Wi-Fi networks were one of the first use-cases for Software-defined networking (SDN). However, to deploy a software-defined Wi-Fi network today, one has to rely on research prototypes with availability, documentation, hardware requirements, and scalability issues. To alleviate this situation, we demonstrate two simple techniques to bring SDN functionality to existing Wi-Fi networks and discuss their benefits and short-comings. Researchers can use our techniques to convert their existing Wi-Fi testbeds into software defined Wi-Fi testbeds. Our two techniques thus significantly lower the barrier-to-entry for deploying software-defined Wi-Fi networks.

Wireless data transmission consumes a significant part of the overall energy consumption of smartphones, due to the popularity of Internet applications. In this paper, we investigate the energy consumption characteristics of data transmission over Wi-Fi, focusing on the effect of Internet flow characteristics and network environment. We present deterministic models that describe the energy consumption of Wi-Fi data transmission with traffic burstiness, network performance metrics like throughput and retransmission rate, and parameters of the power saving mechanisms in use. Our models are practical because their inputs are easily available on mobile platforms without modifying low-level software or hardware components. We demonstrate the practice of model-based energy profiling on Maemo, Symbian, and Android phones, and evaluate the accuracy with physical power measurement of applications including file transfer, web browsing, video streaming, and instant messaging. Our experimental results show that our models are of adequate accuracy for energy profiling and are easy to apply.

Spaceify is a novel edge architecture and an ecosystem for smart spaces --- a technology that extends the mobile user view of today's common space services (e.g., WiFi) to a richer portfolio of space-centric, localized services and space-interactive applications.

Energy consumption of wireless data transmission, a significant part of the overall energy consumption on a mobile device, is context-dependent—it depends on both internal and external contexts, such as application workload and wireless signal strength. In this paper, we propose an event-driven framework that can be used for efficient power management on mobile devices. The framework adapts the behavior of a device component or an application to the changes in contexts, defined as events, according to developer-specified event-condition-action (ECA) rules that describe the power management mechanism. In contrast to previous work, our framework supports complex event processing. By correlating events, complex event processing helps to discover complex events that are relevant to power consumption. Using our framework developers can implement and configure power management applications by editing event specifications and ECA rules through XML-based interfaces. We evaluate this framework with two applications in which the data transmission is adapted to traffic patterns and wireless link quality. These applications can save roughly 12 percent more energy compared to normal operation.

The fundamental P2P principle that downloading peers help other peers can be applied in the context of video-on-demand. This represents a demanding application combining aspects of other well-known P2P applications, i.e., live streaming and traditional file sharing. We seek to provide insight on fundamental questions about the performance and scalability of the system. A deterministic fluid model is derived that explicitly takes into account the video transfer and playback phases. The analytical results are complemented with extensive simulations from the corresponding stochastic model, as well as traces from a more realistic BitTorrent simulator.

In this paper we present findings from our windowing BitTorrent simulations and show that by carefully optimizing other factors a reasonable level of performance can be achieved while leaving the original BitTorrent tit-for-tat mechanism intact. We compare the previously proposed windowing piece selection algorithms for BitTorrent and propose a new one, called the stretching window algorithm. We also propose a new method for reducing buffering times, adjusting the window size as the download progresses, and we show its effectiveness. We also observe that windowing BitTorrent algorithms exhibit steady state behavior, and that even a small level of altruism leads to significantly improved system performance.