During the one minute it takes to read this abstract, two billion smartphones worldwide will perform billions of Wifi channel scans recording the signal strength of nearby Wifi Access Points (APs). Yet despite this ongoing planetary-scale wireless network measurement, few systematic efforts are made today to recover this potentially valuable data. In this paper we ask the question: “Are the smartphone channel scans useful in monitoring enterprise Wifi networks?” More specifically, can these client-side measurements provide new insights compared to the AP-side measurements that enterprise Wifi networks already perform? Beginning with two Wifi scan datasets collected on two large scale smartphone testbeds, we conduct case studies that show how smartphone channel scans can be used to (1) improve AP spectrum management, and (2) predict the impact of AP failure or overload. In each case, a walk on the client side yields valuable insights for network operators that are otherwise impossible to gain from AP-side measurements, and together our results demonstrate the value of smartphone channel scans.

MOTIVATION: Network alignment (NA) aims to find regions of similarities between species' molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories. RESULTS: We introduce new measures of alignment quality that allow for fair comparison of the different LNA and GNA outputs, as such measures do not exist. We provide user-friendly software for efficient alignment evaluation that implements the new and existing measures. We evaluate prominent LNA and GNA methods on synthetic and real-world biological networks. We study the effect on alignment quality of using different interaction types and confidence levels. We find that the superiority of one NA category over the other is context-dependent. Further, when we contrast LNA and GNA in the application of learning novel protein functional knowledge, the two produce very different predictions, indicating their complementarity. Our results and software provide guidelines for future NA method development and evaluation. AVAILABILITY AND IMPLEMENTATION: Software: http://www.nd.edu/~cone/LNA_GNA CONTACT: : [email protected] information: Supplementary data are available at Bioinformatics online.

The incredible rise in popularity of mobile smart devices has placed tremendous pressure on wireless service providers. While much of the pressure arises from increasingly rich multimedia and social offerings, a sizable portion of the traffic originates when the user is not actively interacting with the device. The focus of this paper is to explore the prevalence and utility of smartphone background traffic through a pool of over one hundred campus smartphone users over a seven-week period from the Spring of 2013. Notably, our work shows that background traffic constitutes a non-trivial portion of wireless traffic ranging between one-third to two-fifths of traffic across the wireless interfaces. Our work breaks down background traffic with respect to diurnal behavior, wireless interface, mobile application, and latency until screen activation to further characterize the data.