IoT mobile phone auxiliary positioning method based on real-time power consumption distance network weight

Abstract

The invention relates to wireless communication, positioning and Internet technologies, provides a mobile phone auxiliary positioning method and improves the positioning precision of Internet-of-Things equipment, and provides an IoT mobile phone auxiliary positioning method based on real-time power consumption distance network weight. A smart phone is used as a mobile anchor node with a self-tracking capability, a server is used for realizing an offline positioning algorithm and layout information of an indoor environment, the smart phone provides the layout of a target position in a digital map form, has a self-tracking function, and tracks the position of the target position at a home gate through the motion sensor; and the smart phone and the Internet-of-Things equipment communicate with each other in a short distance through wireless communication transmission power control, when the smart phone broadcasts beacon information for positioning, each Internet-of-Things equipment replies information, and the reply information comprises an identifier, a supplier and a model. The method is mainly applied to the positioning occasion of the Internet-of-Things equipment.

Description

technical field [0001] The invention relates to the technical fields of wireless communication, positioning and the Internet, in particular to an IoT mobile phone assisted positioning method based on real-time power consumption distance network weights. Background technique [0002] In recent years, more and more Internet of Things (Internet of Things, IoT) devices, such as mobile smart devices, electrical appliances and sensors, can be accommodated in smart homes. In smart homes, users want to remotely control and monitor IoT devices such as smart TVs, air conditioners, lights, temperature controllers, door locks, rice cookers, refrigerators, and robot cleaners. Smartphones have many motion sensors for indoor positioning, such as accelerometers, gyroscopes, and magnetometers. Sensor fusion of these sensors allows smartphones to keep track of their movements indoors such as homes and offices. Researchers generally pay attention to how to use the positioning ability of smar...

AI Technical Summary

Problems solved by technology

[0003] Now there are many indoor positioning schemes about WiFi, such as using Gaussian process regression to train the wireless signal strength (Received Signal Strength, RSS) data set, and using the particle filter to estimate the location of the smartphone, which is constructed in the offline training phase Discrete location fingerprints, calculate the estimated value of each location according to Bayesian decision rules and observed signal strength; a distributed RSS-based indoor IoT device positioning scheme, using a dynamic circle expansion mechanism, which can be flexibly expanded based on three The coverage of the positioning circle of the reference node confidence until the circles of the reference nodes intersect each other; an indoor positioning scheme based on the Global System for Mobile Communication (GSM), using 6 strongest signal units and Wide signal strength fingerprints composed of more available units are more stable and reliable than WiFi fingerprint-based schemes, but this scheme cannot be used in rural areas where there are fewer mobile phone base stations; an indoor positioning scheme that does not require pre-deployment, such as through The RSS measurement of WiFi access points constructs a fingerprint map, and uses the constraints of physical wireless propagation and genetic algorithms to locate it, but in the residential environment with a small number of access points, this scheme cannot provide reasonable indoor positioning; a An indoor positioning solution based on smartphones, which is based on dead reckoning using the motion sensors of smartphones (such as accelerometers and gyroscopes), and the sensing data of accelerometers is used for step calculation, that is, to measure the number of steps taken by smartphone users, but Since the motion error is accumulated over time, the scheme uses calibration markers to correct the accumulated motion error; an enhanced indoor localization scheme based on social perception, in order to collaboratively calibrate the estimation error in localization, a mobile smartphone is used to perform self- Encounter and non-encounter events tracked, designed to locate mobile smartphone users collaboratively with multiple smartphones, rather than static IoT devices with a single smartphone

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