Cutting Wi-Fi Scan Tax for Smart Devices

Today most popular mobile apps and location-based services require near always-on Wi-Fi connectivity (e.g., Skype, Viber, Wi-Fi Finder). The Wi-Fi power drain resulting from frequent Wi-Fi active scans is undermining the battery performance of smart devices and causing users to remove apps or disable important services. We collectively call this the scan tax problem. The main reason for this problem is that the main processor has to be active during Wi-Fi active scans and hence consumes a significant and disproportionate amount of energy during scan periods. We propose a simple and effective architectural change, where the main processor periodically computes an SSID list and scan parameters (i.e. scan interval, timeout) taking into account user mobility and behavior (e.g. walking); allowing scan to be offloaded to the Wi-Fi radio. We design WiScan, a complete system to realize scan offloading, and implement our system on the Nexus 5. Both our prototype experiments and trace-driven emulations demonstrate that WiScan achieves 90%+ of the maximal connectivity (connectivity that the existing Wi-Fi scan mechanism could achieve with 5 seconds scan interval), while saving 50-62% energy for seeking connectivity (the ratio between the Wi-Fi connected duration and total time duration) compared to existing active scan implementations. We argue that our proposed shift not only significantly reduces the scan tax paid by users, but also ultimately leads to ultra-low power, always-on Wi-Fi connectivity enabling a new class of context-aware apps to emerge

Wi-Fi Offload: Tragedy of the Commons or Land of Milk and Honey?

Fueled by its recent success in provisioning on-site wireless Internet access, Wi-Fi is currently perceived as the best positioned technology for pervasive mobile macro network offloading. However, the broad transitions of multiple collocated operators towards this new paradigm may result in fierce competition for the common unlicensed spectrum at hand. In this light, our paper game-theoretically dissects market convergence scenarios by assessing the competition between providers in terms of network performance, capacity constraints, cost reductions, and revenue prospects. We will closely compare the prospects and strategic positioning of fixed line operators offering Wi-Fi services with respect to competing mobile network operators utilizing unlicensed spectrum. Our results highlight important dependencies upon inter-operator collaboration models, and more importantly, upon the ratio between backhaul and Wi-Fi access bit-rates. Furthermore, our investigation of medium- to long-term convergence scenarios indicates that a rethinking of control measures targeting the large-scale monetization of unlicensed spectrum may be required, as otherwise the used free bands may become subject to tragedy-of-commons type of problems.Comment: Workshop on Spectrum Sharing Strategies for Wireless Broadband Services, IEEE PIMRC'13, to appear 201

Traffic Offloading/Onloading in Multi-RAT Cellular Networks

We analyze next generation cellular networks, offering connectivity to mobile users through multiple radio access technologies (RATs), namely LTE and WiFi. We develop a framework based on the Markovian agent formalism, which can model several aspects of the system, including user traffic dynamics and radio resource allocation. In particular, through a mean-field solution, we show the ability of our framework to capture the system behavior in flash-crowd scenarios, i.e., when a burst of traffic requests takes place in some parts of the network service area. We consider a distributed strategy for the user RAT selection, which aims at ensuring high user throughput, and investigate its performance under different resource allocation scheme