Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper

Doctor of Philosophy

dissertationThis work seeks to improve upon existing methods for device-free localization (DFL) using radio frequency (RF) sensor networks. Device-free localization is the process of determining the location of a target object, typically a person, without the need for a device to be with the object to aid in localization. An RF sensor network measures changes to radio propagation caused by the presence of a person to locate that person. We show how existing methods which use either wideband or narrowband RF channels can be improved in ways including localization accuracy, energy efficiency, and system cost. We also show how wideband and narrowband systems can combine their information to improve localization. A common assumption in ultra-wideband research is that to estimate the bistatic delay or range, "background subtraction" is effective at removing clutter and must first be performed. Another assumption commonly made is that after background subtraction, each individual multipath component caused by a person's presence can be distinguished perfectly. We show that these assumptions are often not true and that ranging can still be performed even when these assumptions are not true. We propose modeling the difference between a current set of channel impulse responses (CIR) and a set of calibration CIRs as a hidden Markov model (HMM) and show the effectiveness of this model over background subtraction. The methods for performing device-free localization by using ultra-wideband (UWB) measurements and by using received signal strength (RSS) measurements are often considered separate topic of research and viewed only in isolation by two different communities of researchers. We consider both of these methods together and propose methods for combining the information obtained from UWB and RSS measurements. We show that using both methods in conjunction is more effective than either method on its own, especially in a setting where radio placement is constrained. It has been shown that for RSS-based DFL, measuring on multiple channels improves localization accuracy. We consider the trade-o s of measuring all radio links on all channels and the energy and latency expense of making the additional measurements required when sampling multiple channels. We also show the benefits of allowing multiple radios to transmit simultaneously, or in parallel, to better measure the available radio links

Vertrauenswürdige, adaptive Anfrageverarbeitung in dynamischen Sensornetzwerken zur Unterstützung assistiver Systeme

Assistenzsysteme in smarten Umgebungen sammeln durch den Einsatz verschiedenster Sensoren viele Daten, um die Intentionen und zukünftigen Aktivitäten der Nutzer zu berechnen. In den meisten Fällen werden dabei mehr Informationen gesammelt als für die Erfüllung der Aufgabe des Assistenzsystems notwendig sind. Das Ziel dieser Dissertation ist die Konzeption und Implementierung von datenschutzfördernden Algorithmen für die Weitergabe sensibler Sensor- und Kontextinformationen zu den Analysewerkzeugen der Assistenzsysteme. Die Datenschutzansprüche der Nutzer werden dazu in Integritätsbedingungen der Datenbanksysteme transformiert, welche die gesammelten Informationen speichern und auswerten. Ausgehend vom Informationsbedarf des Assistenzsystems und den Datenschutzbedürfnissen des Nutzers werden die gesammelten Daten so nahe wie möglich am Sensor durch Selektion, Reduktion, Kompression oder Aggregation durch die Datenschutzkomponente des Assistenzsystems verdichtet. Sofern nicht alle Informationen lokal verarbeitet werden können, werden Teile der Analyse an andere, an der Verarbeitung der Daten beteiligte Rechenknoten ausgelagert. Das Konzept wurde im Rahmen des PArADISE-Frameworks (Privacy-AwaRe Assistive Distributed Information System Environment) umgesetzt und u. a. in Zusammenarbeit mit dem DFG-Graduiertenkolleg 1424 (MuSAMA-Multimodal Smart Appliances for Mobile Application) anhand eines Beispielszenarios getestet