An Architectural Framework for Energy- and Network-Efficient Mobile Tracking via Adaptive Sampling and Motion-State Filtering

Mobile Global Positioning System (GPS) tracking plays a critical role in navigation, logistics, personal security, and asset monitoring applications. However, continuous GPS polling on mobile devices leads to excessive battery consumption and increased network communication overhead. This paper presents the architectural design and prototype implementation of an adaptive mobile tracking framework developed for the Android platform. The proposed approach integrates motion-state detection using accelerometer-based Signal Vector Magnitude (SVM), velocity-adaptive sampling intervals, battery-aware modulation, and spatiotemporal filtering for GNSS data validation. The system is formulated as a multi-objective control framework balancing positioning accuracy, energy consumption, and network utilization. A controlled prototype implementation validates the functional feasibility and subsystem integration of the proposed optimization mechanisms within a real mobile environment. The work establishes a practical foundation for energy-aware and network-efficient mobile tracking systems, with comprehensive quantitative benchmarking reserved for future large-scale evaluation.

Computational Sustainability, Adaptive Sampling, Multi-Objective Optimization

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