Paper Info
Title
Exploiting Traffic Sampling Techniques to Optimize Energy Efficiency in Mobile Peer Networks
Abstract
Wireless infrastructures have seen a drastic increase in energy requirements as technology shifted towards higher frequencies in an attempt to increase bandwidth. Driven by the increase in demand from an always increasing subscriber base, large cities are also demanding more base stations and access points to guarantee adequate quality of service. Mobile peer networking is a possible solution for power-efficient communication, since transmission over short distances demands less power. A novel algorithm based on peer nodal hierarchies, traffic mapping and neural networks is proposed. Results show that this technique presents a remarkable power efficiency improvement over standard peer-to-peer networks.
Year
DOI
Venue
2009
10.1109/AP2PS.2009.20
AP2PS
Keywords
Field
DocType
optimize energy efficiency,neural network,drastic increase,subscriber base,mobile peer networks,access point,adequate quality,higher frequency,energy requirement,remarkable power efficiency improvement,base station,large city,exploiting traffic sampling techniques,energy efficiency,artificial neural networks,bandwidth,computer architecture,routing,neural nets,quality of service,sampling technique,power efficiency,mobile communication,neural networks,algorithm design and analysis,mobile computing,energy efficient
Mobile computing,Base station,Wireless,Computer science,Efficient energy use,Quality of service,Computer network,Bandwidth (signal processing),Energy consumption,Mobile telephony,Distributed computing
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Julian K. Buhagiar141.16
Carl James Debono23811.66