We present a measurement study of wireless experience in a diverse set of home environments by deploying an infrastructure, we call WiSe. Our infrastructure consists of OpenWrt-based Access Points (APs) that have been given away to residents for free to be installed as their primary wireless access mechanism. These APs are configured with our specialized measurement and monitoring software that communicates with our measurement controller through an open API. We have collected wireless performance traces from 30 homes for a period in excess of 6 months. To analyze the characteristics of these home wireless environments, we have also developed a simple metric that estimates the likely TCP throughput different clients can expect based on current channel and environmental conditions. With this infrastructure, we provide multiple quantitative observations, some of which are anecdotally understood in our community. For example, while a majority of links performed well most of the time, we observed cases of poor client experience about 2.1% of the total time.
We describe a large-scale and long-term measurement study of a popular mobile Massively Multiplayer Online Role Playing Game (MMORPG), called Parallel Kingdom, which has over 600,000 unique users distributed across more than 100 countries. Our study covers important aspects of the game including (i) characteristics of its population, (ii) players' game usage behavior, and (iii) correlation between players' interest and the money spent by them in the game. Our measurement study spans almost the entire life of the game staring from its inception on October 31, 2008 to November 10, 2011 (1104 days in total). To perform this study, we instrumented the game's client software (iOS and Android) to interact with our measurement server. The rich dataset gathered allowed us to analyze various characteristics of this highly popular mobile MMORPG.
We propose Snoopy, a system that can translate one's mobile phone or tablet into a low-cost, yet effective RF spectrum analyzer. Since typical spectrum analyzers are specialized hardware that is both expensive to acquire and cumbersome to carry around, they are rarely available for quick-and-easy spectrum sensing while on the go. To address this challenge, Snoopy augments popular mobile devices with a small attachable hardware unit (RF frequency translator) that can provide a reasonable view of the wireless spectrum across different frequency bands. It achieves this by leveraging the spectral scan functionality available in certain 802.11 NICs (e.g., the Atheros 9280 family of chipsets), which provides an unique lens towards the WiFi spectrum (2.4 GHz). Through the use of suitable frequency translators, such a view can be flexibly shifted to other spectrum bands. Although such a construction might not match the precision of the most sophisticated but expensive spectrum analyzers, we show that by leveraging some carefully designed spectral features, Snoopy can achieve decent accuracy in determining TV whitespaces (512 -698 MHz) -it can detect primary signals at up to -90dBm with an error rate of <15%, while achieving a median error of < 4dB in estimating the power of these signals. These promising results suggest that Snoopy is an intriguing option in bringing the ability of spectrum sensing to the masses, thereby truly enabling crowdsourcing options in this domain.
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