信道質量和服務質量（QoS）是無線體域網的兩個重要挑戰。本文旨在研究信道特性和在保證高吞吐率和低數據弛豫時間前提下探索低功耗WBAN系統策略。本論文的貢獻在於三個方面。首先研究人體信道（HBC）特性。作為IEEE標準802.15.6定義的三種PHY層之一，HBC已經作為體表傳感器通信媒介被廣泛研究。但是，HBC的詳細機理至今仍然不夠明朗，特別是對於那些采用了體內傳感器的應用更少人涉足。因此，我們為此專門預設四種應用場景，並在場景中測量實際信道特性。依據測量結果，我們觀測到數個影響信道質量的因素。其次，我們提出了一種專門針對WBAN的信道建模方法。該方法依賴於對人體組織的直接測量結果，並且此建模方法在建模過程中兼顧體表通信和體內通信。該建模方法包括兩個階段，第一階段是構建人體各部分的子模型，在第二階段調用先前構建之子模型並依賴信號衰減特性來構造上層模型。最終得到的模型包含兩個自變量：頻率和信道長度，從而可應用此模型同時預測不同頻率和不同長度條件下的信道特性。在設計的實驗中，結果表明該模型具有良好的精度，在10 kHz到60 MHz的頻率範圍內，最差的誤差為2.5 dB。除此之外，我們還在一個演示系統中對信道進行了測試，尤其是誤碼率（BER）和信號衰減情形。該測試結果也顯示出該模型所具有的良好預測性。第三，我們提出了一種關註QoS的WBAN系統優化方法。在IEEE標準中定義了數種不同存取模式（Access mode）和存取方式（Access method）。為了提高功率效率，我們著重研究了數據壓縮對系統總功率的影響，另外還對系統吞吐率建立了分析模型。仿真結果顯示，在一定條件下，數據壓縮對功耗降低具有良好功效，另外采用較高數據傳輸速率會對功耗存在改進作用。當數據壓縮模塊的壓縮率超過2倍，而功耗低於收發器的40%時，插入數據壓縮模塊可以確保整個系統消耗更低能源。 / Channel loss and maintaining the Quality of Service (QoS) are two of the major challenges in realizing an effective Wireless Body Area Network (WBAN). This thesis studies the body channel characteristics and proposes a methodology to improve energy efficiency for an entire WBAN system to achieve high throughput and low data latency. Three main contributions are made in this thesis. Firstly, we focus on human body channel (HBC). HBC, as a possible PHY layer for IEEE standards 802.15.6, has been found useful in networking on-body sensors. However, the HBC channel dynamics is not well understood and this is particularly the case when transceivers implanted inside a human body are involved. To this end, channel measurements were performed on real subjects under four different scenarios so that factors affecting channel quality could be identified. Secondly, a channel modelling methodology is proposed for body area network that takes into account the body structure and the dielectric properties of human tissues; this represents the first modelling effort to cover both in-body and on-body communications in vivo. The proposed modelling method composes of two phases: sub-model construction and top-level model construction. The constructed model is a function of two variables, frequency and channel length, enabling channel impedance prediction with respect to either frequency or channel length. Meanwhile, experimental results show that good model accuracy, a maximum error of 2.5 dB, can be achieved in frequencies range from 10 kHz to 60 MHz. In this endeavor, a modified HBC development system was used to measure bit error rate (BER) and signal attenuation during transmission. The measurements show a good match against simulation results and the channel model. Thirdly, a power optimization technique is proposed for the WBAN. The latest IEEE standard 802.15.6 defines several access modes and access methods together with new power management schemes and frame structures. To improve the power efficiency of a body area network, the merit of having data compression was investigated. For this purpose, an analytical model was developed to evaluate the power efficiency of a BAN system. Simulation results show that good power efficiency can be achieved by employing data compression. It is evident that higher data rate can also help improve energy efficiency. When the compression factor is larger than 2, better energy efficiency can be guaranteed by introducing a data processing unit in a sensor node as long as its power consumption is limited to 40% of that of the transceiver unit. / Detailed summary in vernacular field only. / Ai, Yanqing. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 126-135). / Abstracts also in Chinese.

Healthcare is changing, correction ... healthcare is in need of change. The population ageing, the increase in chronic and heart diseases and just the increase in population size will overwhelm the current hospital-centric healthcare. There is a growing interest by individuals to monitor their own physiology. Not only for sport activities, but also to control their own diseases. They are changing from the passive healthcare receiver to a proactive self-healthcare taker. The focus is shifting from hospital centred treatment to a patient-centric healthcare monitoring. Continuous, everyday, wearable monitoring and actuating is part of this change. In this setting, sensors that monitor the heart, blood pressure, movement, brain activity, dopamine levels, and actuators that pump insulin, 'pump' the heart, deliver drugs to specific organs, stimulate the brain are needed as pervasive components in and on the body. They will tend for people's need of self-monitoring and facilitate healthcare delivery. These components around a human body that communicate to sense and act in a coordinated fashion make a Body Area Network (BAN). In most cases, and in our view, a central, more powerful component will act as the coordinator of this network. These networks aim to augment the power to monitor the human body and react to problems discovered with this observation. One key advantage of this system is their overarching view of the whole network. That is, the central component can have an understanding of all the monitored signals and correlate them to better evaluate and react to problems. This is the focus of our thesis. In this document we argue that this multi-parameter correlation of the heterogeneous sensed information is not being handled in BANs. The current view depends exclusively on the applica- tion that is using the network and its understanding of the parameters. This means that every application will oversee the BAN's heterogeneous resources managing them directly without taking into consideration other applications, their needs and knowledge. There are several physiological correlations already known by the medical field. Correlating blood pressure and cross sectional area of blood vessels to calculate blood velocity, estimating oxygen delivery from cardiac output and oxygen saturation, are such examples. This knowledge should be available in a BAN and shared by the several applications that make use of the network. This architecture implies a central component that manages the knowledge and the resources. And this is, in our view, missing in BANs. Our proposal is a middleware layer that abstracts the underlying BAN's resources to the applica- tion, providing instead an information model to be queried. The model describes the correlations for producing new information that the middleware knows about. Naturally, the raw sensed data is also part of the model. The middleware hides the specificities of the nodes that constitute the BAN, by making available their sensed production. Applications are able to query for information attaching requirements to these requests. The middleware is then responsible for satisfying the requests while optimising the resource usage of the BAN.Our architecture proposal is divided in two corresponding layers, one that abstracts the nodes' hardware (hiding node's particularities) and the information layer that describes information available and how it is correlated. A prototype implementation of the architecture was done to illustrate the concept.

"Precise and accurate localization and motion classification is an emerging fundamental areas for scientific research and engineering developments. Such science and technology began from the broad out door area applications, and gradually grew into smaller and more complicated in-door area and more recently it is proceeding into in-body area networking for medical applications. Localization and motion classification technologies have their own specific challenges depending on the application and environment, which are left for scientists and engineers to overcome. One major challenge is that location estimation and motion classification often use hand-held devices or wearable sensors. Such devices and sensors usually work in indoor, near body environments and the human object has certain effects on the measurements. In that situation, existing mathematical models for general environments are no longer accurate and new models and analytical approaches are required to deal with the human body effects. This has opened opportunities for researchers to tackle a number of demanding problems. This dissertation focuses on three novel problems in localization and motion classification using radio propagation (RF) modeling, in and around the human body. (1) We develop an empirical Time-of-Arrival (TOA) ranging error model for radio propagation from body-mounted sensors to external access points, for human body tracking in indoor environment. This model reflects the effects of human angular motion on TOA ranging estimation, which enables accurate analysis for conventional TOA-based human tracking systems. (2) We use empirical data collected from a RF connection between a pair of body-mounted sensors to classify seven frequently appeared human body motions. This RF based classification approach has enabled health monitoring applications for first responders, hospital patient, and elderly care centers and in most of the situations it can replace the costly video base monitoring systems. (3) We use radio propagation models from body-mounted sensor to medical implants and the moving pattern of micro-robots inside the body to analyze the accuracy of hybrid localization inside the human body. This analysis demonstrates the feasibility of millimeter level of accurate localization inside the human body, which opens up possibilities for 3D reconstruction of the interior of human GI tract."

In Time-of-Arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line-of-sight (NLOS) scenario and result in significant ranging error. In this thesis, we measured the TOA ranging error in a typical indoor environment and analyzed sources of inaccuracy in TOAbased indoor localization system. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the on-body creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of Transmitter-Receiver, signal to noise ratio (SNR) and bandwidth of the system, which clearly shows the effects of human body on TOA ranging. An efficient way to fight against the TOA ranging error caused by human body is to employ site-specific channel models by using ray-tracing technology. However, existing ray-tracing softwares lack the propagation model that takes the effects of human body into account. To address that issue, this thesis presents a empirical model for near human body ultra-wideband (UWB) propagation channel that is valid for the frequency range from 3GHz to 8GHz. It is based on measurements conducted in a anechoic chamber which can be regarded as free space. The empirical model shows the joint propagation characteristics of the on body channel and the channel between body surface and external access point. It includes the loss of the first path, arrival time of the first path and the total pathloss. Models for all three aspects have been partitioned into two sections by a break point due to the geometrical property of human body and the creeping wave phenomenon. The investigation on first path behavior can be regarded as a theoretical basis of raytracing technique that takes the effects of human body into consideration.

This research is focused on identifying an appropriate design for a reconfigurable Body Area Network (BAN). In order to investigate the benefits and drawbacks of the proposed design, a BAN system prototype was built. This system consists of two distinct node types: a slave node and a master node. These nodes communicate using ZigBee radio transceivers. The microcontroller-based slave node acquires sensor data and transmits digitized samples to the master node. The master node is FPGA-based and runs uClinux on a soft-core microcontroller. The purpose of the master node is to receive, process and store digitized sensor data. In order to verify the operation of the BAN system prototype and demonstrate reconfigurability, a specific application was required. Pattern recognition in electrocardiogram (ECG) data was the application used in this work and the MIT-BIH Arrhythmia Database was used as the known data source for verification. A custom test platform was designed and built for the purpose of injecting data from the MIT-BIH Arrhythmia Database into the BAN system. The BAN system designed and built in this work demonstrates the ability to record raw ECG data, detect R-peaks, calculate and record R-R intervals, detect premature ventricular and atrial contractions. As this thesis will identify, many aspects of this BAN system were designed to be highly reconfigurable allowing it to be used for a wide range of BAN applications, in addition to pattern recognition of ECG data.

Localization inside the human body using radio frequency (RF) transmission is gaining importance in a number of applications such as Wireless Video Capsule Endoscopy. The accuracy of RF localization depends on the technology adopted for this purpose. The two most common RF localization technologies use received signal strength (RSS) and time-of-arrival (TOA). This research presents a comparison of the accuracy of TOA and RSS based localization inside human tissue using computational techniques for simulation of radio propagation inside human tissues. Computer simulation of the propagation of radio waves inside the human body is extremely challenging and computationally intensive. We designed a basic, MATLAB coded, finite difference time-domain (FDTD) for the radio propagation in and around the human body and compared the results obtained from this software with the commonly used and commercially available Finite Element Method (FEM) modeling in ANSYS HFSS. We first show that the FDTD analysis yields comparable results. Then we use the software to simulate the RSS and TOA of the wideband signals propagated inside the human body for RF localization to compare the accuracies of the two methods. We then develop a statistical TOA model using empirical data gathered from these simulations; and, in conjunction with pre-established mathematical models for RSS, we compare the accuracy of each technique with the Cramer-Rao Lower Bound (CRLB) commonly used for calculation of bounds for the performance of localization techniques and the effects of human body movements.

"Body area networks (BAN) is a technology gaining widespread attention for application in medical examination, monitoring and emergency therapy. The basic concept of BAN is monitoring a set of sensors on or inside the human body which enable transfer of vital parameters between the patient´s location and the physician in charge. As body area network has certain characteristics, which impose new demands on performance evaluation of systems for wireless access and localization for medical sensors. However, real-time performance evaluation and localization in wireless body area networks is extremely challenging due to the unfeasibility of experimenting with actual devices inside the human body. Thus, we see a need for a real-time hardware platform, and this thesis addressed this need. In this thesis, we introduced a unique hardware platform for performance evaluation of body area wireless access and in-body localization. This hardware platform utilizes a wideband multipath channel simulator, the Elektrobit PROPSimâ„¢ C8, and a typical medical implantable device, the Zarlink ZL70101 Advanced Development Kit. For simulation of BAN channels, we adopt the channel model defined for the Medical Implant Communication Service (MICS) band. Packet Reception Rate (PRR) is analyzed as the criteria to evaluate the performance of wireless access. Several body area propagation scenarios simulated using this hardware platform are validated, compared and analyzed. We show that among three modulations, two forms of 2FSK and 4FSK. The one with lowest raw data rate achieves best PRR, in other word, best wireless access performance. We also show that the channel model inside the human body predicts better wireless access performance than through the human body. For in-body localization, we focus on a Received Signal Strength (RSS) based localization algorithm. An improved maximum likelihood algorithm is introduced and applied. A number of points along the propagation path in the small intestine are studied and compared. Localization error is analyzed for different sensor positions. We also compared our error result with the Cramèr- Rao lower bound (CRLB), shows that our localization algorithm has acceptable performance. We evaluate multiple medical sensors as device under test with our hardware platform, yielding satisfactory localization performance."

Les avancées technologiques de ces dernières années, combinées au succès avéré et toujours croissant des communications sans fil, ont tout naturellement donné naissance à un nouveau type de réseaux sans fil, communément appelés Body Area networks. A terme, ces réseaux corporels sans fil doivent permettre à un ensemble de senseurs bio-médicaux répartis sur le corps humain de communiquer, soit pour échanger des informations en vue d'un traitement en temps réel du patient, soit pour enregistrer des données physiologiques en vue d'une analyse ultérieure. L’objectif de cette Thèse vise la réduction de la consommation énergétique au niveau des senseurs de sorte à leur garantir une autonomie de quelques mois, voire de quelques années. En réponse à cette contrainte énergétique, une association innovante de deux technologies émergentes est proposée, à savoir une combinaison des transmissions à ultra-large bande aux systèmes à multiples antennes. Une nouvelle architecture pour les réseaux corporels sans fil est donc envisagée pour laquelle les performances doivent être évaluées. Notre principale contribution à cet objectif consiste en la proposition d'une modélisation spatio-temporelle complète du canal de transmission dans le cadre de senseurs répartis autour du corps. Cette modélisation fait appel à la définition de nouveaux modèles, l'élaboration d'outils spécifiques d'extraction de paramètres et une compréhension fine des mécanismes de propagation liés à la proximité du corps humain. Ce manuscrit présente les résultats majeurs de nos recherches en cette matière.

Body Area Networks (BANs) are emerging as a convenient option for patient monitoring. They have shown potential in improving health care services through a network of external or implanted biosensors and actuators collecting real-time physiological data. Advancements in wireless networking and sensor development are expediting the adoption of BANs. However, real-time patient monitoring still remains a challenge due to network failures and congestion. In order to improve channel loss resilience and thus link availability, a multi-radio systems approach is adopted incorporating Bluetooth and Wi-Fi. In this work, we propose a multi-radio interface designed for a BAN to improve end-to-end communications. A multi-radio BAN controller is introduced to interface between the two wireless protocols (Wi-Fi and Bluetooth), control inter-radio handovers, manage a shared transmission buffer, and overall, route data accordingly through the protocol stacks. Simulations are conducted to study the performance of the system by adjusting handover timing and its effect on link availability. Advancing a handover has the benefit of a higher throughput at the cost of an increase in power consumption and timing overhead. Furthermore, various human mobility models, AP placement arrangements, and network densities are simulated to evaluate the performance of the BAN multi-radio interface. Sparse networks were found to have the most gain from the addition of the secondary Bluetooth radio system, as primary AP coverage was already very limited. Simulation results for various combinations of simulation parameters are presented to illustrate the improvement in BAN dependability through a multi-radio interface.

In this thesis, we propose a personal gateway for wireless body area network(WBAN). By using wireless communication and a proper WBAN topology, patients¡¦ physiological signal could be recorded without restricting their mobility. Moreover, integration of several kinds of signals from different sensor nodes in one data platform, personal gateway (PG), can reduce the redundant hardware of individual links as well as the complexity of WBAN. A device for long-term bladder urine pressure measurement is designed as a sensor node of PG. Not only is the design cost reduced, but also the reliability is enhanced by using a 1-atm canceling sensing IA (instrumentation amplifier). Because the urine pressure inside the bladder does not vary drastically, both the sleeping and working modes are required to save the battery power for the long-term observation. To integrate circuits with different supply voltages in PG, a 0.9/1.2/1.8/2.5/3.3/5.0 V wide-range I/O buffer carried out using a typical CMOS process is designed. An input buffer with a logic calibration circuit is used for receiving a low voltage signal. A novel floating N-well circuit is employed to remove the body effect at the output PMOS. Moreover, a dynamic driving detector is included to equalize the turn-on voltages for the output PMOS and NMOS transistors. ZigBee is used as a communication channel in this thesis because of its features, including low power, low complexity, medium range, and medium data rate. The 868/915 MHz mode has lower cost and power consumption than those of 2.4 GHz mode, and the data rate is far enough for WBAN applications. Moreover, lower carrier frequency causes less unnecessary power absorbed by human tissue. Therefore, the ZigBee tranceiver with 868/915 MHz mode is explored. A low power all digital phase lock loop (ADPLL) using a controller which employs a binary frequency searching method is also proposed as a clock generator of PG. Glitch hazards and timing violations which occurred very often in prior ADPLLs are avoided by a novel control method and a new digital-controlled oscillator (DCO) with multiplexers. Besides, the feedback DCO is disabled half a cycle in every two cycles so as to reduce 25% of dynamic power theoretically.