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

fi=vertaisarvioimaton|en=nonPeerReviewed

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

Mitigating the Multipath Effects on Radio Tomographic Imaging

Various radio tomographic imaging (RTI) models and reconstruction methods are equipped with capabilities to mitigate the effects of multipath interference. This thesis combined the network shadowing (NeSh) and weighting-g models in conjunction with Tikhonov regularization and low-rank and sparse decomposition (LRSD). MATLAB was used to implement the four combinations for six experimental data sets and produce attenuation images. The attenuation images were analyzed qualitatively and quantitatively to accomplish the goal of determining which combination performed best at locating human targets. After analyzing the results, it was determined that no single combination outperformed the others for at least three out of the five quantitative metrics. Therefore, a rating technique was used instead to normalize the average results of each metric and find the mean across each combination\u27s newly normalized average results. In accordance with the normalization scale, the lowest and best rating revealed the optimum combination was the weighting-g model implemented in conjunction with LRSD

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

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

Correlative Tomography: Three Dimensional Multiscale Imaging and Modelling of Hierarchical Porous Materials

Heterogeneous catalyst based pellets typify a material where functionality is dependant on hierarchical pore structures spanning many orders of magnitude from nanometers up to tens of microns. The total activity, selectivity and lifetime of catalyst based pellets depends on the ability of molecules to flow through a large pellet bed (m), into the pellets (mm) and their pore structure (μm-nm) to/from the active sites. Three dimensional imaging techniques such as tomography allow for the direct characterisation and quantification of pore structures. However, the field of view in tomography decreases as resolution increases. This work circumvents this issue with multiscale tomography (MT) combining x-ray microtomography (XMT), dual beam focused ion beam tomography (DB-FIB) and electron tomography (ET) to probe porous pellet based catalysts. The results show MT as a viable method that offers new insights into the quantification and behaviour of pellet based catalysts across large length scales, all in three dimensions (3D), that no single tomographic technique can adequately capture. MT was successfully used in the characterising of pore sizes, distributions, structures and spatial relationships and this was compared to existing multiscale characterisation techniques to illustrate the new insights that can be obtained. The pore structures were meshed and modeled using MT data to provide results for understanding the transport properties scaled up from the nanometre length scale to the packed bed, through pellet based catalysts produced under different manufacturing conditions. The results show the very strong dependence on the calcining temperature which is important for designing better catalysts in future. The tomography data was also used to determine thermal/mechanical stresses at both a pellet and pellet bed level. Although many stresses are compressive; the packing of the pellets creates local tensile stresses and a potential cause for pellet failure through internal flaws at relatively low loads. In summary, multiscale tomography was demonstrated to be a viable method for obtaining new insights for the development of pellet based catalysts by both improved quantification and allows for the first time direct 3D multiscale simulation of transport and mechanical properties across multiple scales from nanometers to metres to catalyst pellets in beds

Data-driven Channel Learning for Next-generation Communication Systems

University of Minnesota Ph.D. dissertation. October 2019. Major: Electrical/Computer Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); x, 116 pages.The turn of the decade has trademarked the `global society' as an information society, where the creation, distribution, integration, and manipulation of information have significant political, economic, technological, academic, and cultural implications. Its main drivers are digital information and communication technologies, which have resulted in a "data deluge", as the number of smart and Internet-capable devices increases rapidly. Unfortunately, establishing information infrastructure to collect data becomes more challenging particularly as communication networks for those devices become larger, denser, and more heterogeneous to meet the quality-of-service (QoS) for the users. Furthermore, scarcity in spectral resources due to an increased demand for mobile devices urges the development of a new methodology for wireless communications possibly facing unprecedented constraints both on hardware and software. At the same time, recent advances in machine learning tools enable statistical inference with efficiency as well as scalability in par with the volume and dimensionality of the data. These considerations justify the pressing need for machine learning tools that are amenable to new hardware and software constraints, and can scale with the size of networks, to facilitate the advanced operation of next-generation communication systems. The present thesis is centered on analytical and algorithmic foundations enabling statistical inference of critical information under practical hardware/software constraints to design and operate wireless communication networks. The vision is to establish a unified and comprehensive framework based on state-of-the-art data-driven learning and Bayesian inference tools to learn the channel-state information that is accurate yet efficient and non-demanding in terms of resources. The central goal is to theoretically, algorithmically, and experimentally demonstrate how valuable insights from data-driven learning can lead to solutions that markedly advance the state-of-the-art performance on inference of channel-state information. To this end, the present thesis investigates two main research thrusts: i) channel-gain cartography leveraging low-rank and sparsity; and ii) Bayesian approaches to channel-gain cartography for spatially heterogeneous environment. The aforementioned research thrusts introduce novel algorithms that aim to tackle the issues of next-generation communication networks. Potential of the proposed algorithms is showcased by rigorous theoretical results and extensive numerical tests

Near field sensing and antenna design for wireless body area network

PhD ThesisWireless body area network (WBAN) has emerged in recent years as a special class of wireless sensor network; hence, WBAN inherits the wireless sensor network challenges of interference by passive objects in indoor environments. However, attaching wireless nodes to a person’s body imposes a unique challenge, presented by continuous changes in the working environment, due to the normal activities of the monitored personnel. Basic activities, like sitting on a metallic chair or standing near a metallic door, drastically change the antenna behaviour when the metallic object is within the antenna near field. Although antenna coupling with the human body has been investigated by many recent studies, the coupling of the WBAN node antenna with other objects within the surrounding environment has not been thoroughly studied. To address the problems above, the thesis investigates the state-of-the art of WBAN, eximanes the influence of metallic object near an antenna through experimental studies and proposes antenna design and their applications for near field environments. This thesis philosophy for the previously mentioned challenge is to examine and improve the WBAN interaction with its surrounding by enabling the WBAN node to detect nearby objects based solely on change in antenna measurements. The thesis studies the interference caused by passive objects on WBAN node antenna and extracts relevant features to sense the object presence within the near field, and proposes new design of WBAN antenna suitable for this purpose. The major contributions of this study can be summarised as follows. First, it observes and defines the changes in the return loss of a narrow band antenna when a metallic object is introduced in its near field. Two methods were proposed to detect the object, based on the refelction coefficient and transmission coefficient of an antenna in free space. Then, the thesis introduces a new antenna design that conforms to the WBAN requirements of size, while achieving very low sensitivity to human body. This was achieved through combining two opposite Vivaldi shapes on one PCB and using a metallic sheet to act as a reflector, which minimised the antenna coupling with the human body and reduced the radiation pattern towards the body. Finally, the proposed antennas were tested on several human body parts with nearby metallic objects, to compare the change in antenna s-parameters due to presence of the human body and presence of the metallic object. Based on the measurements, basic statistical indicators and Principal Component Analysis were proposed to detect object presense and estimate its distance. In conclusion, the thesis successfully shows WBAN antenna’s ability to detect nearby metallic objects through a set of proposed indicators and novel antenna design. The thesis is wrapped up by the suggestion to investigate time domain features and modulated signal for future work in WBAN near field sensing