Crowd sensing task allocation method based on double time limits

Abstract

The invention discloses a crowd sensing task distribution method based on double time limits. The method comprises the following steps that S1, a task publisher sends a sensing task with the time limits to a sensing service platform; S2, sensing participants upload personal information of themselves to a sensing service platform; S3, after the sensing service platform receives the sensing task setand the information of the participants, an immune genetic algorithm is operated to obtain the pairing condition of the participants and the tasks; s4, the sensing service platform sends the task information to the selected participants and provides a task completion sequence; s5, the selected participant executes the sensing task and sends a perception result to the perception service platform;and S6, the perception service platform integrates the received sensing results, returns the perception results to the task publisher, and pays for the participants. According to the method, under theconditions of budget constraint and dual-time limitation, the benefit obtained by the sensing platform is highest.

Description

technical field [0001] The invention relates to the field of group intelligence sensing, in particular to a method for assigning group intelligence sensing tasks based on dual time constraints. Background technique [0002] With the development of technology, mobile smart devices (mobile phones, tablet computers, wearable devices, etc.) have penetrated into people's lives, and crowd sensing, as a new mode of collecting data and providing information services, has gradually begun attract people's attention. Crowdsensing uses the mobile smart devices of ordinary users as the basic sensing unit, and realizes the distribution of sensing tasks and the collection of sensing data through interaction and collaboration with the Internet (server), and completes complex social sensing tasks that are difficult to complete alone . In short, CrowdSensing gives full play to the power of a large number of ordinary users, and forms a distributed, all-time, and closely connected perception ...

AI Technical Summary

Problems solved by technology

In this case, as the number of participants and tasks increases, the solution space of matching pairs will increase rapidly. The current optimization models and algorithms are also weak in this situation, which will reduce the completion efficiency of perception tasks.

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