Accurate online power estimation and automatic battery behavior based power model generation for smartphones

By Lide Zhang, B. Tiwana, Zhiyun Qian, Zhaoguang Wang, R. Dick, Z. Morley Mao, Lei Yang. International Conference on Hardware/Software Codesign and System Synthesis, 2010

1,256 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4ffcd50592eee39748d864b00b14a7c138e50a9a

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In Automotive Engineering

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Citations in the corpus

Serendipity: enabling remote computing among intermittently connected mobile devices (2012, methodology): “A node s device pro.le includes its execution speed which is estimated by running synthetic benchmarks and its energy consumption model using techniques like PowerBooter [34].”
AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring (2012, methodology): “Usage estimation for hardware components has been previously completed using hardware performance counter (HPC) [5, 10-13], software performance counter (SPC) such as the Linux procfs/sysfs [7, 14-16], or BatteryStats, which is provided by Android [8, 17].”
To offload or not to offload? The bandwidth and energy costs of mobile cloud computing (2013, methodology): “framework yields energy consumption estimation values very close to those of the Mobile Device Power Monitor1, a tool used in other works in the area [3], [16].”
Profiling resource usage for mobile applications: a cross-layer approach (2011, background): “* Reported by [31] on an Android HTC Dream phone”
Quantifying the energy cost of data movement for emerging smart phone workloads on mobile platforms (2014, background): “[18] leveraged the battery sensor on a smart phone to construct a model for power estimation.”
Identifying diverse usage behaviors of smartphone apps (2011, background): “Also related are studies that proposed measurement tools fo r smartphone devices characterizing either the device perfo rmance or the performance of certain apps [14, 30, 21], e.”
A close examination of performance and power characteristics of 4G LTE networks (2012, methodology): “ARO [28] employs an approach similar to [35] but focusing only on radio power usage.”
A variegated look at 5G in the wild: performance, power, and QoE implications (2021, background): “Prior studies have built power models for 3G [47, 48, 55, 65] and 4G/LTE [26, 31, 42].”
Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings (2013, background): “Prior works on WiFi and cellular radio power measurements [16, 19, 51] indicate that WiFi sleep power is about 2x the sleep power of cellular technologies such as 3G, the data transmission in WiFi is about an order of magnitude more efficient than cellular, the energy spent by WiFi radio to scan and”
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance (2020, methodology): “When the CPU is selected as the action, Renergy is calculated using the utilization-based CPU power model [46, 95] as in (1),”
ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading (2012, methodology): “We take inspiration from the recent PowerTutor [19] model which accounts for power consumption of CPU, LCD screen, GPS, WiFi, 3G, and audio interfaces on HTC Dream and HTC Magic phones.”