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

Doctor of Philosophy

dissertationLocation information of people is valuable for many applications including logistics, healthcare, security and smart facilities. This dissertation focuses on localization of people in wireless sensor networks using radio frequency (RF) signals, speci cally received signal strength (RSS) measurements. A static sensor network can make RSS measurements of the signal from a transmitting badge that a person wears in order to locate the badge. We call this kind of localization method radio device localization. Since the human body causes RSS changes between pairwise sensor nodes of a static network, we can also use RSS measurements from pairwise nodes of a network to locate people, even if they are not carrying any radio device. We call this device-free localization (DFL). The rst contribution of this dissertation is to radio device localization. The human body has a major e ect on the antenna gain pattern of the transmitting badge that the person is wearing, however, existing r

Doctor of Philosophy

dissertationDevice-free localization (DFL) and tracking services are important components in security, emergency response, home and building automation, and assisted living applications where an action is taken based on a person's location. In this dissertation, we develop new methods and models to enable and improve DFL in a variety of radio frequency sensor network configurations. In the first contribution of this work, we develop a linear regression and line stabbing method which use a history of line crossing measurements to estimate the track of a person walking through a wireless network. Our methods provide an alternative approach to DFL in wireless networks where the number of nodes that can communicate with each other in a wireless network is limited and traditional DFL methods are ill-suited. We then present new methods that enable through-wall DFL when nodes in the network are in motion. We demonstrate that we can detect when a person crosses between ultra-wideband radios in motion based on changes in the energy contained in the first few nanoseconds of a measured channel impulse response. Through experimental testing, we show how our methods can localize a person through walls with transceivers in motion. Next, we develop new algorithms to localize boundary crossings when a person crosses between multiple nodes simultaneously. We experimentally evaluate our algorithms with received signal strength (RSS) measurements collected from a row of radio frequency (RF) nodes placed along a boundary and show that our algorithms achieve orders of magnitude better localization classification than baseline DFL methods. We then present a way to improve the models used in through-wall radio tomographic imaging with E-shaped patch antennas we develop and fabricate which remain tuned even when placed against a dielectric. Through experimentation, we demonstrate the E-shaped patch antennas lower localization error by 44% compared with omnidirectional and microstrip patch antennas. In our final contribution, we develop a new mixture model that relates a link's RSS as a function of a person's location in a wireless network. We develop new localization methods that compute the probabilities of a person occupying a location based on our mixture model. Our methods continuously recalibrate the model to achieve a low localization error even in changing environments

Wireless sensor systems in indoor situation modeling II (WISM II)

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Localization Services for Online Common Operational Picture and Situation Awareness

Many operations, be they military, police, rescue, or other field operations, require localization services and online situation awareness to make them effective. Questions such as how many people are inside a building and their locations are essential. In this paper, an online localization and situation awareness system is presented, called Mobile Urban Situation Awareness System (MUSAS), for gathering and maintaining localization information, to form a common operational picture. The MUSAS provides multiple localization services, as well as visualization of other sensor data, in a common frame of reference. The information and common operational picture of the system is conveyed to all parties involved in the operation, the field team, and people in the command post. In this paper, a general system architecture for enabling localization based situation awareness is designed and the MUSAS system solution is presented. The developed subsystem components and forming of the common operational picture are summarized, and the future potential of the system for various scenarios is discussed. In the demonstration, the MUSAS is deployed to an unknown building, in an ad hoc fashion, to provide situation awareness in an urban indoor military operation.Peer reviewe

Device-free indoor localisation with non-wireless sensing techniques : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electronics and Computer Engineering, Massey University, Albany, New Zealand

Global Navigation Satellite Systems provide accurate and reliable outdoor positioning to support a large number of applications across many sectors. Unfortunately, such systems do not operate reliably inside buildings due to the signal degradation caused by the absence of a clear line of sight with the satellites. The past two decades have therefore seen intensive research into the development of Indoor Positioning System (IPS). While considerable progress has been made in the indoor localisation discipline, there is still no widely adopted solution. The proliferation of Internet of Things (IoT) devices within the modern built environment provides an opportunity to localise human subjects by utilising such ubiquitous networked devices. This thesis presents the development, implementation and evaluation of several passive indoor positioning systems using ambient Visible Light Positioning (VLP), capacitive-flooring, and thermopile sensors (low-resolution thermal cameras). These systems position the human subject in a device-free manner (i.e., the subject is not required to be instrumented). The developed systems improve upon the state-of-the-art solutions by offering superior position accuracy whilst also using more robust and generalised test setups. The developed passive VLP system is one of the first reported solutions making use of ambient light to position a moving human subject. The capacitive-floor based system improves upon the accuracy of existing flooring solutions as well as demonstrates the potential for automated fall detection. The system also requires very little calibration, i.e., variations of the environment or subject have very little impact upon it. The thermopile positioning system is also shown to be robust to changes in the environment and subjects. Improvements are made over the current literature by testing across multiple environments and subjects whilst using a robust ground truth system. Finally, advanced machine learning methods were implemented and benchmarked against a thermopile dataset which has been made available for other researchers to use

Langattomien anturiverkkojen sotilas-, agroteknologia- ja energiatutkimussovelluksia

The physical quantities nowadays are widely measured by using electronic sensors. Wireless sensor networks (WSNs) are low-cost, low-power electronic devices capable of collecting data using their onboard sensors. Some wireless sensor nodes are equipped with actuators, providing the possibility to change the state of the physical world. The ability to change the state of a physical system means that WSNs can be used in control and automation applications. This research focuses on appropriate system design for four different wireless measurement and control cases. The first case provides a hardware and software solution for camera integration to a wireless sensor node. The images are captured and processed inside the sensor node using low power computational techniques. In the second application, two different wireless sensor networks function in cooperation to overcome seeding problems in agricultural machinery. The third case focuses on indoor deployment of the wireless sensor nodes into an area of urban crisis, where the nodes supply localization information to friendly assets such as soldiers, firefighters and medical personnel. The last application focuses on a feasibility study for energy harvesting from asphalt surfaces in the form of heat.Fysikaaliset suureet mitataan nykyisin elektronisten anturien avulla. Langattomat anturiverkot ovat kustannustasoltaan edullisia, matalan tehonkulutuksen elektronisia laitteita, jotka kykenevät suorittamaan mittauksia niissä olevilla antureilla. Langattomat anturinoodit voidaan myös liittää toimilaitteisiin, jolloin ne voivat vaikuttaa fyysiseen ympäristöönsä. Koska langattomilla anturi- ja toimilaiteverkoilla voidaan vaikuttaa niiden fysikaalisen ympäristön tilaan, niiden avulla voidaan toteuttaa säätö- ja automaatiosovelluksia. Tässä väitöskirjaty össä suunnitellaan ja toteutetaan neljä erilaista langattomien anturi- ja toimilaiteverkkojen automaatiosovellusta. Ensimmäisenä tapauksena toteutetaan elektroniikka- ja ohjelmistosovellus, jolla integroidaan kamera langattomaan anturinoodiin. Kuvat tallennetaan ja prosessoidaan anturinoodissa vähän energiaa kuluttavia laskentamenetelmiä käyttäen. Toisessa sovelluksessa kahdesta erilaisesta langattomasta anturiverkosta koostuvalla järjestelmällä valvotaan siementen syöttöä kylvökoneessa. Kolmannessa sovelluksessa levitetään kaupunkiympäristössä kriisitilanteessa rakennuksen sisätiloihin langaton anturiverkko. Sen anturinoodit välittävät paikkatietoa rakennuksessa operoiville omille joukoille, jotka voivat tilanteesta riippuen olla esimerkiksi sotilaita, palomiehiä tai lääkintähenkilökuntaa. Neljännessä sovelluksessa toteutetaan langaton anturiverkko, jonka keräämää mittausdataa käytetään arvioitaessa lämpöenergian keräämismahdollisuuksia asfalttipinnoilta.fi=vertaisarvioitu|en=peerReviewed

Identifying High-Traffic Patterns in the Workplace With Radio Tomographic Imaging in 3D Wireless Sensor Networks

The rapid progress of wireless communication and embedded mircro-sensing electro-mechanical systems (MEMS) technologies has resulted in a growing confidence in the use of wireless sensor networks (WSNs) comprised of low-cost, low-power devices performing various monitoring tasks. Radio Tomographic Imaging (RTI) is a technology for localizing, tracking, and imaging device-free objects in a WSN using the change in received signal strength (RSS) of the radio links the object is obstructing. This thesis employs an experimental indoor three-dimensional (3-D) RTI network constructed of 80 wireless radios in a 100 square foot area. Experimental results are presented from a series of stationary target localization and target tracking experiments using one and two targets. Preliminary results demonstrate a 3-D RTI network can be effectively used to generate 3-D RSS-based images to extract target features such as size and height, and identify high-traffic patterns in the workplace by tracking asset movement