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

Device Free Localisation Techniques in Indoor Environments

The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised

An adaptive weighting algorithm for accurate radio tomographic image in the environment with multipath and WiFi interference

Radio frequency device-free localization based on wireless sensor network has proved its feasibility in buildings. With this technique, a target can be located relying on the changes of received signal strengths caused by the moving object. However, the accuracy of many such systems deteriorates seriously in the environment with WiFi and the multipath interference. State-of-the-art methods do not efficiently solve the WiFi and multipath interference problems at the same time. In this article, we propose and evaluate an adaptive weighting radio tomography image algorithm to improve the accuracy of radio frequency device-free localization in the environment with multipath and different intensity of WiFi interference. Field experiments prove that our approach outperforms the state-of-the-art radio frequency device-free localization systems in the environment with multipath and WiFi interference

Indoor localization utilizing existing infrastructure in smart homes : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer and Electronics Engineering, Massey University, Albany, New Zealand

Listed in 2019 Dean's List of Exceptional ThesesIndoor positioning system (IPS) have received significant interest from the research community over the past decade. However, this has not eventuated into widespread adoption of IPS and few commercial solutions exist. Integration into Smart Homes could allow for secondary services including location-based services, targeted user experiences and intrusion detection, to be enabled using the existing underlying infrastructure. Since New Zealand has an aging population, we must ensure that the elderly are well looked after. An IPS solution could detect whether a person has been immobile for an extended period and alert medical personnel. A major shortcoming of existing IPS is their reliance on end-users to undertake a significant infrastructure investment to facilitate the localization tasks. An IPS that does not require extensive installation and calibration procedures, could potentially see significant uptake from end users. In order to expedite the widespread adoption of IPS technology, this thesis focuses on four major areas of improvement, namely: infrastructure reuse, reduced node density, algorithm improvement and reduced end user calibration requirements. The work presented demonstrates the feasibility of utilizing existing wireless and lighting infrastructure for positioning and implements novel spring-relaxation and potential fields-based localization approaches that allow for robust target tracking, with minimal calibration requirements. The developed novel localization algorithms are benchmarked against the existing state of the art and show superior performance

Multiple target tracking with RF sensor networks

pre-printRF sensor networks are wireless networks that can localize and track people (or targets) without needing them to carry or wear any electronic device. They use the change in the received signal strength (RSS) of the links due to the movements of people to infer their locations. In this paper, we consider real-time multiple target tracking with RF sensor networks. We apply radio tomographic imaging (RTI), which generates images of the change in the propagation field, as if they were frames of a video. Our RTI method uses RSS measurements on multiple frequency channels on each link, combining them with a fade level-based weighted average. We introduce methods, inspired by machine vision and adapted to the peculiarities of RTI, that enable accurate and real-time multiple target tracking. Several tests are performed in an open environment, a one-bedroom apartment, and a cluttered office environment. The results demonstrate that the system is capable of accurately tracking in real-time up to four targets in cluttered indoor environments, even when their trajectories intersect multiple times, without mis-estimating the number of targets found in the monitored area. The highest average tracking error measured in the tests is 0.45 m with two targets, 0.46 m with three targets, and 0.55 m with four targets

Wireless Sensor Network Optimization for Radio Tomographic Imaging

Radio tomographic imaging (RTI) is a form of device-free, passive localization (DFPL) that uses a wireless sensor network (WSN) typically made up of affordable, low-power transceivers. The intent for RTI is to have the ability to monitor a given area, localizing and tracking obstructions within. The specific advantages rendered by RTI include the ability to provide imaging, localization, and tracking where other well developed methods like optical surveillance fall short. RTI can function through optical obstructions such as smoke and even physical obstructions like walls. This provides a tool that is particularly valuable for tactical operations like emergency response and military operations in urban terrain (MOUT). Many methods to optimize the performance of RTI systems have been explored, but little work that focuses on the sequence of transceiver reports can be found in the body of literature. This thesis provides an exploration of the effects from attempting to optimize the transmission sequence in a WSN by creating a metric to quantify the value of the information a transceiver will report and using it to develop a dynamic, utility-driven, token passing process. After deriving a metric from the Fisher information matrix of the imaging solution, it was combined with a weighting based on the time each node last reported across the WSN. Modeling and simulation was performed to determine if the novel transmission sequence provided any benefit to the localization and tracking performance. The results showed a small improvement in two different localization methods when packet loss in the WSN reached 50%. These results provide a proof-of-concept that warrants further exploration and suggest that performance improvements may be realized by implementing a transmission sequence based on the metric developed in this thesis

RSS-based Device-free Passive Detection and Localization using Home Automation Network Radio Frequencies

This research provided a proof of concept for a device-free passive (DfP) system capable of detecting and localizing a target through exploitation of a home automation network’s radio frequency (RF) signals. The system was developed using Insteon devices with a 915 MHz center frequency. Without developer privileges, limitations of the Insteon technology like no intrinsic received signal strength (RSS) field and silent periods between messages were overcome by using software-defined radios to simulate Insteon devices capable of collecting and reporting RSS, and by creating a message generation script and implementing a calibrated filter threshold to reduce silent periods. Evaluation of the system deployment in a simple room with no furniture produced detection rates up to PD Æ 100% and false positive rates as low as PF Æ 1.6% for baseline threshold detection along the line of sight (LOS) in a simple tripwire setup. Signal attenuation of foam blocks at different distances along this LOS ranged from 2.2-4.4 dB. Cell-based fingerprinting for localization using multiple nodes in this room achieved accuracy only as high as PA Æ 5.4% and false positives only as low as PF Æ 88.3%. A context-based localization method was developed in response and was able to achieve PA Æ 28.3% and PF Æ 40.0%. The system was then deployed in a similar room containing several metal objects and achieved PA Æ 42.2% and PF Æ 0.0%. Deployment in a similar room with RF absorbent objects achieved PA Æ 23.3% and PF Æ 53.3%. Feasibility of exploiting RF of a home automation network for DfP indoor detection and localization was demonstrated. Despite not achieving optimal localization performance, the results showed promise for future DfP system deployment on top of home automation RF devices