RSSI localization with Gaussian Processes and Tracking

  • Claussen H.
  • Dashti M.
  • Perez-Cruz F.
  • Yiu S.

Location Fingerprinting (LF) is a promising localization technique that enables many commercial and emergency location-based services (LBS). While significant efforts have been invested in enhancing LF using advanced machine learning methods, the configuration effort required to deploy a LF system remains a significant issue. In this paper a practical LF system is proposed which employs Gaussian Processes (GP) to significantly reduce the required data base density. The GP solution is enhanced with a tracking algorithm which can easily incorporate floor plan constraints. The proposed system was prototyped with Android mobile phones in an enterprise environment. It is shown that with the proposed system an accuracy required for most commercial LBS applications can be achieved with a significantly reduced configuration effort.

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 ...