Sensor-Enhanced Mobility Prediction for Energy-Efficient Localization Energy-Efficient Localization

Energy efficiency & positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an en-ergy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localiza-tion system based on signal strength fingerprinting is designed, implemented, & evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to pre-dict the positional error of a real working positioning engine un-der different mobility levels of mobile targets, estimation error from the positioning engine, processing & networking delay in the location infrastructure, & sampling rate of location infor-mation. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing addi-tional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We imple-mented our energy-saving methods inside a working localization infrastructure & conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption.