CARM: Crowd-Sensing Accurate Outdoor RSS Maps with Error-Prone Smartphone Measurements

• Published in: IEEE transactions on mobile computing Vol. 15; no. 11; pp. 2669 - 2681
• Main Authors: Xiang, Chaocan, Yang, Panlong, Tian, Chang, Zhang, Lan, Lin, Hao, Xiao, Fu, Zhang, Maotian, Liu, Yunhao
• Format: Magazine Article
• Language: English
• Published: Los Alamitos IEEE 01.11.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Buildings; Crowd-sensing; IEEE 802.11 Standard; Iterative methods; Measurement errors; Measurement uncertainty; Mobile computing; received signal strength; Sensors; signal propagation model; smartphone; Smartphones; Wireless sensor networks
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2015.2508814

Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with "inaccurate" and "incomplete" data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.