Towards Sustainable Mobile Computing: Power Optimization Techniques and Evaluation

Abstract

Power optimization is a critical dimension of modern computing systems, encompassing strategies to reduce energy consumption while preserving performance, reliability, and user experience. This study investigates power optimization techniques in mobile operating systems within the broader framework of digital electronics and energy-efficient computing. The methodology integrates system monitoring, statistical analysis, and mathematical modeling of power consumption, with particular emphasis on the relationship between voltage, frequency, and dynamic power dissipation. Specifically, techniques such as Dynamic Voltage and Frequency Scaling (DVFS), power-aware scheduling, and resource allocation strategies are examined, alongside software-level mechanisms including Doze mode, App Standby, adaptive brightness, and network optimization. The study further evaluates the utility of profiling and benchmarking tools, namely Trepn Profiler, PowerTutor, and Battery Historian, in identifying energy inefficiencies and informing system performance improvements. The results demonstrate that integrating hardware and software-level optimizations yields significant reductions in power consumption. Notably, DVFS alone achieves substantial energy savings attributable to the quadratic dependence of dynamic power on supply voltage. Furthermore, intelligent scheduling and context-aware resource management enhance energy efficiency by dynamically adapting to workload variations and user behavioral patterns. These findings underscore the need to balance power efficiency with system responsiveness, particularly in latency-sensitive applications such as mobile banking. The study concludes that effective power optimization requires an integrated, data-driven approach that leverages mathematical modeling, statistical analysis, and adaptive control mechanisms to enable sustainable, efficient mobile computing.