A comprehensive review of wireless body area network

Recent development and advancement of information and communication technologies facilitate people in different dimensions of life. Most importantly, in the healthcare industry, this has become more and more involved with the information and communication technology-based services. One of the most important services is monitoring of remote patients, that enables the healthcare providers to observe, diagnose and prescribe the patients without being physically present. The advantage of miniaturization of sensor technologies gives the flexibility of installing in, on or off the body of patients, which is capable of forwarding physiological data wirelessly to remote servers. Such technology is named as Wireless Body Area Network (WBAN). In this paper, WBAN architecture, communication technologies for WBAN, challenges and different aspects of WBAN are illustrated. This paper also describes the architectural limitations of existing WBAN communication frameworks. blueFurthermore, implementation requirements are presented based on IEEE 802.15.6 standard. Finally, as a source of motivation towards future development of research incorporating Software Defined Networking (SDN), Energy Harvesting (EH) and Blockchain technology into WBAN are also provided

IoT-Based Secure Healthcare Framework Using Blockchain Technology with A Novel Simplified Swarm-Optimized Bayesian Normalized Neural Networks

The Internet of Things (IoT) is growing in popularity nowadays and has many potential uses, particularly in healthcare. A large amount of sensing data is generated from a variety of sensing devices as a result of the growing needs of the IoT. The use of artificial intelligence (AI) methods is crucial for real-time, scalable, and accurate data processing. However, there are certain obstacles in the way of the layout and implementation of a successful analysis of the big data approach. These include a lack of suitable training data, resource limits, and a centralized architecture for the data. However, emerging blockchain technology provides a distributed system. It is advocated for getting rid of centralized control and solving AI issues, and it allows for safe data and resource exchange across the many nodes of the IoT network. Thus, this research develops a novel simplified swarm-optimized Bayesian normalized neural network (SSO-BNNN) for the secret transmission of medical images to address the aforementioned challenge. The neighborhood indexing sequence (NIS) approach is also used to encrypt the hash value. Several experiments were conducted to verify the outcomes of the suggested approach, and several facets of those results are discussed. Experimental results show that the proposed excellent findings with the best accuracy, sensitivity, and specificity were produced by the model

Low Latency Protocols Investigation for Event-Driven Wireless Body Area Networks

Nowadays distributed electronic health and fitness monitoring are hot-topics in bio-engineering, however common solutions for Wireless Body Area Networks (WBANs) featuring high-density sampled data transmission still stumbles over the trade-off among data rate, application throughput, and latency. Therefore, the Bluetooth Low Energy (BLE) and the IEEE 802.15.4 protocols are here investigated, with the aim of developing an event-driven WBAN to support a threshold-crossing surface ElectroMyoGraphy (sEMG) acquisition approach. We then implemented a custom protocol to overcome their limitations and fulfil all the requirements, resulting in a transmission latency of 0.856 ms ± 1 µs and enabling a functional operating time up to 110 h

Efficient energy for one node and multi-nodes of wireless body area network

Compression sensing approaches have been used extensively with the idea of overcoming the limitations of traditional sampling theory and applying the concept of pressure during the sensing procedure. Great efforts have been made to develop methods that would allow data to be sampled in compressed form using a much smaller number of samples. Wireless body area networks (WBANs) have been developed by researchers through the creation of the network and the use of miniature equipment. Small structural factors, low power consumption, scalable data rates from kilobits per second to megabits per second, low cost, simple hardware deployment, and low processing power are needed to hold the wireless sensor through lightweight, implantable, and sharing communication tools wireless body area network. Thus, the proposed system provides a brief idea of the use of WBAN using IEEE 802.15.4 with compression sensing technologies. To build a health system that helps people maintain their health without going to the hospital and get more efficient energy through compression sensing, more efficient energy is obtained and thus helps the sensor battery last longer, and finally, the proposed health system will be more efficient energy, less energy-consuming, less expensive and more throughput

Ultra-wideband CMOS power amplifier for wireless body area network applications: a review

A survey on ultra-wideband complementary metal-oxide semiconductor (CMOS) power amplifiers for wireless body area network (WBAN) applications is presented in this paper. Formidable growth in the CMOS integrated circuits technology enhances the development in biomedical manufacture. WBAN is a promising mechanism that collects essential data from wearable sensors connected to the network and transmitted it wirelessly to a central patient monitoring station. The ultra-wideband (UWB) technology exploits the frequency band from 3.1 to 10.6 GHz and provides no interference to other communication systems, low power consumption, low-radiated power, and high data rate. These features permit it to be compatible with medical applications. The demand target is to have one transceiver integrated circuit (IC) for WBAN applications, consequently, UWB is utilized to decrease the hardware complexity. The power amplifier (PA) is the common electronic device that employing in the UWB transmitter to boost the input power to the desired output power and then feed it to the antenna of the transmitter. The advance in the design and implementation of ultra-wideband CMOS power amplifiers enhances the performance of the UWB-transceivers for WBAN applications. A review of recently published CMOS PA designs is reported in this paper with comparison tables listing wideband power amplifiers' performance

Particle Swarm Optimization for Interference Mitigation of Wireless Body Area Network: A Systematic Review

Wireless body area networks (WBAN) has now become an important technology in supporting services in the health sector and several other fields. Various surveys and research have been carried out massively on the use of swarm intelligent (SI) algorithms in various fields in the last ten years, but the use of SI in wireless body area networks (WBAN) in the last five years has not seen any significant progress. The aim of this research is to clarify and convince as well as to propose a answer to this problem, we have identified opportunities and topic trends using the particle swarm optimization (PSO) procedure as one of the swarm intelligence for optimizing wireless body area network interference mitigation performance. In this research, we analyzes primary studies collected using predefined exploration strings on online databases with the help of Publish or Perish and by the preferred reporting items for systematic reviews and meta-analysis (PRISMA) way. Articles were carefully selected for further analysis. It was found that very few researchers included optimization methods for swarm intelligence, especially PSO, in mitigating wireless body area network interference, whether for intra, inter, or cross-WBAN interference. This paper contributes to identifying the gap in using PSO for WBAN interference and also offers opportunities for using PSO both standalone and hybrid with other methods to further research on mitigating WBAN interference

Energy efficiency considerations in software‐defined wireless body area networks

Wireless body area networks (WBAN) provide remote services for patient monitoring which allows healthcare practitioners to diagnose, monitor, and prescribe them without their physical presence. To address the shortcomings of WBAN, software-defined networking (SDN) is regarded as an effective approach in this prototype. However, integrating SDN into WBAN presents several challenges in terms of safe data exchange, architectural framework, and resource efficiency. Because energy expenses account for a considerable portion of network expenditures, energy efficiency has to turn out to be a crucial design criterion for modern networking methods. However, creating energy-efficient systems is difficult because they must balance energy efficiency with network performance. In this article, the energy efficiency features are discussed that can widely be used in the software-defined wireless body area network (SDWBAN). A comprehensive survey has been carried out for various modern energy efficiency models based on routing algorithms, optimization models, secure data delivery, and traffic management. A comparative assessment of all the models has also been carried out for various parameters. Furthermore, we explore important concerns and future work in SDWBAN energy efficiency

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

Diseño de un prototipo de monitoreo y diagnóstico remoto de arritmias cardiacas, basado en el procesamiento de señales con deep learning

Desarrollar un prototipo remoto de monitoreo y diagnóstico de arritmias cardiacas, mediante la técnica del procesamiento de señales con DEEP LEARNING para facilitar la detección de anomalías cardiacas.En esta investigación se presenta el diseño de un prototipo de monitoreo y diagnostico remoto de arritmias cardiacas basado en redes neuronales, para lo cual, se emplea la metodología en V que se comprende por las fases de verificación, codificación y validación. La fase de verificación consiste la descripción a detalle de los requerimientos del sistema, donde, se considera como referencia el estándar ISO/IEC / IEEE 29148: 2011 (ISO/IEC/IEEE, 2011). Posterior a la descripción de requerimientos del sistema se procede a realizar la selección de los componentes de hardware y software. En este sentido, para seleccionar cada componente se realiza una comparación técnica de varias opciones posibles y se evalúa según su adaptación a los requerimientos deseados. Luego se programa la técnica de Deep Learning, para lo cual, se empleó la base datos de arritmias cardiacas de la MIT-BIH, considerando tres patologías específicas, bradicardia, taquicardia y estado normal. Las pruebas físicas de funcionamiento se realizan en base a una ficha de cumplimiento de características, donde se detalla el tipo de prueba y los posibles fallos que se presentaron en cada prueba. Asimismo, el sistema se prueba con 8 pacientes, que presentan patologías asociadas al corazón, bajo la supervisión del doctor Fernando Cruz Cevallos. Por último, el sistema demuestra ser muy completo para ser usado por el profesional de la salud ya que aparte de recibir y detectar de forma correcta las arritmias cardiacas como bradicardia, taquicardia y la frecuencia cardiaca normal, este almacena los datos de una forma organizada, lo que facilita el seguimiento y control médico del paciente, además, su efectividad es del 100%.Ingenierí