Network signal intensity detecting and fusing method

Abstract

The invention discloses a crowdsourcing and Bayesian consistency estimation based network signal intensity detecting and fusing method. The network signal intensity detecting and fusing method comprises the following steps of detecting network signal intensity of each place by utilizing a smart phone terminal and uploading data to a database through a cellular network or a wireless network, wherein acquired data packets comprise the following information: detected network types, detected time buckets T, detected positions P and data S about the wireless signal intensity; sieving and classifying the data based on a sorting criterion; fusing each type of the data respectively and obtaining the signal intensity of one network at one position and at one time bucket; and displaying and outputting fusion results so as to enable a user to obtain the signal intensity of one network at one position and at one time bucket, marking the signal intensity range of the network in a map by using different colors, and updating as well as displaying signal intensities at different time buckets.

Description

technical field [0001] The invention relates to wireless communication, in particular to a network signal strength detection and fusion method based on crowdsourcing and Bayesian consistency estimation. Background technique [0002] With the high development of information technology, people are increasingly dependent on the network. By detecting the signal strength of 2G, 3G, 4G and wireless networks in a certain location, an accurate signal map can be established to realize network switching, It provides a basis for selecting the optimal network model, and also provides important data support for network service operators. [0003] In recent years, the more common way to collect a large amount of information is to use wireless sensor networks, which collect information through a large number of randomly distributed nodes in the network, and return it to the area where it is located for analysis and processing by query users. Regarding the signal map, according to the retr...

AI Technical Summary

Problems solved by technology

[0007] The disadvantages of existing technologies are as follows: 1) In most cases, the energy and resources of sensor nodes are limited. When the scope of information collection is large, the number of sensors required will increase accordingly, which will increase the cost and energy consumption of sensors. It also determines that it is not feasible to use wireless sensor networks to collect signal strength and build signal maps; 2) As far as decision-making fusion is concerned, it is often difficult to deal with the lack of certain criteria and decision-making methods, and it is urgent to develop a new The consensus estimation algorithm combined with the traditional multi-sensor data fusion method for effective consensus estimation

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