Regression Based Mobility Estimation Method Using Received Signal Strength
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In this paper, estimation of mobility using received signal strength (RSS) is presented. In contrast to standard methods, speed can be estimated without the use of any additional hardware like accelerometer, gyroscope or position estimator. The pattern of mobility can be inferred using any hand-held device such as mobile phone, tablet or smart watch and source of RSS. The strength of Wireless Fidelity (WiFi) signal is considered herein to compute the time-domain features such as mean, minimum, maximum, and autocorrelation of signal strength. The experiments are carried out in different environments like academic area, residential area and in open space. The experimental results indicate that the average accuracy in the estimated speed is 88% using the maximum RSS model with limited data set. The accuracy can be further increased by carrying out the training with a larger dataset. The proposed method is cost-effective and having linear complexity with reasonable accuracy. Additionally, the proposed method is scalable that is the performance is not affected in a multi-smartphones scenario in a WiFi or cellular environment.
KeywordsMobility Received signal strength WiFi signal
The authors would like to thank Prof. Niki Trigoni and her research group, Oxford University, United Kingdom for granting the permission to use SensorApp for the collection of WiFi samples. A part of the work was carried out at Visvesvaraya National Institute of Technology Nagpur, India when author S. Kumar was associated with that institute.
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