Power consumption and bandwidth savings with video transcoding to mobile device-specific spatial resolution

Author(s):  
Leon Dragic ◽  
Daniel Hofman ◽  
Mario Kovac ◽  
Martin Zagar ◽  
Josip Knezovic
Author(s):  
João Nuno Silva ◽  
Luís Veiga

This book chapter presents the integration of widely available technologies to bridge the gap between mobile devices and their computational rich surrounding environments. Taking as common glue Cloud Storage systems, new interaction between devices becomes more natural. The processing of files can be transparently executed on nearby computers, taking advantage of better hardware and saving mobile devices power. In this chapter, the authors present a novel resource evaluation mechanism, which allows a finer evaluation and more precise comparison of remote resources, leading to fewer wasted resources and better use of those resources. The use of remote resources can be performed by means of processing offloading, executing complete application on remote devices or by relocation of mobile classes. Both methods resort to the presented resource evaluation mechanism. Monolithic applications are transformed (with information from a configuration file) into distributed application, where some components execute on remote devices: nearby computers (to take advantage of existing human-computer interaction devices) or on the cloud (to speed processing). Processing offloading is accomplished by executing on nearby computers applications compatible with the one on the mobile device. This speeds that processing task (better CPU, better interaction devices), reducing the mobile device’s power consumption.


2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Jin-Hee Lee ◽  
Yeong-Ju Lee ◽  
Minseok Song ◽  
Byeong-Seok Shin

It is important to recognize the motion of the user and the surrounding environment with multiple sensors. We developed a guidance system based on mobile device for visually impaired person that helps the user to walk safely to the destination in the previous study. However, a mobile device having multiple sensors spends more power when the sensors are activated simultaneously and continuously. We propose a method for reducing the power consumption of a mobile device by considering the motion context of the user. We analyze and classify the user’s motion accurately by means of a decision tree and HMM (Hidden Markov Model) that exploit the data from a triaxial accelerometer sensor and a tilt sensor. We can reduce battery power consumption by controlling the number of active ultrasonic sensors and the frame rate of the camera used to acquire spatial context around the user. This helps us to extend the operating time of the device and reduce the weight of the device’s built-in battery.


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