5 research outputs found
An analysis of the properties and the performance of WiFi RTT for indoor positioning in non-line-of-sight environments
Indoor positioning system based on WiFi Round-Trip Time (RTT) measurement is believed to deliver sub-metre level accuracy with trilateration, under ideal indoor conditions. However, the performance of WiFi RTT positioning in complex, non-line-of-sight environments re-mains a research challenge.To this end, this paper investigates the properties of WiFi RTT in several real-world indoor environments on heterogeneous smartphones. We present a large-scale real-world dataset containing both RTT and received signal strength (RSS) signal measures with correct ground-truth labels.Our results indicated that RTT fingerprinting system delivered an accuracy below 0.75 m which was 98% better than RSS fingerprinting and 166% better than RTT trilateration, which failed to deliver sub-metre accuracy as claimed
Intrusion learning: An overview of an emergent discipline
The purpose of this article is to provide a definition of intrusion learning, identify its distinctive aspects, and provide recommendations for advancing intrusion learning as a practice domain. The authors define intrusion learning as the collection of online network algorithms that learn from and monitor streaming network data resulting in effective intrusion-detection methods for enabling the security and resiliency of enterprise systems. The network algorithms build on advances in cyber-defensive and cyber-offensive capabilities. Intrusion learning is an emerging domain that draws from machine learning, intrusion detection, and streaming network data. Intrusion learning offers to significantly enhance enterprise security and resiliency through augmented perimeter defense and may mitigate increasing threats facing enterprise perimeter protection. The article will be of interest to researchers, sponsors, and entrepreneurs interested in enhancing enterprise security and resiliency
A review of smartphones based indoor positioning: challenges and applications
The continual proliferation of mobile devices has encouraged much effort in
using the smartphones for indoor positioning. This article is dedicated to
review the most recent and interesting smartphones based indoor navigation
systems, ranging from electromagnetic to inertia to visible light ones, with an
emphasis on their unique challenges and potential real-world applications. A
taxonomy of smartphones sensors will be introduced, which serves as the basis
to categorise different positioning systems for reviewing. A set of criteria to
be used for the evaluation purpose will be devised. For each sensor category,
the most recent, interesting and practical systems will be examined, with
detailed discussion on the open research questions for the academics, and the
practicality for the potential clients