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Oppgave.bbl
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% $ biblatex auxiliary file $
% $ biblatex bbl format version 2.7 $
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\refsection{0}
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\entry{NetworkX}{misc}{}
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\field{title}{NetworkX}
\field{year}{2015}
\verb{url}
\verb https://networkx.github.io/documentation/networkx-1.10/
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\field{note}{Online: accessed 26-September-2017}
\field{title}{WiFi Alliance News Release}
\field{year}{2016}
\verb{url}
\verb https://www.wi-fi.org/news-events/newsroom/wi-fi-device-shipments-to-surpass-15-billion-by-end-of-2016
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\field{booktitle}{Parallel Architectures, Algorithms, and Networks, 1999. (I-SPAN '99) Proceedings. Fourth InternationalSymposium on}
\field{issn}{1087-4089}
\field{title}{Distributed clustering for ad hoc networks}
\field{year}{1999}
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\verb{doi}
\verb 10.1109/ISPAN.1999.778957
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\keyw{computational complexity;parallel algorithms;ad hoc networks;distributed clustering;fully mobile network;hierarchical organization;mobile environment;network parameter;time complexity;Ad hoc networks;Clustering algorithms;Computer science;Electronic mail;Mobile computing;Network topology;Partitioning algorithms;Routing;Telecommunication network topology;Wireless networks}
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\field{journaltitle}{IEEE Journal on Selected Areas in Communications}
\field{month}{03}
\field{number}{3}
\field{title}{Performance analysis of the IEEE 802.11 distributed coordination function}
\field{volume}{18}
\field{year}{2000}
\field{pages}{535\bibrangedash 547}
\range{pages}{13}
\verb{doi}
\verb 10.1109/49.840210
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\keyw{IEEE standards;carrier sense multiple access;packet radio networks;standardisation;telecommunication standards;wireless LAN;CSMA/CA;DCF;IEEE 802.11 distributed coordination function;IEEE 802.11 protocol;MAC;analytical model;binary slotted exponential backoff;carrier sense multiple access;collision avoidance;ideal channel conditions;medium access control;packet transmission;performance analysis;standardization;terminals;threshold;throughput performance evaluation;wireless local area networks;Access protocols;Analytical models;Collision avoidance;Media Access Protocol;Multiaccess communication;Performance analysis;Physical layer;Throughput;Wireless LAN;Wireless application protocol}
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\field{journaltitle}{Commun. ACM}
\field{month}{04}
\field{number}{4}
\field{title}{New Methods to Color the Vertices of a Graph}
\field{volume}{22}
\field{year}{1979}
\field{pages}{251\bibrangedash 256}
\range{pages}{6}
\verb{doi}
\verb 10.1145/359094.359101
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\verb{url}
\verb http://doi.acm.org/10.1145/359094.359101
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\keyw{NP-complete,balancing,comparison of the methods,graph coloring,graph structure,scheduling}
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\field{journaltitle}{Cs.nyu.edu}
\field{note}{Online: accessed 14-Nov-2017}
\field{title}{Computing Distances}
\field{year}{1999}
\verb{url}
\verb https://cs.nyu.edu/visual/home/proj/tiger/gisfaq.html
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\field{title}{Practical optimization: a gentle introduction}
\field{year}{2006}
\verb{url}
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\field{note}{Accessed 16th Nov, 2017}
\field{title}{Radio Resource Management under Unified Wireless Networks}
\field{year}{2010}
\verb{url}
\verb https://www.cisco.com/c/en/us/support/docs/wireless-mobility/wireless-lan-wlan/71113-rrm-new.html
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\entry{CiscoVNI2017}{article}{}
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\field{howpublished}{White Paper}
\field{month}{06}
\field{title}{{Cisco Visual Networking Index: Forecast and Methodology, 2016--2021}}
\field{year}{2017}
\verb{url}
\verb https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html
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{Berlin, Heidelberg}%
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{Springer Berlin Heidelberg}%
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\field{abstract}{Many algorithms are available to learn deep hierarchies of features from unlabeled data, especially images. In many cases, these algorithms involve multi-layered networks of features (e.g., neural networks) that are sometimes tricky to train and tune and are difficult to scale up to many machines effectively. Recently, it has been found that K-means clustering can be used as a fast alternative training method. The main advantage of this approach is that it is very fast and easily implemented at large scale. On the other hand, employing this method in practice is not completely trivial: K-means has several limitations, and care must be taken to combine the right ingredients to get the system to work well. This chapter will summarize recent results and technical tricks that are needed to make effective use of K-means clustering for learning large-scale representations of images. We will also connect these results to other well-known algorithms to make clear when K-means can be most useful and convey intuitions about its behavior that are useful for debugging and engineering new systems.}
\field{booktitle}{Neural Networks: Tricks of the Trade: Second Edition}
\field{isbn}{978-3-642-35289-8}
\field{title}{Learning Feature Representations with K-Means}
\field{year}{2012}
\field{pages}{561\bibrangedash 580}
\range{pages}{20}
\verb{doi}
\verb 10.1007/978-3-642-35289-8_30
\endverb
\verb{url}
\verb https://doi.org/10.1007/978-3-642-35289-8_30
\endverb
\endentry
\entry{std80211}{article}{}
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family={Cole},
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given={Terry\bibnamedelima L.},
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{{hash=6f10b737c5c2de5e4ce66daa235caf11}{%
family={Barber},
family_i={B\bibinitperiod},
given={Simon.\bibnamedelimi eds.\bibnamedelimi (2007)},
given_i={S\bibinitperiod\bibinitdelim e\bibinitperiod\bibinitdelim (\bibinitperiod}}}%
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{Piscataway, NJ: Institute of Electrical}%
{Electronics Engineers}%
}
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\field{isbn}{0-7381-5656-6}
\field{journaltitle}{Local and Metropolitan Area Networks, Specific Requirements, IEEE Standard for Information technology— Telecommunications and information exchange between systems.}
\field{title}{Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications (IEEE Std 802.11-2007)}
\field{year}{2007}
\endentry
\entry{Denning}{article}{}
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\field{title}{In Search of an Understandable Consensus Algorithm}
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\field{pages}{305\bibrangedash 320}
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\field{title}{Characterization and Evaluation of Similarity Measures for Pairs of Clusterings}
\field{volume}{19}
\field{year}{2009}
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