rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Lightweight indoor localisation and linguistic location authority
  name:
    name part: Olowolayemo, Akeem
    role:
      role term: creator
  name:
    name part: International Islamic University Malaysia
    name part: Kulliyyah of Information and Communication Technology
  genre: Theses, IIUM local
  origin info:
    place:
      place term: my
    place:
      place term: Kuala Lumpur
    publisher: Kulliyyah of Information and Communication Technology, International Islamic University Malaysia
    date issued: 2015
  language:
    language term: eng
  physical description:
    extent: xvii, 202 leaves : ill. ; 30cm.
  abstract: Indoor positioning and navigation unlike outdoor positioning requires different techniques apart from the classical geometric based approached utilizing satellite communications. This is due to the fact that satellite signal reception is poor in indoor environment. Approaches to indoor localization using Received Signal Strengths (RSS) are generally based on signal propagation models or location fingerprinting methods, using different algorithms. All algorithms whether applied on signal propagation models or location fingerprinting can be classified as heavyweight or lightweight algorithms. Heavyweight algorithms generally have better accuracies but require rigorous and complex computations thereby place critical strain on processing power of mobile devices and suffer from location response delay due to the complexity of the computation and extended time requirement. Lightweight algorithms are less complex and do not require extensive time or processing power compare to the heavyweight algorithms, however they perform relatively poorer in accuracy. Lightweight algorithms have been investigated in this thesis for near heavyweight accuracy and sufficiently accurate for indoor environments. The two novel algorithms proposed achieve 95% room level accuracy and a maximum update time of 2 seconds reducing update time considerably. The first one is Fuzzy Weighted Aggregation of Received Signal Strengths of Wi-Fi signals with Compensated Weighted Attenuation Factor (CWAF) in the form of fuzzy weighted signal quality and noise while the second is lightweight localization approach based on the extreme learning algorithm (ELM), a single hidden layer neural network. For every location based system requires the representation of the location in effective and efficient scheme. In order to provide suitable location authority for indoor positioning approaches proposed, this work introduced a perception-based linguistic approach to locations relative to landmarks to extend present l…
  note: by Akeem Olowolayemo
  note: Thesis (Ph.D)--International Islamic University Malaysia, 2015.
  note: Includes bibliographical references (leaves 165-181).
  subject:
    geographic code: a-my---
  classification: QA76.59
  identifier: http://studentrepo.iium.edu.my/handle/123456789/5461
  record info:
    record content source: UIAM
    record creation date: 150909
    language of cataloging:
      language term: eng