Impact of Indoor Environment on Path Loss in Body Area Networks

In this paper the influence of an example indoor environment on narrowband radio channel path loss for body area networks operating around 2.4 GHz is investigated using computer simulations and on-site measurements. In contrast to other similar studies, the simulation model included both a numerical human body phantom and its environment—room walls, floor and ceiling. As an example, radio signal attenuation between two different configurations of transceivers with dipole antennas placed in a direct vicinity of a human body (on-body scenario) is analyzed by computer simulations for several types of reflecting environments. In the analyzed case the propagation environments comprised a human body and office room walls. As a reference environment for comparison, free space with only a conducting ground plane, modelling a steel mesh reinforced concrete floor, was chosen. The transmitting and receiving antennas were placed in two on-body configurations chest–back and chest–arm. Path loss vs. frequency simulation results obtained using Finite Difference Time Domain (FDTD) method and a multi-tissue anthropomorphic phantom were compared to results of measurements taken with a vector network analyzer with a human subject located in an average-size empty cuboidal office room. A comparison of path loss values in different environments variants gives some qualitative and quantitative insight into the adequacy of simplified indoor environment model for the indoor body area network channel representation

OFDM over IEEE 802.11b hardware for telemedical applications

Using a wireless Local Area Network (WLAN) to transmit live high-quality video suitable for a telemedical application presents many challenges, including ensuring sufficient Quality of Service (QoS) for the end-user to be able to make an accurate diagnosis. One of the many problems that exist when developing such a system is the multipath effect caused by the reflections of the transmitted signals on various surfaces including walls, floors, furniture and people. This degrades the signal quality and reduces the amount of available bandwidth and, thus, the quality of the image. Presently, most of Europe is using the IEEE 802.11b hardware for such applications. As an alternative to the existing modulation of 802.11b, Orthogonal Frequency Division Multiplexing (OFDM) is investigated, especially for use inside hospitals. The advantages of using this modulation over IEEE 802.11b hardware for a telemedicine application are examined by means of simulation using three different simulation packages

SNR-based evaluation of coexistence in wireless system of hospital

Abstract. The wireless system (IEEE Std. 802.11) of North Karelian Central Hospital (NKCH) has been studied in the newly opened J2 building of the hospital. The measurements have been carried out using Ekahau Sidekick spectrum analyser and Ekahau Pro software. Signal propagation has been modelled in the control ward of the Emergency department because many coexisting systems are used with critical requirements of data communication over there. The analytical models have been developed to understand the radio-frequency (RF) signal propagation in the entire building. Measurements have also been carried out on the entire first floor, in the Department of the Abdominal Diseases on the ground floor and in the Children’s wards on the third floor. The multi-slope path-loss propagation models with shadowing have been generated based on the Received Signal Strength Indicator (RSSI) measurements for typical hospital environment at the 2.4 GHz and 5 GHz Industrial, Scientific, and Medical (ISM) band. The measurements have been carried out within the two predefined routes. The models have also been compared to the empirically derived path-loss models. The probability of signal outage has been calculated for both measured routes. The aggregate interference has been measured within the routes that cover the area where remarkable signal variations and the high level of interference has been indicated based on the heatmaps of Ekahau. The use of Ekahau Sidekick and Ekahau Pro software in the coexistence study has been described. The noise floor has been determined based on the averaged values of the six measurement campaigns. The local changes in signal strength of the desired signal and aggregated power of interference have been studied. The Signal-to-Interference Ratio (SIR) models have been generated within the measured routes. The rapid decreases of Signal-to-Noise Ratio (SNR) have been indicated on all measured floors of building J2. They have been studied and their effect on the network performance has been evaluated. The evaluation has been done by comparing the measured values of RSSI, SNR and SIR to the requirements of the respective Modulation and Coding Scheme (MCS). The link margins have been calculated based on the chosen bit error probability and the given SNR requirement of the respective MCS. The comparison between the measured RSSI readings and the required threshold of the respective MCS has been done using the defined shadowing as a link margin. It has been shown that the measured difference between the signal strength of the 2.4 GHz and 5 GHz bands has been caused by the reduced transmit power at the 2.4 GHz band. Based on the SIR measurements, it has been shown that the access points of the neighbouring building have contributed locally to the measured aggregate interference in the Control ward. However, the primary reason for the decrease of SIR at the 2.4 GHz band has been the decrease of desired signal power that has been contributed by the above mentioned reduced transmit power. The strong SNR drops have been indicated on every measured floor before the roaming has occurred.Sairaalan langattoman järjestelmän arviointi signaali-kohina-suhteen avulla. Tiivistelmä. Tässä diplomityössä on tutkittu Pohjois-Karjalan keskussairaalan (PKKS) langatonta verkkoa (IEEE Std. 802.11) äskettäin avatussa sairaalan laajennusosassa (J2-rakennus). Mittaukset on toteutettu käyttäen Ekahau Sidekick spektrianalysaattoria ja Ekahau Pro -ohjelmaa. Päivystyksen valvontaosasto on valittu tutkimuskohteeksi, koska siellä käytetään paljon eri teknologioihin perustuvia järjestelmiä, joiden välinen tiedonsiirto on luonteeltaan kriittistä. Luotujen mallien avulla rakennuksen langatonta toimintaympäristöä tutkitaan RF-järjestelmän (Radio-Frequency) näkökulmasta myös muissa mittausten kohteina olleissa tiloissa. Mittauksia on tehty myös valvontaosaston ulkopuolella 1. kerroksessa sekä 3. kerroksen lastenosastoilla ja Vatsakeskuksen tiloissa pohjakerroksessa. RSSI-mittausten perusteella on luotu radiotiehäviöihin perustuvat etenemismallit molemmilla käytössä olevilla ISM-taajuuskaistoilla (Industrial, Scientific and Medical bands). Varjostuminen ja etenemishäviökertoimen muutokset on otettu huomioon etenemismalleissa. Mittaukset on suoritettu ennalta määritellyillä reiteillä. Luotuja malleja on verrattu myös tutkimuskirjallisuudessa esitettyihin, empiirisesti johdettuihin etenemishäviömalleihin. Signaalikatkoksen todennäköisyys on laskettu molemmille reiteille 2.4 GHz:n taajuuskaistalla. Vastaanotetun häiriötehon summa on mitattu koko mallinnettavan tilan alueelle ulottuvien mittausreittien pohjalta. Mittausreitit on määritelty Ekahau Pron tuottamien kuuluvuus- ja häiriökarttojen avulla ottaen huomioon havaitut signaalitason vaihtelut. Ekahau Sidekick -spektrianalysaattorin ja Ekahau Pro -ohjelman käyttöä on kuvattu tämän tutkimuksen kontekstissa. Kohinataso on määritelty kaikissa kuudessa mittauskampanjassa mitattujen kohina-tehoarvojen keskiarvona. Paikallisten hyötysignaalinvoimakkuus- ja häiriötehovaihteluiden vaikutusta verkon suorituskykyyn on tutkittu ja molemmat mittausreitit kattavat SIR-mallit (Signal-to-Interference Ratio) on luotu. Kaikissa tutkituissa kerroksissa havaittuja äkillisiä signaali-kohinasuhteen vaihteluita on tutkittu ja niiden vaikutusta järjestelmän suorituskykyyn on arvioitu. Mitattujen hyöty- ja häiriösignaalivaihteluiden arviointi on toteutettu vertaamalla mittaamalla saatuja SNR- (Signal-to-Noise ratio), SIR- ja RSSI-arvoja (Received Signal Strength Indicator) eri tiedonsiirtonopeuksia käyttävien MCS-indeksien vaatimiin signaalinvoimakkuus- ja signaali-kohina-suhteen arvoihin. Kynnysarvoille on laskettu linkkimarginaalit käyttäen mitoitusvaatimuksena valittua bittivirhetodennäköisyyden arvoa. Mitattuja RSSI-arvoja on verrattu käyttäen linkkimarginaalina etenemismallinnuksessa määritettyjä varjostumisvaikutuksen arvoja. 2.4 ja 5 GHz:n taajuusalueiden välillä mitatun signaalinvoimakkuuseron on tutkimuksessa saatujen tulosten perusteella osoitettu olevan seurausta alennetusta lähetystehosta 2.4 GHz:n kaistalla. SIR-mittausten perusteella on todettu viereisen rakennuksen tukiasemien kasvattaneen vastaanotettua häiriötehosummaa valvontaosastolla paikallisesti. Ensisijainen syy mitattuihin SIR-arvojen vaihteluihin ovat kuitenkin alhainen signaalinvoimakkuus 2.4 GHz:n kaistalla, mikä osittain johtuu edellä kuvatusta alennetusta lähetystehosta. Voimakkaita SNR-vaihteluita on mitattu kaikissa kerroksissa ennen kuin päätelaite kytkeytyy uuteen tukiasemaan

Antennas and Propagation of Implanted RFIDs for Pervasive Healthcare Applications

© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10.1109/JPROC.2010.205101

사물인터넷을 위한 무선 실내 측위 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 김성철.실내 위치 기반 서비스는 스마트폰을 이용한 실내에서의 경로안내, 스마트 공장에서의 자원 관리, 실내 로봇의 자율주행 등 많은 분야에 접목될 수 있으며, 사물인터넷 응용에도 필수적인 기술이다. 다양한 위치 기반 서비스를 구현하기 위해서는 정확한 위치 정보가 필요하며, 적합한 거리 및 위치를 추정 기술이 핵심적이다. 야외에서는 위성항법시스템을 이용해서 위치 정보를 획득할 수 있다. 본 학위논문에서는 와이파이 기반 측위 기술에 대해 다룬다. 구체적으로, 전파의 신호 세기 및 도달 시간을 이용한 정밀한 실내 위치 추정을 위한 세 가지 기술에 대해 다룬다. 먼저, 비가시경로 환경에서의 거리 추정 정확도를 향상시켜 거리 기반 측위의 정확도를 향상시키는 하이브리드 알고리즘을 제안한다. 제안하 알고리즘은듀얼 밴드 대역의 신호세기를 감쇄량을 측정하여 거리 기반 측위 기법을 적용할 때, 거리 추정부 단계만을 데이터 기반 학습을 이용한 깊은 신경망 회귀 모델로 대체한 방안이다. 적절히 학습된 깊은 회귀 모델의 사용으로 비가시경로 환경에서 발생하는 거리 추정 오차를 효과적으로 감소시킬 수 있으며, 결과적으로 위치 추정 오차 또한 감소시켰다. 제안한 방법을 실내 광선추적 기반 모의실험으로 평가했을 때, 기존 기법들에 비해서 위치 추정 오차를 중간값을 기준으로 22.3% 이상 줄일 수 있음을 검증했다. 추가적으로, 제안한 방법은 실내에서의 AP 위치변화 등에 강인함을 확인했다. 다음으로, 본 논문에서는 비가시경로에서 단일 대역 수신신호세기를 측정했을 때 비가시경로가 많은 실내 환경에서 위치 추정 정확도를 높이기 위한 방안을 제안한다. 단일 대역 수신신호세기를 이용하는 방안은 기존에 이용되는 와이파이, 블루투스, 직비 등의 기반시설에 쉽게 적용될 수 있기 때문에 널리 이용된다. 하지만 신호 세기의 단일 경로손실 모델을 이용한 거리 추정은 상당한 오차를 지녀서 위치 추정 정확도를 감소시킨다. 이러한 문제의 원인은 단일 경로손실 모델로는 실내에서의 복잡한 전파 채널 특성을 반영하기 어렵기 때문이다. 본 연구에서는 실내 위치 추정을 위한 목적으로, 중첩된 다중 상태 경로 감쇄 모델을 새롭게 제시한다. 제안한 모델은 가시경로 및 비가시경로에서의 채널 특성을 고려하여 잠재적인 후보 상태들을 지닌다. 한 순간의 수신 신호 세기 측정치에 대해 각 기준 기지국별로 최적의 경로손실 모델 상태를 결정하는 효율적인 방안을 제시한다. 이를 위해 기지국별 경로손실모델 상태의 조합에 따른 측위 결과를 평가할 지표로서 비용함수를 정의하였다. 각 기지국별 최적의 채널 모델을 찾는데 필요한 계산 복잡도는 기지국 수의 증가에 따라 기하급수적으로 증가하는데, 유전 알고리즘을 이용한 탐색을 적용하여 계산량을 억제하였다. 실내 광선추적 모의실험을 통한 검증과 실측 결과를 이용한 검증을 진행하였으며, 제안한 방안은 실제 실내 환경에서 기존의 기법들에 비해 위치 추정 오차를 약 31% 감소시켰으며 평균적으로 1.92 m 수준의 정확도를 달성함을 확인했다. 마지막으로 FTM 프로토콜을 이용한 실내 위치 추적 알고리즘에 대해 연구하였다. 스마트폰의 내장 관성 센서와 와이파이 통신에서 제공하는 FTM 프로토콜을 통한 거리 추정을 이용하여 실내에서 사용자의 위치를 추적할 수 있다. 하지만 실내의 복잡한 다중경로 환경으로 인한 피크 검출 실패는 거리 측정치에 편향성을 유발한다. 또한 사용하는 디바이스의 종류에 따라 예상치 못한 거리 오차가 발생할 수있다. 본 논문에서는 실제 환경에서 FTM 거리 추정을 이용할 때 발생할 수 있는 오차들을 고려하고 이를 보상하는 방안에 대해 제시한다. 확장 칼만 필터와 결합하여 FTM 결과를 사전필터링 하여 이상값을 제거하고, 거리 측정치의 편향성을 제거하여 위치 추적 정확도를 향상시킨다. 실내에서의 실험 결과 제안한 알고리즘은 거치 측정치의 편향성을 약 44-65% 감소시켰으며 최종적으로 사용자의 위치를 서브미터급으로 추적할 수 있음을 검증했다.Indoor location-based services (LBS) can be combined with various applications such as indoor navigation for smartphone users, resource management in smart factories, and autonomous driving of robots. It is also indispensable for Internet of Things (IoT) applications. For various LBS, accurate location information is essential. Therefore, a proper ranging and positioning algorithm is important. For outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. It is necessary to develop a technology that can replace GNSS in GNSS-denied environments. Among the various alternative systems, the one of promising technology is to use a Wi-Fi system that has already been applied to many commercial devices, and the infrastructure is in place in many regions. In this dissertation, Wi-Fi based indoor localization methods are presented. In the specific, I propose the three major issues related to accurate indoor localization using received signal strength (RSS) and fine timing measurement (FTM) protocol in the 802.11 standard for my dissertation topics. First, I propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. I replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error. Next, I study on positioning algorithm that considering NLOS conditions for each APs, using single band RSS measurement. The single band RSS information is widely used for indoor localization because they can be easily implemented by using existing infrastructure like Wi-Fi, Blutooth, or Zigbee. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, I develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and NLOS states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, I formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, I apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92~m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09\% over the benchmark scenario. Finally, I investigate accurate indoor tracking algorithm using FTM protocol in this dissertation. By using the FTM ranging and the built-in sensors in a smartphone, it is possible to track the user's location in indoor. However, the failure of first peak detection due to the multipath effect causes a bias in the FTM ranging results in the practical indoor environment. Additionally, the unexpected ranging error dependent on device type also degrades the indoor positioning accuracy. In this study, I considered the factors of ranging error in the FTM protocol in practical indoor environment, and proposed a method to compensate ranging error. I designed an EKF-based tracking algorithm that adaptively removes outliers from the FTM result and corrects bias to increase positioning accuracy. The experimental results verified that the proposed algorithm reduces the average ofthe ranging bias by 43-65\% in an indoor scenarios, and can achieve the sub-meter accuracy in average route mean squared error of user's position in the experiment scenarios.Abstract i Contents iv List of Tables vi List of Figures vii 1 INTRODUCTION 1 2 Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-BandWi-Fi 6 2.1 Motivation 6 2.2 Preliminary 8 2.3 System model 11 2.4 Proposed Ranging Method 13 2.5 Performance Evaluation 16 2.5.1 Ray-Tracing-Based Simulation 16 2.5.2 Analysis of the Ranging Accuracy 21 2.5.3 Analysis of the Neural Network Structure 25 2.5.4 Analysis of Positioning Accuracy 26 2.6 Summary 29 3 Genetic Algorithm for Path Loss Model Selection in Signal Strength Based Indoor Localization 31 3.1 Motivation 31 3.2 Preliminary 34 3.2.1 RSS-based Ranging Techniques 35 3.2.2 Positioning Technique 37 3.3 Proposed localization method 38 3.3.1 Localization Algorithm with Overlapped Multi-State Path Loss Model 38 3.3.2 Localization with Genetic Algorithm-Based Search 41 3.4 Performance evaluation 46 3.4.1 Numerical simulation 50 3.4.2 Experimental results 56 3.5 Summary 60 4 Indoor User Tracking with Self-calibrating Range Bias Using FTM Protocol 62 4.1 Motivation 62 4.2 Preliminary 63 4.2.1 FTM ranging 63 4.2.2 PDR-based trajectory estimation 65 4.3 EKF design for adaptive compensation of ranging bias 66 4.4 Performance evaluation 69 4.4.1 Experimental scenario 69 4.4.2 Experimental results 70 4.5 Summary 75 5 Conclusion 76 Abstract (In Korean) 89박

Development and Experimental Analysis of Wireless High Accuracy Ultra-Wideband Localization Systems for Indoor Medical Applications

This dissertation addresses several interesting and relevant problems in the field of wireless technologies applied to medical applications and specifically problems related to ultra-wideband high accuracy localization for use in the operating room. This research is cross disciplinary in nature and fundamentally builds upon microwave engineering, software engineering, systems engineering, and biomedical engineering. A good portion of this work has been published in peer reviewed microwave engineering and biomedical engineering conferences and journals. Wireless technologies in medicine are discussed with focus on ultra-wideband positioning in orthopedic surgical navigation. Characterization of the operating room as a medium for ultra-wideband signal transmission helps define system design requirements. A discussion of the first generation positioning system provides a context for understanding the overall system architecture of the second generation ultra-wideband positioning system outlined in this dissertation. A system-level simulation framework provides a method for rapid prototyping of ultra-wideband positioning systems which takes into account all facets of the system (analog, digital, channel, experimental setup). This provides a robust framework for optimizing overall system design in realistic propagation environments. A practical approach is taken to outline the development of the second generation ultra-wideband positioning system which includes an integrated tag design and real-time dynamic tracking of multiple tags. The tag and receiver designs are outlined as well as receiver-side digital signal processing, system-level design support for multi-tag tracking, and potential error sources observed in dynamic experiments including phase center error, clock jitter and drift, and geometric position dilution of precision. An experimental analysis of the multi-tag positioning system provides insight into overall system performance including the main sources of error. A five base station experiment shows the potential of redundant base stations in improving overall dynamic accuracy. Finally, the system performance in low signal-to-noise ratio and non-line-of-sight environments is analyzed by focusing on receiver-side digitally-implemented ranging algorithms including leading-edge detection and peak detection. These technologies are aimed at use in next-generation medical systems with many applications including surgical navigation, wireless telemetry, medical asset tracking, and in vivo wireless sensors

Empirical RF Propagation Modeling of Human Body Motions for Activity Classification

Many current and future medical devices are wearable, using the human body as a conduit for wireless communication, which implies that human body serves as a crucial part of the transmission medium in body area networks (BANs). Implantable medical devices such as Pacemaker and Cardiac Defibrillators are designed to provide patients with timely monitoring and treatment. Endoscopy capsules, pH Monitors and blood pressure sensors are used as clinical diagnostic tools to detect physiological abnormalities and replace traditional wired medical devices. Body-mounted sensors need to be investigated for use in providing a ubiquitous monitoring environment. In order to better design these medical devices, it is important to understand the propagation characteristics of channels for in-body and on- body wireless communication in BANs. The IEEE 802.15.6 Task Group 6 is officially working on the standardization of Body Area Network, including the channel modeling and communication protocol design. This thesis is focused on the propagation characteristics of human body movements. Specifically, standing, walking and jogging motions are measured, evaluated and analyzed using an empirical approach. Using a network analyzer, probabilistic models are derived for the communication links in the medical implant communication service band (MICS), the industrial scientific medical band (ISM) and the ultra- wideband (UWB) band. Statistical distributions of the received signal strength and second order statistics are presented to evaluate the link quality and outage performance for on-body to on- body communications at different antenna separations. The Normal distribution, Gamma distribution, Rayleigh distribution, Weibull distribution, Nakagami-m distribution, and Lognormal distribution are considered as potential models to describe the observed variation of received signal strength. Doppler spread in the frequency domain and coherence time in the time domain from temporal variations is analyzed to characterize the stability of the channels induced by human body movements. The shape of the Doppler spread spectrum is also investigated to describe the relationship of the power and frequency in the frequency domain. All these channel characteristics could be used in the design of communication protocols in BANs, as well as providing features to classify different human body activities. Realistic data extracted from built-in sensors in smart devices were used to assist in modeling and classification of human body movements along with the RF sensors. Variance, energy and frequency domain entropy of the data collected from accelerometer and orientation sensors are pre- processed as features to be used in machine learning algorithms. Activity classifiers with Backpropagation Network, Probabilistic Neural Network, k-Nearest Neighbor algorithm and Support Vector Machine are discussed and evaluated as means to discriminate human body motions. The detection accuracy can be improved with both RF and inertial sensors

Experimental Determination of Penetration Loss into Multi-Storey Buildings at 900 and 1800MHz

This study presents building pentration loss into and around multi-storey buildings at 900 and 1800MHz based on experimental data obtained through drive test, using Test Mobile System (TEMS) investigation tools. The received signal level was measured inside and outside three buildings; the Senate building of the University of Lagos (B1), Mike Adenuga Towers (B2) and the Sapetro Towers (B3) located in Victoria Island, Lagos Nigeria. The building penetration loss (BPL) was derived from measurements, and the average and standard deviations of the BPL were computed. Results showed that the average BPL of 17.0dB and 13.8dB obtained from building B1 at 900 and 1800MHz, respectively, are comparatively higher than those of buildings B2 and B3. The standard deviation of the BPL shows an increase from 5.2dB at 900MHz to 7.8dB at 1800MHz for building B1, whereas it fell drastically from 8.65dB at 900MHz to 1.40dB at 1800MHz for B2, and a similar behaviour in B1 is seen for building B3 where it rises sharply from 1.55dB at 900MHz to 6.55dB at 1800MHz. This is in agreement with the general trend of increasing penetration loss with increase in frequency except for building B2 where an anomaly is observed. In order to examine the correlation between the measured and the predicted BPL, cubic regression was used to fit a third order polynomial to the measured BPL. Overrall, the fitted models could find useful applications in the design of novel and robust BPL models for modern multi-floored buildings