Crowd sensing node selection method based on space-time credibility under large-range urban road network

Abstract

The invention provides a crowd sensing node selection method based on space-time credibility in a large-range urban road network environment. The service quality of a sensing task in the large-range urban road network environment is improved by optimizing selection of sensing nodes in limited resources. The method comprises the following steps: 1, constructing a large-range urban road network crowd sensing system; 2, calculating the space-time credibility of the vehicle; 3, calculating the coverage area of each node according to the track information and the space-time credibility of the vehicle nodes; 4, selecting a vehicle sensing node based on the time-space credibility. According to the node space-time credibility provided by the invention, the historical reputation and space-time characteristics of the vehicle nodes in the large-scale urban road network are comprehensively considered, the quality of the sensing nodes can be effectively improved, and a larger sensing coverage range and higher data accuracy can be obtained in limited road network resources.

Description

technical field [0001] The invention relates to the technical field of crowd sensing and intelligent transportation, in particular to a crowd sensing node selection method based on spatio-temporal reliability in a large-scale urban road network. Background technique [0002] Due to the advantages of easy maintenance, low deployment cost, and strong scalability, mobile crowd sensing has become one of the most widely used sensing methods. As for the perception under the large-scale urban road network, because of the different object-oriented and the unique nature of the perception task in the traffic system, there are obvious differences compared with the ordinary crowd sensing. [0003] Crowdsensing under the large-scale urban road network requires the sensing nodes to be vehicles and the sensing areas to be road sections. Compared with other mobile devices, vehicles have stronger mobility, can quickly reach the required location to collect data when completing sensing tasks...

AI Technical Summary

Problems solved by technology

[0005] However, in the existing technologies, most of them have not paid attention to the problem of sensory node selection in large-scale urban road networks. Some schemes for node selection in road traffic systems have been proposed, but there are still defects.

Some technologies consider the attributes of vehicles in the urban environment, but do not consider the characteristics of the road section and the topology of the road network

Moreover, most of the technologies have a relatively simple selection of sensing nodes, and basically only consider a certain factor of node location or node historical reputation, and do not pay attention to the spatio-temporal characteristics of vehicles in a large-scale road network in the urban environment.

Therefore, existing node selection methods are inapplicable and inaccurate for evaluating crowd sensing tasks in large-scale urban road networks

Images