February 09, 2017

Extracting Location Information Content from RF Fingerprints

  • Claussen H.
  • Dashti M.

Location Fingerprinting (LF) is a promising localization technique that enables many commercial and emergency location-based services (LBS). Significant efforts have been invested in enhancing LF using advanced machine learning methods. Most of these techniques require a huge amount of geo-tagged training data to achieve significant improvement in accuracy. This increases calibration efforts and cost. In this paper, enhancing the localization accuracy by providing more reliable input data to the LF algorithms is discussed. A method to extract the most information content from the fingerprint measurements is proposed. The localization accuracy is improved without increasing the calibration costs or computational complexity. This solution can be scaled for high volume commercial LBS applications. We prototyped our proposed solution to locate users in an enterprise building scenario. Android mobile users connected to our local cloud localization server are accurately located within the building.

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Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...