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Cognitive Means Smart: Knowledge Saves Power

  • Dionysia TriantafyllopoulouEmail author
  • Klaus Moessner
  • Muhammad Alam
  • Ayman Radwan
  • Jonathan Rodriguez
Chapter
  • 1.4k Downloads
Part of the Signals and Communication Technology book series (SCT)

Abstract

The previous chapters mainly examined methods to save energy at the mobile handset, either by using short-range cooperation between mobile terminals, or by performing smart vertical handovers between heterogeneous radio access technologies. These techniques can be beneficial to mobile systems, but they have to be performed based on informed decisions; meaning that mobile devices need to be cognitive. Modern devices already collect significant amounts of information, but they have limited capability to exploit such context/information, and handover decisions are merely based on signal strength, or are network controlled and based on network load. In this chapter, we aim to go beyond the state-of-the-art by envisioning mobile terminals with the capability to make informed decisions based on a reservoir of context information made available through context providers; namely what is referred to as smart phones. We include a survey of the current state of the art for context extraction and management in context-aware systems; besides listing the current context extraction techniques and research efforts, we pinpoint the important properties of good context extraction techniques. Thereafter, we discuss how context information can be exploited in energy saving when performing network or node discovery mechanism, by instructing the mechanisms to scan for certain nodes/networks which are known to be in the vicinity. Finally, we discuss the range of context information that can be used to make informed decisions to save power.

Keywords

Medium Access Control Context Information Wireless Local Area Network Mobile Terminal Vertical Handover 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dionysia Triantafyllopoulou
    • 1
    Email author
  • Klaus Moessner
    • 1
  • Muhammad Alam
    • 2
  • Ayman Radwan
    • 2
  • Jonathan Rodriguez
    • 2
  1. 1.University of SurreyGuildfordUK
  2. 2.Instituto de TelecomunicaçõesCampus Universitário de SantiagoAveiroPortugal

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