While crowdsourcing is an attractive approach to collect large-scale wireless measurements, understanding the quality and variance of the resulting data is difficult. Our work analyzes the quality of crowdsourced cellular signal measurements in the context of basestation localization, using large international public datasets (419M signal measurements and 1M cells) and corresponding ground truth values. Performing localization using raw received signal strength (RSS) data produces poor results and very high variance. Applying supervised learning improves results moderately, but variance remains high. Instead, we propose feature clustering, a novel application of unsupervised learning to detect hidden correlation between measurement instances, their features, and localization accuracy. Our results identify RSS standard deviation and RSS-weighted dispersion mean as key features that correlate with highly predictive measurement samples for both sparse and dense measurements respectively. Finally, we show how optimizing crowdsourcing measurements for these two features dramatically improves localization accuracy and reduces variance.

Network transmissions are the cornerstone of most mobile apps today, and a main contributor to energy consumption. We use a componentized energy model to quantify energy use by device, and observe significant energy consumption by the CPU in network operations. We assert that optimizing network operations in the CPU can produce significant energy savings, and explore the impact of two potential approaches: one-copy data moves and offloading the network stack to the basestation.

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based Sybil devices that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on co-location edges, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large proximity graphs that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.

We describe our efforts to empirically validate a distributed spectrum monitoring system built on commodity smartphones and embedded low-cost spectrum sensors. This system enables real-time spectrum sensing, identifies and locates active transmitters, and generates alarm events when detecting anomalous transmitters. To evaluate the feasibility of such a platform, we perform detailed experiments using a prototype hardware platform using smartphones and RTL dongles. We identify multiple sources of error in the sensing results and the end-user overhead (i.e. smartphone energy draw). We propose and implement a variety of techniques to identify and overcome errors and uncertainty in the data, and to reduce energy consumption. Our work demonstrates the basic viability of user-driven spectrum monitoring on commodity devices.

Most of today's mobile devices come equipped with both cellular LTE and WiFi wireless radios, making radio bundling (simultaneous data transfers over multiple interfaces) both appealing and practical. Despite recent studies documenting the benefits of radio bundling with MPTCP, many fundamental questions remain about potential gains from radio bundling, or the relationship between performance and energy consumption in these scenarios. In this study, we seek to answer these questions using extensive measurements to empirically characterize both energy and performance for radio bundling approaches. In doing so, we quantify potential gains of bundling using MPTCP versus an ideal protocol. We study the links between traffic partitioning and bundling performance, and use a novel componentized energy model to quantify the energy consumed by CPUs (and radios) during traffic management. Our results show that MPTCP achieves only a fraction of the total performance gain possible, and that its energy-agnostic design leads to considerable power consumption by the CPU. We conclude that not only there is room for improved bundling performance, but an energy-aware bundling protocol is likely to achieve a much better tradeoff between performance and power consumption.

Social interactions and interpersonal communication has undergone significant changes in recent years. Increasing awareness of privacy issues and events such as the Snowden disclosures have led to the rapid growth of a new generation of anonymous social networks and messaging applications. By removing traditional concepts of strong identities and social links, these services encourage communication between strangers, and allow users to express themselves without fear of bullying or retaliation.

The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today's cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to characterize in detail traffic hotspots in today's cellular networks using real data.