Event Category: In-house Presentation

In-depth analysis of the Android supply chain: Vendor customizations on critical networking components

The openness and extensibility of the Android Open Source Project (AOSP) enable Android device vendors, also known as Original Equipment...

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Simulation-based Comparison of Network Scale-Up Methods

The Network Scale-up Method (NSUM) is a method to estimate hard-to-reach populations based on indirect surveys. Unlike the classical surveys,...

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Rethinking LiFi for Carbon Neutral Sunlight-based Communication

Wireless communication has to be redesigned to achieve sustainable communication infrastructure providing net-zero carbon dioxide emission. In this presentation, we...

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SIGNiPHY: Reconciling Random Access with Directional Reception for Efficient mmWave WLANs

Millimeter-Wave (mmWave) WiFi can provide very low latency and multi-Gbps throughput, but real-world deployments usually do not achieve the theoretically...

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The Case for Hierarchical Deep Learning Inference at the Network Edge

Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL)...

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Fast Detection of Cyberattacks on the Metaverse through User-plane Inference

The metaverse is envisioned as a digital world where people can experience an immersive three-dimensional Internet, thanks to the profound...

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Flowrest: Practical Flow-Level Inference in Programmable Switches with Random Forests

User-plane machine learning enables low-latency and high-throughput inference at line rate. Yet, data planes are highly constrained environments, and restrictions...

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A high-resolution service-level traffic dataset

The study of sociology and intrinsic human characteristics is reaching new frontiers after getting the access of mobile data sources....

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Quantum Networks: A stepping stone towards the Quantum Internet

The field of Quantum Communications has seen a rapid transition from a theoretical topic at the boundary of Physics and...

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AutoManager: a Meta-Learning Model for Network Management from Intertwined Forecasts

A variety of network management and orchestration (MANO) tasks take advantage of predictions to support anticipatory decisions. In many practical...

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