An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context

Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy

Nonparametric Regression-based Step-length Estimation for Arm-swing Walking using a Smartphone

In this paper, we propose an adaptive step-estimation method to estimate the distance traveled for arm-swinging activities at three level-walking speeds, i.e., low, normal, and high speed. The proposed method is constructed based on a polynomial function of the pedestrian speed and variance of walking acceleration. We firstly apply a low-pass filter with 10 Hz cut-off frequency for acceleration data. Then, we analyze the acceleration data to find the number of steps in each sample. Finally, the traveled distance is calculated by summing all step lengths which are estimated by the proposed method during walking. Applying the proposed method, we can estimate the walking distance with an accuracy rate of 95.35% in a normal walking speed. The accuracy rates of low and high walking speeds are 94.63% and 94.97%, respectively. Furthermore, the proposed method outperforms conventional methods in terms of accuracy and standard deviation at low, normal, and high speeds

Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone

The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy