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--- a/references.bib
+++ b/references.bib
@@ -2325,4 +2325,41 @@ ALGOL 68 is substantially different from ALGOL 60 and was not well received, so
 	year = {2016},
 	doi = {10.2172/1372902},
 	file = {Shehabi et al. - 2016 - United States Data Center Energy Usage Report.pdf:/home/loic/.zotero/zotero/383myqxk.default/zotero/storage/52D7SSUY/Shehabi et al. - 2016 - United States Data Center Energy Usage Report.pdf:application/pdf}
+}
+
+@article{maity_tcp_2017,
+	title = {{TCP} {Download} {Performance} in {Dense} {WiFi} {Scenarios}: {Analysis} and {Solution}},
+	volume = {16},
+	issn = {1536-1233},
+	shorttitle = {{TCP} {Download} {Performance} in {Dense} {WiFi} {Scenarios}},
+	url = {http://ieeexplore.ieee.org/document/7430293/},
+	doi = {10.1109/TMC.2016.2540632},
+	abstract = {How does a dense WiFi network perform, specifically for the common case of TCP download? While the empirical answer to this question is ‘poor’, analysis and experimentation in prior work has indicated that TCP clocks itself quite well, avoiding contentiondriven WiFi overload in dense settings. This paper focuses on measurements from a real-life use of WiFi in a dense scenario: a classroom where several students use the network to download quizzes and instruction material. We find that the TCP download performance is poor, contrary to that suggested by prior work. Through careful analysis, we explain the complex interaction of various phenomena which leads to this poor performance. Specifically, we observe that a small amount of upload traffic generated when downloading data upsets the TCP clocking, and increases contention on the channel. Further, contention losses lead to a vicious cycle of poor interaction with autorate adaptation and TCP’s timeout mechanism. To reduce channel contention and improve performance, we propose a modification to the AP scheduling policy to improve the performance of large TCP downloads. Our solution, WiFiRR, picks only a subset of clients to be served by the AP during any instant, and varies this set of “active” clients periodically in a round-robin fashion over all clients to ensure that no client starves. We have done extensive evaluation of WiFiRR in simulation and in real settings. By reducing the number of contending nodes at any point of time, WiFiRR improves the download time of large TCP flows upto 3:5Â of our classroom scenario. We also compare WiFiRR with state-of-the-art prior work WiFox, WiFiRR improves download time by 2:25Â over WiFox.},
+	language = {en},
+	number = {1},
+	urldate = {2019-05-27},
+	journal = {IEEE Transactions on Mobile Computing},
+	author = {Maity, Mukulika and Raman, Bhaskaran and Vutukuru, Mythili},
+	month = jan,
+	year = {2017},
+	pages = {213--227},
+	file = {Maity et al. - 2017 - TCP Download Performance in Dense WiFi Scenarios .pdf:/home/loic/.zotero/zotero/383myqxk.default/zotero/storage/XVNCPAZJ/Maity et al. - 2017 - TCP Download Performance in Dense WiFi Scenarios .pdf:application/pdf}
+}
+
+@article{jalali_fog_2016,
+	title = {Fog {Computing} {May} {Help} to {Save} {Energy} in {Cloud} {Computing}},
+	volume = {34},
+	issn = {0733-8716},
+	url = {http://ieeexplore.ieee.org/document/7439752/},
+	doi = {10.1109/JSAC.2016.2545559},
+	abstract = {Tiny computers located in end-user premises are becoming popular as local servers for Internet of Things (IoT) and Fog computing services. These highly distributed servers that can host and distribute content and applications in a peer-to-peer (P2P) fashion are known as nano data centers (nDCs). Despite the growing popularity of nano servers, their energy consumption is not well-investigated. To study energy consumption of nDCs, we propose and use flow-based and time-based energy consumption models for shared and unshared network equipment, respectively. To apply and validate these models, a set of measurements and experiments are performed to compare energy consumption of a service provided by nDCs and centralized data centers (DCs). A number of findings emerge from our study, including the factors in the system design that allow nDCs to consume less energy than its centralized counterpart. These include the type of access network attached to nano servers and nano server’s time utilization (the ratio of the idle time to active time). Additionally, the type of applications running on nDCs and factors such as number of downloads, number of updates, and amount of preloaded copies of data influence the energy cost. Our results reveal that number of hops between a user and content has little impact on the total energy consumption compared to the above-mentioned factors. We show that nano servers in Fog computing can complement centralized DCs to serve certain applications, mostly IoT applications for which the source of data is in end-user premises, and lead to energy saving if the applications (or a part of them) are off-loadable from centralized DCs and run on nDCs.},
+	language = {en},
+	number = {5},
+	urldate = {2019-05-28},
+	journal = {IEEE Journal on Selected Areas in Communications},
+	author = {Jalali, Fatemeh and Hinton, Kerry and Ayre, Robert and Alpcan, Tansu and Tucker, Rodney S.},
+	month = may,
+	year = {2016},
+	pages = {1728--1739},
+	file = {Jalali et al. - 2016 - Fog Computing May Help to Save Energy in Cloud Com.pdf:/home/loic/.zotero/zotero/383myqxk.default/zotero/storage/36J4R5W6/Jalali et al. - 2016 - Fog Computing May Help to Save Energy in Cloud Com.pdf:application/pdf}
 }
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