353,123 research outputs found
Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems
Localization services for wireless devices play an increasingly important
role in our daily lives and a plethora of emerging services and applications
already rely on precise position information. Widely used on-device positioning
methods, such as the global positioning system, enable accurate outdoor
positioning and provide the users with full control over what services and
applications are allowed to access their location information. In order to
provide accurate positioning indoors or in cluttered urban scenarios without
line-of-sight satellite connectivity, powerful off-device positioning systems,
which process channel state information (CSI) measured at the infrastructure
base stations or access points with deep neural networks, have emerged
recently. Such off-device wireless positioning systems inherently link a user's
data transmission with its localization, since accurate CSI measurements are
necessary for reliable wireless communication -- this not only prevents the
users from controlling who can access this information but also enables
virtually everyone in the device's range to estimate its location, resulting in
serious privacy and security concerns. We therefore propose on-device attacks
against off-device wireless positioning systems in multi-antenna orthogonal
frequency-division multiplexing systems while remaining standard compliant and
minimizing the impact on quality-of-service, and we demonstrate their efficacy
using real-world measured datasets for cellular outdoor and wireless LAN indoor
scenarios. We also investigate defenses to counter such attack mechanisms, and
we discuss the limitations and implications on protecting location privacy in
existing and future wireless communication systems.Comment: To appear in the IEEE Transactions on Wireless Communication
Town of Milton Shoreland Protection Project
Mettee Planning Consultants (MPC) worked with the Milton Conservation Commission to evaluate and streamline the townâs water resource protection regulations. One of the outcomes was revision and passage of a Shoreland Protection Overlay District as part of the townâs zoning ordinance
Joint in-network video rate adaptation and measurement-based admission control: algorithm design and evaluation
The important new revenue opportunities that multimedia services offer to network and service providers come with important management challenges. For providers, it is important to control the video quality that is offered and perceived by the user, typically known as the quality of experience (QoE). Both admission control and scalable video coding techniques can control the QoE by blocking connections or adapting the video rate but influence each other's performance. In this article, we propose an in-network video rate adaptation mechanism that enables a provider to define a policy on how the video rate adaptation should be performed to maximize the provider's objective (e.g., a maximization of revenue or QoE). We discuss the need for a close interaction of the video rate adaptation algorithm with a measurement based admission control system, allowing to effectively orchestrate both algorithms and timely switch from video rate adaptation to the blocking of connections. We propose two different rate adaptation decision algorithms that calculate which videos need to be adapted: an optimal one in terms of the provider's policy and a heuristic based on the utility of each connection. Through an extensive performance evaluation, we show the impact of both algorithms on the rate adaptation, network utilisation and the stability of the video rate adaptation. We show that both algorithms outperform other configurations with at least 10 %. Moreover, we show that the proposed heuristic is about 500 times faster than the optimal algorithm and experiences only a performance drop of approximately 2 %, given the investigated video delivery scenario
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