Abstract: Mobile computing has morphed into a principal form of human communication, business, and social interaction. Unfortunately, the energy demands of newer ambient intelligence and collaborative technologies on mobile devices have greatly overwhelmed modern energy storage abilities. This paper proposes several novel techniques that exploit spatiotemporal and device context to predict device wireless data and location interface configurations that can optimize energy consumption in mobile devices. These techniques, which include variants of linear discriminate analysis, linear logistic regression, non-linear logistic regression with neural networks, k-nearest neighbor, and support vector machines are explored and compared on synthetic and user traces from real-world usage studies. The experimental results show that up to 90% success s full prediction is possible with neural networks and k-nearest neighbor algorithms, improving upon prediction strategies in prior work by approximately 50%. Further, an average improvement of 24% energy savings is achieved compared to state-of-the -art prior work on energy-efficient location sensing.
Keywords: Mobile, PDA’s, Sensor, 3D Gaming, Wi-Fi, GPS
Title: MINIMIZING OVERHEAD AND IMPROVING ENERGY FOR SMART MOBILE DEVICES
Author: N.Priyanka, Dr.R.Indra Gandhi
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
Research Publish Journals
