The 17th International Conference on Mobility, Sensing and Networking (MSN 2021)

Session ECAISS: ECAISS
Session IDC: IDC
Session Tutorial-1: RFID and Backscatter Communications for Motion Capture and Fine Scale Localization Session Tutorial-2: Federated Analytics: A New Collaborative Computing Paradigm towards Privacy focusing World Session Tutorial-3: Machine Learning Security and Privacy in Networking
Session NMIC-S1: NMIC Session I Session NMIC-S2: NMIC Session II
Session UEIoT: UEIoT
Session AI2OT: AI2OT

Tutorials

Session Tutorial-1

RFID and Backscatter Communications for Motion Capture and Fine Scale Localization

Conference
8:00 AM — 10:00 AM GMT
Local
Dec 13 Mon, 2:00 AM — 4:00 AM CST

RFID and Backscatter Communications for Motion Capture and Fine Scale Localization

Prof. Gregory D. Durgin (Georgia Institute of Technology, USA)

0
How do you capture the choreography of a ballerina’s performance? How does a drone navigate a vast, complex shipping yard to perform inventory? How do you condition a large-aperture antenna so that it is capable of beaming microwave power across long distances in space? In this tutorial, we answer these questions by exploring the emerging world of RFID-based motion capture and fine-scale localization. This tutorial first presents the fundamental barriers that wireless techniques experience in the drive for precise localization. We then survey the available techniques – from basic signal-strength mapping localization using off-the-shelf RFID tags to elegant, quantum-tunneling tags that are used to trace out the echoes of surrounding RF multipath – and quantify/rank performance. RFID and backscatter-based approaches are shown to have the most promise for realizing real-time, motion-capture-grade localization for wireless nodes.
Session Chair

Gregory D. Durgin (Georgia Tech., USA)

Session Tutorial-2

Federated Analytics: A New Collaborative Computing Paradigm towards Privacy focusing World

Conference
12:00 PM — 3:00 PM GMT
Local
Dec 13 Mon, 6:00 AM — 9:00 AM CST

Federated Analytics: A New Collaborative Computing Paradigm towards Privacy focusing World

Prof. Dan Wang and Ms. Siping Shi (The Hong Kong Polytechnic University, Hong Kong, China)

0
In this tutorial, we present federated analytics, a new distributed computing paradigm for data analytics applications with privacy concerns. Today’s edge-side applications generate massive data. In many applications, the edge devices and the data belong to diverse owners; thus data privacy has become a concern to these owners. Federated analytics is a newly proposed computing paradigm where raw data are kept local with local analytics and only the insights generated from local analytics are sent to a server for result aggregation. It differs from the federated learning paradigm in the sense that federated learning emphasizes on collaborative model training, whereas federated analytics emphasizes on drawing conclusions from data. This tutorial will be divided into three parts. First, we will present the definition, taxonomy, application cases and architecture of the federated analytics paradigm. In particular, we present a federated video analytics framework which can be used for HD map construction using social vehicles with privacy concerns. Second, we will present federated anomaly analytics to address the local model poisoning attack in current federated learning systems. Third, we will present federated skewness analytics to address the data skewness problem in current federated learning systems.
Session Chair

Dan Wang (The Hong Kong Polytechnic University, Hong Kong)

Session Tutorial-3

Machine Learning Security and Privacy in Networking

Conference
3:15 PM — 6:15 PM GMT
Local
Dec 13 Mon, 9:15 AM — 12:15 PM CST

Machine Learning Security and Privacy in Networking

Prof. Yanjiao Chen (Zhejiang University, P. R. China)

0
Machine learning has gradually found its way into the networking area. Unfortunately, the vulnerability of machine learning models also infects the networking domain, raising alarming issues that may threaten the privacy and security of critical applications. In this tutorial, I will give a systematic introduction of typical attacks against machine learning models, including adversarial attacks, backdoor attacks, membership inference attacks, model extraction attacks, model inversion attacks and so on. The tutorial will cover a series of works on applying modern machine learning to networking and analyze the potential risk of current architectures of machine learning models and its impact on networking applications.
Session Chair

Yanjiao Chen (Zhejiang University, China)

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