January 19, 2017

A Metric to Describe Access Point Significance in Location Estimation

  • Claussen H.
  • Dashti M.

Indoor localization is a key enabling technology for numerous location based services (LBS). A promising indoor localization technique is location fingerprinting (LF), having the major advantage of exploiting already existing radio infrastructures. LF estimates users location accurately providing reliable Rf fingerprints, that are unique and stable over time. We propose a method to advance the LF by exploiting the spatio temporal characteristics of RF signals. The method quantifies an access point's (AP) significance in location estimation based on spatial uniqueness and temporal stability characteristics of its RF signals. Based on the proposed significance metric, APs contribute with different weights in location estimation. By weighting the measurements, more reliable input data are provided to the localization algorithm which consequently results in improved LF performance.

View Original Article

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 ...