Modern cell phones are required to receive and display alerts via the Wireless Emergency Alert (WEA) program, under the mandate of the Warning, Alert, and Response Act of 2006. These alerts include AMBER alerts, severe weather alerts, and (unblockable) Presidential Alerts, intended to inform the public of imminent threats. Recently, a test Presidential Alert was sent to all capable phones in the United States, prompting concerns about how the underlying WEA protocol could be misused or attacked. In this paper, we investigate the details of this system, and develop and demonstrate the first practical spoofing attack on Presidential Alerts, using both commercially available hardware as well as modified open source software. Our attack can be performed using a commercially-available software defined radio, and our modifications to the open source NextEPC and srsLTE software libraries. We find that with only four malicious portable base stations of a single Watt of transmit power each, almost all of a 50,000-seat stadium can be attacked with a 90% success rate. The true impact of such an attack would of course depend on the density of cell phones in range; fake alerts in crowded cities or stadiums could potentially result in cascades of panic. Fixing this problem will require a large collaborative effort between carriers, government stakeholders, and cell phone manufacturers. To seed this effort, we also discuss several defenses to address this threat in both the short and long term.

Since the diagnosis of severe bufferbloat in mobile cellular networks, a number of low-latency congestion control algorithms have been proposed. However, due to the need for continuous bandwidth probing in dynamic cellular channels, existing mechanisms are designed to cyclically overload the network. As a result, it is inevitable that their latency deviates from the smallest possible level (i.e., minimum RTT). To tackle this problem, we propose a new low-latency congestion control, ExLL, which can adapt to dynamic cellular channels without overloading the network. To do so, we develop two novel techniques that run on the cellular receiver: 1) cellular bandwidth inference from the downlink packet reception pattern and 2) minimum RTT calibration from the inference on the uplink scheduling interval. Furthermore, we incorporate the control framework of FAST into ExLL's cellular specific inference techniques. Hence, ExLL can precisely control its congestion window to not overload the network unnecessarily. Our implementation of ExLL on Android smartphones demonstrates that ExLL reduces latency much closer to the minimum RTT compared to other low-latency congestion control algorithms in both static and dynamic channels of LTE networks.

Fog computing is an emerging architecture that aims to run applications on multiple devices that lie on a continuum from cloud servers to personal user smartphones. These architectures allow applications to optimize over the information stored at and functionalities run on each device, based on individual device capabilities. We demonstrate the benefits of this approach for mobile video streaming. Existing HAS (HTTP adaptive streaming) techniques often suffer from problems like unstable video quality and suboptimal resource utilization. We find that a lack of coordination prevents both clientand network-side HAS techniques from solving them. However, our fog approach can exploit existing telecommunication APIs, which expose network capabilities to applications, in order to coordinate between clients and the network. Our coordinated HAS solution, FLARE, optimizes the total utility of all clients in a cell while maintaining stable video quality and supporting user- and device-specific needs. We implement FLARE on a commodity LTE femtocell and use the implementation to conduct the first comparison of HAS players on an LTE femtocell. By conducting extensive experiments using the ns-3 simulator, we also demonstrate that FLARE (i) enhances the average video bitrate, (ii) achieves stable video quality, and (iii) balances the throughput of simultaneous video and data flows, compared to other representative HAS solutions.

ABSTRACT In wireless network convergence, each mobile host is expected to have multiple kinds of wireless interfaces. Multicast‐based applications are expected to be widely deployed. In this paper, a new network selection scheme is proposed for a mobile host to select the most appropriate wireless access network to maximize user satisfaction and ISP's profit, simultaneously. We have devised a metric to measure a user's satisfaction and we also developed a ‘normalized network resource’ metric for system profit measure. We have compared our scheme with three other reference schemes, through simulations. Depending on the network deployment situations, our scheme exhibits one‐fifth service disruption time of other reference schemes, while the resource consumption of our scheme is comparable to that of the minimum resource scheme. Overall, the gain of our scheme becomes higher as users move faster and/or the population density increases. Copyright © 2010 John Wiley & Sons, Ltd.