such as smartphones with multiple wireless interfaces. However, the high energy consumption of Wi-Fi interfaces, especially when a device is idle, presents a significant barrier to the widespread adoption of VoIP over Wi-Fi. To address this issue, we present Cell2Notify, a practical and deployable energy management

such as smartphones with multiple wire-less interfaces. However, the high energy consumption of Wi-Fi interfaces, especially when a device is idle, presents a significant barrier to the widespread adoption of VoIP over Wi-Fi. To address this issue, we present Cell2Notify, a practical and deployable en-ergy management

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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances — including the key problems of computing marginals and modes of probability distributions — are best studied in the general setting. Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, we develop general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sum-product, cluster variational methods, expectation-propagation, mean field methods, max-product and linear programming relaxation, as well as conic programming relaxations — can all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.

W). Applications like VoIP do not perform well under PSM mode however, due to their real-time nature, so the energy footprint is quite high. The challenge is to save energy while not affecting performance. In this paper we present SiFi: Silence prediction based WiFi energy adaptation. SiFi examines audio streams

Abstract- Wireless data transmission consumes a significant part of the overall energy consumption of smartphones, due to the popularity of Internet applications. The proposed method investigate the energy consumption characteristics of data transmission over Wi-Fi, focusing on user characteristics

that are extremely sensitive to delay or loss (e.g., VoIP), Wiffler quickly switches to 3G if WiFi is unable to successfully transmit the packet within a small time window. We implement and deploy Wiffler in our vehicular testbed. Our experiments show that Wiffler significantly reduces 3G usage. For a realistic

The demand for an all-in-one phone with integrated personal information management and data access capabilities is beginning to accelerate. While personal digital assistants (PDAs) with built-in cellular, WiFi, and Voice-Over-IP technologies have the ability to serve these needs in a single package

WiFi continues to be a prime source of energy consumption in mobile devices. This paper observes that, despite a rich body of research in WiFi energy management, there is room for improvement. Our key finding is that WiFi energy optimizations have conventionally been designed with a single AP

WiFi radios in smart-phones consume a significant amount of power when active. The 802.11 standard allows these devices to save power through an energy-conserving Power Save Mode (PSM). However, depending on the PSM implementation strategies used by the clients/Access Points (APs), we find