A Task Allocation Method for Radio Environment Map Data Collection

Abstract

The invention discloses a task distribution method for radio environment map data collection. The radio environment map data collection is carried out through utilization of a mobile crowd sensing technology; and two-phase task distribution algorithms are employed according to information attributes registered in a system by users in a mobile crowd sensing network, namely data types capable of being sensed by the users and quotations of the users for various sensed data types. According to the two-phase task distribution algorithms, a high-dimensional radio environment map data collection task is divided into a plurality of low-dimensional subtasks based on a k-means clustering thought; and the subtasks are distributed to the right number of appropriate sensing users through adoption of an LCBPA algorithm. An experimental result shows that the method has the beneficial effects that through coordinative sensing among the users in the mobile crowd sensing network, the radio environment map data collection difficulty and cost are reduced, a task finishing rate is improved, the total data collection cost is minimized, and the user participating balance is improved.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a task allocation method for radio environment map data collection. Background technique [0002] Radio Environment Map (REM, Radio Environment Map) is an integrated spatio-temporal database describing the radio working environment, which can provide comprehensive information in multiple fields of cognitive radio, based on which further details of the radio environment can be measured and built. It can be extended to a variety of upper-level applications, so it has great application potential and good development prospects. [0003] However, there are great limitations in the construction of REM at present, and the corresponding collection equipment is usually deployed in a designated area according to a specific application to obtain the required information. In this form, every time a new application is introduced, a dedicated database must be rebuilt, and a large amount ...

AI Technical Summary

Problems solved by technology

[0006] In the actual face of REM data collection tasks, the perception function of ordinary equipment is usually not enough to support such high-dimensional data collection tasks, and the deployment of a large number of professional perception equipment requires high overhead, making the construction of REM more difficult and costly. high

On the other hand, the existing mobile crowd-sensing task allocation methods do not consider the balance of participation of user nodes. When performing task allocation, more tasks are often imposed on some low-cost and powerful user nodes, which not only increases the The burden of such nodes also makes it difficult for other user nodes to obtain opportunities to participate in sensing tasks, which is not conducive to maintaining long-term sensing applications

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