Task distribution method based on social attributes and effective user calculation

Abstract

The invention discloses a task distribution method based on social attributes and effective user calculation. The method comprises the following steps: step (1), acquiring a new user through a socialnetwork of an old user; step (2), receiving a task content of a task requester by an MCS (mobilecrowdsensing) system and generating a task list; step (3), acquiring a geographic location of the user by the MCS system, and adding eligible users to the task list according to the geographic location of the user; and step (4), selectively pushing tasks to a user who meets a ranking condition based onthe effective credit rankings of the users within the task list by the MCS system. According to the task distribution method based on the social attributes and the effective user calculation providedby the invention, user social elements are added, the task distribution channel is expanded, and the number of users participating in the task is improved; and besides, a credit mechanism reflecting the effective user status is proposed, which serves as the basis for screening users when performing the next task, and provides guarantee for the finishing quality of the tasks.

Description

technical field [0001] The present invention relates to the field of crowdsourcing task distribution, and more specifically relates to a task distribution method based on social attributes and effective user calculations. Background technique [0002] At present, with the rapid development of wireless sensor networks and the popularization of various mobile smart devices, mobile crowd sensing (MCS) has become the core of mobile computing. Large-scale, complex environment and social perception tasks can be completed by using crowd sensing, and crowdsourcing task distribution is an important part of this application. Among the common crowd sensing task distribution methods, the most representative one is the Bubble-sensing mode, which registers task information on the relevant server, broadcasts task information to users who move to the server area, and performs data processing after receiving the task. collection. In order to maintain the presence of task communicators, the...

AI Technical Summary

Problems solved by technology

[0003] First of all, the Bubble-sensing mode relies too much on the encounter opportunities between users to distribute tasks. Due to the uncertainty of users' social characteristics and their moving trajectories, users cannot obtain task information in time, which reduces the speed of task distribution.

Secondly, in the Bubble-sensing mode, forwarding tasks between users is blind, resulting in a relatively high rejection rate of tasks

Thirdly, the Bubble-sensing mode does not distinguish willing users when distributing tasks, and does not respect users' wishes to force push tasks, causing malicious users to submit low-quality data, resulting in pollution of the final analysis results

Finally, the Bubble-sensing mode does not evaluate the user's task completion status, and cannot rank users according to the user's task completion performance, so that tasks cannot be pushed to users with better performance, resulting in the failure to improve the quality of task completion

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