scholarly journals Characterizing system level energy consumption in mobile computing platforms

2005 ◽  
Author(s):  
C.B. Margi ◽  
K. Obraczka ◽  
R. Manduchi
Keyword(s):  
Energy Consumption ◽  
Mobile Computing ◽  
System Level ◽  
Level Energy ◽  
Computing Platforms

scholarly journals Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 89
Author(s):  
Florian Grützmacher ◽  
Albert Hein ◽  
Thomas Kirste ◽  
Christian Haubelt
Keyword(s):  
Energy Consumption ◽  
Activity Recognition ◽  
Human Activity ◽  
Energy Efficient ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
System Level ◽  
Estimation Methods ◽  
Level Energy ◽  
Wearable Sensor

The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

scholarly journals Development of Packaging and Modular Control of Thermoelectric Clothes Dryer, With Performance Evaluation

2017 ◽  
Author(s):  
Viral K. Patel ◽  
Kyle R. Gluesenkamp
Keyword(s):  
Energy Consumption ◽  
Heat Pump ◽  
Minimum Energy ◽  
Experimental System ◽  
The United States ◽  
Primary Energy ◽  
System Level ◽  
Clothes Dryer ◽  
Thermoelectric Heat Pump ◽  
Thermoelectric Heat

This paper provides an overview of a thermoelectric heat pump clothes dryer which was developed with the aim of reducing the significant primary energy consumption attributed to residential electric clothes drying in the United States (623 TBtu/yr). The use of thermoelectric modules in place of the conventional electric resistance heater resulted in a 40% reduction in the energy consumption of the system, compared to the minimum energy efficiency standard. This was achieved for the first time for a standard test load of 8.45 lb, using a clothes dryer prototype with a thermoelectric heat pump module as the sole heating mechanism. The current experimental prototype was developed after extensive modeling, system design and control optimization, and experimental system-level evaluation of control parameters. The demonstration of improved energy consumption has laid the foundation for future development of this technology.

scholarly journals A Comparative Study of Methods for Measurement of Energy of Computing

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2204 ◽  
Author(s):  
Muhammad Fahad ◽  
Arsalan Shahid ◽  
Ravi Reddy Manumachu ◽  
Alexey Lastovetsky
Keyword(s):  
Predictive Models ◽  
Ground Truth ◽  
System Level ◽  
Average Error ◽  
Level Energy ◽  
Physical Measurements ◽  
Wide Range ◽  
External Power ◽  
On Chip ◽  
Multicore Cpus

Energy of computing is a serious environmental concern and mitigating it is an important technological challenge. Accurate measurement of energy consumption during an application execution is key to application-level energy minimization techniques. There are three popular approaches to providing it: (a) System-level physical measurements using external power meters; (b) Measurements using on-chip power sensors and (c) Energy predictive models. In this work, we present a comprehensive study comparing the accuracy of state-of-the-art on-chip power sensors and energy predictive models against system-level physical measurements using external power meters, which we consider to be the ground truth. We show that the average error of the dynamic energy profiles obtained using on-chip power sensors can be as high as 73% and the maximum reaches 300% for two scientific applications, matrix-matrix multiplication and 2D fast Fourier transform for a wide range of problem sizes. The applications are executed on three modern Intel multicore CPUs, two Nvidia GPUs and an Intel Xeon Phi accelerator. The average error of the energy predictive models employing performance monitoring counters (PMCs) as predictor variables can be as high as 32% and the maximum reaches 100% for a diverse set of seventeen benchmarks executed on two Intel multicore CPUs (one Haswell and the other Skylake). We also demonstrate that using inaccurate energy measurements provided by on-chip sensors for dynamic energy optimization can result in significant energy losses up to 84%. We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy. Finally, we present the lessons learned, our recommendations for the use of on-chip sensors and energy predictive models and future directions.

Knowledge Representation in Semantic Mobile Applications

2009 ◽  
pp. 796-804
Author(s):  
Panjak Kamthan
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Mobile Computing ◽  
Business Model ◽  
Mobile Applications ◽  
Service Providers ◽  
Information Architecture ◽  
Future Research ◽  
Information Diversity ◽  
Computing Platforms

Mobile applications today face the challenges of increasing information, diversity of users and user contexts, and ever-increasing variations in mobile computing platforms. They need to continue being a successful business model for service providers and useful to their user community in the light of these challenges. An appropriate representation of information is crucial for the agility, sustainability, and maintainability of the information architecture of mobile applications. This article discusses the potential of the Semantic Web (Hendler, Lassila, & Berners- Lee, 2001) framework to that regard. The organization of the article is as follows. We first outline the background necessary for the discussion that follows and state our position. This is followed by the introduction of a knowledge representation framework for integrating Semantic Web and mobile applications, and we deal with both social prospects and technical concerns. Next, challenges and directions for future research are outlined. Finally, concluding remarks are given.

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