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This paper aims to develop generic strategies for improving energy consumption for location sensing on smartphones and compares the results of iOS and Android implementations. Mobile smartphone applications utilizing localization sensors (e.g. Global Positioning System) collectively face the problem of battery draining. Energy consumption is at a peak when applications permanently and stolidly use those sensors, even if their excessive exploitation is avoidable (e.g. when the user carrying the device is not moving).

Considering contextual parameters affecting localization of mobile devices (i.e. incorporating movement probability, speed, etc.) is the basic idea for developing a strategy capable of reducing energy consumption for location determination on mobile devices. This paper explains the paradigm and draws the architecture for a generic context-based energy saving strategy for mobile location-based services.

The paper reveals the positive implications in terms of energy consumption measured in the course of exhaustive tests for iOS and Android devices and discusses accuracy issues and potential workarounds, especially focusing on Apple’s M7 motion co-processor for consuming accelerometer data on a low energy level.

The paper identifies and measures energy issues for location determination on smartphones and presents a generic and heuristic concept for saving energy.

The aim of this paper is to present an architecture and prototypical implementation of a context‐sensitive software system which combines the tangible user interface approach with a mobile augmented reality (AR) application.

The work which is described within this paper is based on a creational approach, which means that a prototypical implementation is used to gather further research results. The prototypical approach allows performing ongoing tests concerning the accuracy and different context‐sensitive threshold functions.

Within this paper, the implementation and practical use of tangible user interfaces for outdoor selection of geographical objects is reported and discussed in detail.

Further research is necessary within the area of context‐sensitive dynamically changing threshold functions, which would allow improving the accuracy of the selected tangible user interface approach.

The practical implication of using tangible user interfaces within outdoor applications should improve the usability of AR applications.

Despite the fact that there exist a multitude of research results within the area of gesture recognition and AR applications, this research work focuses on the pointing gesture to select outdoor geographical objects.