A computational model for path loss in wireless sensor networks in orchard environments.

A computational model for radio wave propagation through tree orchards is presented. Trees are modeled as collections of branches, geometrically approximated by cylinders, whose dimensions are determined on the basis of measurements in a cherry orchard. Tree canopies are modeled as dielectric spheres of appropriate size. A single row of trees was modeled by creating copies of a representative tree model positioned on top of a rectangular, lossy dielectric slab that simulated the ground. The complete scattering model, including soil and trees, enhanced by periodicity conditions corresponding to the array, was characterized via a commercial computational software tool for simulating the wave propagation by means of the Finite Element Method. The attenuation of the simulated signal was compared to measurements taken in the cherry orchard, using two ZigBee receiver-transmitter modules. Near the top of the tree canopies (at 3 m), the predicted attenuation was close to the measured one-just slightly underestimated. However, at 1.5 m the solver underestimated the measured attenuation significantly, especially when leaves were present and, as distances grew longer. This suggests that the effects of scattering from neighboring tree rows need to be incorporated into the model. However, complex geometries result in ill conditioned linear systems that affect the solver's convergence

Wi-PoS : a low-cost, open source ultra-wideband (UWB) hardware platform with long range sub-GHz backbone

Ultra-wideband (UWB) localization is one of the most promising approaches for indoor localization due to its accurate positioning capabilities, immunity against multipath fading, and excellent resilience against narrowband interference. However, UWB researchers are currently limited by the small amount of feasible open source hardware that is publicly available. We developed a new open source hardware platform, Wi-PoS, for precise UWB localization based on Decawave’s DW1000 UWB transceiver with several unique features: support of both long-range sub-GHz and 2.4 GHz back-end communication between nodes, flexible interfacing with external UWB antennas, and an easy implementation of the MAC layer with the Time-Annotated Instruction Set Computer (TAISC) framework. Both hardware and software are open source and all parameters of the UWB ranging can be adjusted, calibrated, and analyzed. This paper explains the main specifications of the hardware platform, illustrates design decisions, and evaluates the performance of the board in terms of range, accuracy, and energy consumption. The accuracy of the ranging system was below 10 cm in an indoor lab environment at distances up to 5 m, and accuracy smaller than 5 cm was obtained at 50 and 75 m in an outdoor environment. A theoretical model was derived for predicting the path loss and the influence of the most important ground reflection. At the same time, the average energy consumption of the hardware was very low with only 81 mA for a tag node and 63 mA for the active anchor nodes, permitting the system to run for several days on a mobile battery pack and allowing easy and fast deployment on sites without an accessible power supply or backbone network. The UWB hardware platform demonstrated flexibility, easy installation, and low power consumption

Antenna and radio channel characterisation for low‐power personal and body area networks

PhDThe continuous miniaturisation of sensors, as well as the progression in wearable electronics, embedded software, digital signal processing and biomedical technologies, have led to new usercentric networks, where devices can be carried in the user’s pockets, attached to the user’s body. Body-centric wireless communications (BCWCs) is a central point in the development of fourth generation mobile communications. Body-centric wireless networks take their place within the personal area networks, body area networks and sensor networks which are all emerging technologies that have a wide range of applications (such as, healthcare, entertainment, surveillance, emergency, sports and military). The major difference between BCWC and conventional wireless systems is the radio channels over which the communication takes place. The human body is a hostile environment from a radio propagation perspective and it is therefore important to understand and characterise the effects of the human body on the antenna elements, the radio channel parameters and, hence, system performance. This thesis focuses on the study of body-worn antennas and on-body radio propagation channels. The performance parameters of five different narrowband (2.45 GHz) and four UWB (3.1- 10.6 GHz) body-worn antennas in the presence of human body are investigated and compared. This was performed through a combination of numerical simulations and measurement campaigns. Parametric studies and statistical analysis, addressing the human body effects on the performance parameters of different types of narrowband and UWB antennas have been presented. The aim of this study is to understand the human body effects on the antenna parameters and specify the suitable antenna in BCWCs at both 2.45 GHz and UWB frequencies. Extensive experimental investigations are carried out to study the effects of various antenna types on the on-body radio propagation channels as well. Results and analysis emphasize the best body-worn antenna for reliable and power-efficient on-body communications. Based on the results and analysis, a novel dual-band and dual-mode antenna is proposed for power-efficient and reliable on-body and off-body communications. The on-body performance of the DBDM antenna at 2.45 GHz is compared with other five narrowband antennas. Based on the results and analysis of six narrowband and four UWB antennas, antenna specifications and design guidelines are provided that will help in selecting the best body-worn antenna for both narrowband and UWB systems to be applied in body-centric wireless networks (BCWNs). A comparison between IV the narrowband and UWB antenna parameters are also provided. At the end of the thesis, the subject-specificity of the on-body radio propagation channel at 2.45 GHz and 3-10 GHz was experimentally investigated by considering eight real human test subjects of different shapes, heights and sizes. The subject-specificity of the on-body radio propagation channels was compared between the narrowband and UWB systems as well

Diode-switched thermal-transfer printed antenna on flexible substrate

We demonstrate that diode-switching can be used to introduce frequency agility into antennas produced by thermal transfer printing. Our particular example is a triangular Sierpinski fractal pattern with two PIN diodes to switch between operation optimised for the 800 MHz UHF band (diodes on) and the 2400 MHz ISM band (diodes off). Our measured results show an improvement in S11 in the UHF band from -2 dB to -28 dB, and from -7 dB to -30 dB at 2400 MHz, when switching the diodes appropriately. The measured bandwidth is 200 (1000) MHz, and the measured directivity is 3.1dB (5.2dB) while the measured gain is -5.2dB (6.7dB) for the diodes on(off)

An adaptable fuzzy-based model for predicting link quality in robot networks.

It is often essential for robots to maintain wireless connectivity with other systems so that commands, sensor data, and other situational information can be exchanged. Unfortunately, maintaining sufficient connection quality between these systems can be problematic. Robot mobility, combined with the attenuation and rapid dynamics associated with radio wave propagation, can cause frequent link quality (LQ) issues such as degraded throughput, temporary disconnects, or even link failure. In order to proactively mitigate such problems, robots must possess the capability, at the application layer, to gauge the quality of their wireless connections. However, many of the existing approaches lack adaptability or the framework necessary to rapidly build and sustain an accurate LQ prediction model. The primary contribution of this dissertation is the introduction of a novel way of blending machine learning with fuzzy logic so that an adaptable, yet intuitive LQ prediction model can be formed. Another significant contribution includes the evaluation of a unique active and incremental learning framework for quickly constructing and maintaining prediction models in robot networks with minimal sampling overhead

Characterization of UAV-based Wireless Channels With Diverse Antenna Configurations

In the next wave of swarm-based applications, unmanned aerial vehicles (UAVs) need to communicate with peer drones in any direction of a three-dimensional (3D) space. On a given drone and across drones, various antenna positions and orientations are possible. We know that, in free space, high levels of signal loss are expected if the transmitting and receiving antennas are cross polarized. However, increasing the reflective and scattering objects in the channel between a transmitter and receiver can cause the received polarization to become completely independent from the transmitted polarization, making the cross-polarization of antennas insignificant. Usually, these effects are studied in the context of cellular and terrestrial networks and have not been analyzed when those objects are the actual bodies of the communicating drones that can take different relative directions or move at various elevations. In this work, we show that the body of the drone can affect the received power across various antenna orientations and positions and act as a local scatterer that increases channel depolarization, reducing the cross-polarization discrimination (XPD). In addition to communicating with other UAVs in a swarm, UAVs can also serve users on the ground. For example, at ultra-low altitudes, an unmanned aerial vehicle (UAV) can act as a personal base station where it communicates only with one or two users on the ground. The communication device used by a user can be in their pocket, held by hand, or attached to their bodies. In these scenarios, the wireless channel can go through different fading levels, depending on the UAV’s location, user orientation, the location of the UE near the user’s body, and the frequency of the transmitted signal. The extent to which these factors can affect Air-to-Ground channels at ultra-low altitudes is studied in this work. We answer questions regarding how the human body and different use-cases of holding a communication device on the ground can affect the quality of the wireless channel and the optimal UAV hovering location. Furthermore, we demonstrate how the observed effects can be leveraged to our advantage and increase the physical layer security of UAV-assisted networks relying on the human-induced effects. Finally, in situations where a UAV swarm needs to communicate with a target that is far or surrounded by undesired receivers, beamforming can be an attractive solution. With beamforming, the transmitted signal becomes shaped towards a certain direction confining its spatial signature and increasing the received signal-to-noise-ratio (SNR) at the receiver. However, phase synchronization across the swarm is difficult to achieve and there will always exist some degree of phase incoherency across the transmitted signals from the distributed UAVs. Phase differences between the distributed nodes would result in signals arriving at different times and their phases might not align with each other resulting in reductions in beamforming gain. Hence, a method to increase phase coherency at the receiver with limited channel overhead is desired. To this end, we propose a UAV rotation-based method through which the UAV, relying on its heterogeneous body structure, can alter the phase of the incoming signals and increase the beamformed signal level

CHANNEL MODELING FOR FIFTH GENERATION CELLULAR NETWORKS AND WIRELESS SENSOR NETWORKS

In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance

Conductive Textiles and their use in Combat Wound Detection, Sensing, and Localization Applications

Conductive textiles, originally used for electromagnetic shielding purposes, have recently been utilized in body area network applications as fabric antennas and distributed sensors used to document and analyze kinematic movement, health vital signs, or haptic interactions. This thesis investigates the potential for using conductive textiles as a distributed sensor and integrated communication system component for use in combat wound detection, sensing, and localization applications. The goal of these proof-of-concept experiments is to provide a basis for robust system development which can expedite and direct the medical response team in the field. The combat wound detection system would have the capability of predicting the presence and location of cuts or tears within the conductive fabric as a realization of bullet or shrapnel penetration. Collected data, along with health vitals gathered from additional sensors, will then be wirelessly transmitted via integrated communication system components, to the appropriate medical response team. A distributed sensing method is developed to accurately predict the location and presence of textile penetrations. This method employs a Wheatstone bridge and multiplexing circuitry to probe a resistor network. Localized changes in resistance illustrate the presence and approximate location of cuts within the conductive textile. Additionally, this thesis builds upon manually defined textile antennas presented in literature by employing a laser cutting system to accurately define antenna dimensions. With this technique, a variety of antennas are developed for various purposes including large data transmission as would be expected from multi-sensor system integration. The fabrication technique also illustrates multilayer antenna development. To confirm simulation results, electrical parameters are extracted using a single-frequency resonance method. These parameters are used in the simulation and design of single-element and two-element wideband slot antennas as well as the design of a wideband monopole antenna. The monopole antenna is introduced to an indoor ultra-wideband (UWB) localization system to illustrate the capability of pinpointing the wearer of textile antennas for localization applications. A cavity-backed dog-bone slot antenna is developed to establish the ability to incorporate conductive vias by sewing conductive thread. This technique can be easily extrapolated to the development of textile substrate integrated waveguide (SIW) technologies

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