0801.2020050.

摘要

针对群智感知中的平台在用户稀疏区域获取的任务数据可靠性低且任务难以按时完成的问题，提出一种联合区域热度和社交属性感知的参与者选择机制。首先，考虑不同区域热度对任务完成程度的影响，根据区域活跃用户数、用户平均停留时间及区域历史感知任务完成情况评估区域热度。其次，为了分析用户社交属性对任务完成程度的影响，结合用户的状态信息和用户历史感知任务记录计算用户意愿度、信誉度以及活跃度；综合考虑上述因素，分别为高热度和低热度区域设计了以最大化任务完成质量和最大化任务完成数量两种不同的社交属性感知的参与者选择机制。结果表明，所提机制能够显著提升总体数据质量，在低热度区域也可以及时可靠地完成感知任务。相比于SUR和GGA-I两种算法，失败率分别降低了66.7%和50.6%。

Abstract

Aiming at the problem of tasks that are low reliability acquired by platform and difficult to accomplish on time in user sparse area.A participant selection mechanism that combines regional heat and social attribute aware was proposed.Firstly

considering the influence of different regional heat on task completion

the regional heat was evaluated according to the number of active users

the average residence time of users and the completion of historical task.Secondly

in order to analyze the impact of user social attributes on task completion

the user willingness

reputation and activity were calculated by combining the status information of users and the historical task record of users.Finally

by taking the above factors into account

two different mechanisms of participant selection for social attribute perception were designed for high and low heat areas to maximize quality and number oftask completionrespectively.The results show that the proposed mechanism can significantly improve the overall data quality

and can also perform sensing tasks in sparse areas on time.Meanwhile

compared with SUR and GGA-I

the failure rate is reduced by 66.7% and 50.6% respectively.

关键词

Keywords

references

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