133 research outputs found

    Design and stochastic analysis of emerging large-scale wireless-powered sensor networks

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    Premi Extraordinari de Doctorat, promoció 2016-2017. Àmbit d’Enginyeria de les TICUndeniably, the progress in wireless networks during the last two decades is extraordinary. However, the ever-increasing upward trend in the numbers of wireless devices that will overwhelm every field of our everyday life, e.g., building automation, traffic management, health-care, etc., will introduce several issues in terms of communication and energy provision that need to be handled in advance. Regarding the communication issues, it is imperative to ensure the correct operation of the vast collection of nodes, especially for life-critical applications. Two well-known metrics that can characterize sufficiently the network reliability are the coverage and the connectivity probability that are derived by taking into account the network topology, the channel conditions between every transmitter-receiver pair, and the interference from other nodes. Nevertheless, considering all those factors is not straightforward. Lately, stochastic geometry has come into prominence, which is a mathematical tool to study the average network performance over many spatial realizations, while considering all aforementioned factors. Moreover, the other crucial issue for the large-scale dense network deployments of the future is their energy supply. Traditional battery charging or swapping for the wireless devices is both inconvenient and harms the environment, especially if we take into account the enormous numbers of nodes. Therefore, novel solutions have to be found using renewable energy sources to zero down the significant electricity consumption. Wireless energy harvesting is a convenient and environmentally-friendly approach to prolong the lifetime of networks by harvesting the energy from radio-frequency (RF) signals and converting it to direct current electricity through specialized hardware. The RF energy could be harvested from signals generated in the same or other networks. However, if the amount of harvested energy is not sufficient, solar-powered dedicated transmitters could be employed. In this way, we can achieve a favorable outcome by having both a zero-energy network operation and convenience in the charging of the wireless devices. Still, extensive investigation should be done in order to ensure that the communication performance is not affected. To that end, in this thesis, we study the communication performance in large-scale networks using tools from stochastic geometry. The networks that we study comprise wireless devices that are able to harvest the energy of RF signals. In the first part of the thesis, we present the effects of wireless energy harvesting from the transmissions of the cooperative network on the coverage probability and the network lifetime. In the second part of the thesis, we first employ batteryless nodes that are powered by dedicated RF energy transmitters to study the connectivity probability. Then, we assume that the dedicated transmitters are powered by solar energy to study the connectivity in a clustered network and investigate, for the first time, the reliability of zero-energy networks. Finally, we conclude the thesis by providing insightful research challenges for future works.Innegablemente, el progreso en las redes inalámbricas durante las últimas dos décadas es extraordinario. Sin embargo, la creciente tendencia al alza en el número de dispositivos inalámbricos que abarcarán todos los ámbitos de nuestra vida cotidiana, como la automatización de edificios, la gestión del tráfico, la atención sanitaria, etc., introducirá varias cuestiones en términos de comunicación y suministro de energía que se debe tener en cuenta con antelación. Respecto a los problemas de comunicación, es imprescindible asegurar el correcto funcionamiento de la vasta colección de nodos, especialmente para las aplicaciones vitales. Dos métricas bien conocidas que pueden caracterizar suficientemente la fiabilidad de la red son la probabilidad de cobertura y la de conectividad, que se derivan teniendo en cuenta la topología de la red, las condiciones del canal entre cada par transmisor-receptor y la interferencia de otros nodos. Sin embargo, considerar todos esos factores no es sencillo. Últimamente, la geometría estocástica ha llegado a la prominencia como un metodo de análisis, que es una herramienta matemática para estudiar el rendimiento promedio de la red sobre muchas realizaciones espaciales, teniendo en cuenta todos los factores mencionados. Además, la otra cuestión crucial para los despliegues de alta densidad de las redes futuras es su suministro de energía. La carga o el intercambio de baterías para los dispositivos inalámbricos es inconveniente y daña el medio ambiente, especialmente si tenemos en cuenta el enorme número de nodos utilizados. Por lo tanto, se deben encontrar nuevas soluciones utilizando fuentes de energía renovables para reducir el consumo de electricidad. La recolección de energía inalámbrica es un método conveniente y respetuoso con el medio ambiente para prolongar la vida útil de las redes recolectando la energía de las señales de radiofrecuencia (RF) y convirtiéndola en electricidad de corriente continua mediante un hardware especializado. La energía de RF podría ser obtenida a partir de señales generadas en la misma o en otras redes. Sin embargo, si la cantidad de energía obtenida no es suficiente, podrían emplearse transmisores de energía inalambricos que la obtuvieran mediante paneles fotovoltaicos. De esta manera, podemos lograr un resultado favorable teniendo tanto una operación de red de energía cero como una conveniencia en la carga de los dispositivos inalámbricos. Por lo tanto, una investigación exhaustiva debe hacerse con el fin de garantizar que el rendimiento de la comunicación no se ve afectada. En esta tesis se estudia el rendimiento de la comunicación en redes de gran escala utilizando técnicas de geometría estocástica. Las redes que se estudian comprenden dispositivos inalámbricos capaces de recoger la energía de las señales RF. En la primera parte de la tesis, presentamos los efectos de la recolección de energía inalámbrica de las transmisiones de la red cooperativa sobre la probabilidad de cobertura y la vida útil de la red. En la segunda parte de la tesis, primero empleamos nodos sin baterías que son alimentados por transmisores de energía de RF para estudiar la probabilidad de conectividad. A continuación, asumimos que los transmisores dedicados son alimentados por energía solar para estudiar la conectividad en una red agrupada (clustered network) e investigar, por primera vez, la fiabilidad de las redes de energía cero. Finalmente, concluimos la tesis aportando nuevas lineas de investigación para trabajos futurosAward-winningPostprint (published version

    Design and stochastic analysis of emerging large-scale wireless-powered sensor networks

    Get PDF
    Undeniably, the progress in wireless networks during the last two decades is extraordinary. However, the ever-increasing upward trend in the numbers of wireless devices that will overwhelm every field of our everyday life, e.g., building automation, traffic management, health-care, etc., will introduce several issues in terms of communication and energy provision that need to be handled in advance. Regarding the communication issues, it is imperative to ensure the correct operation of the vast collection of nodes, especially for life-critical applications. Two well-known metrics that can characterize sufficiently the network reliability are the coverage and the connectivity probability that are derived by taking into account the network topology, the channel conditions between every transmitter-receiver pair, and the interference from other nodes. Nevertheless, considering all those factors is not straightforward. Lately, stochastic geometry has come into prominence, which is a mathematical tool to study the average network performance over many spatial realizations, while considering all aforementioned factors. Moreover, the other crucial issue for the large-scale dense network deployments of the future is their energy supply. Traditional battery charging or swapping for the wireless devices is both inconvenient and harms the environment, especially if we take into account the enormous numbers of nodes. Therefore, novel solutions have to be found using renewable energy sources to zero down the significant electricity consumption. Wireless energy harvesting is a convenient and environmentally-friendly approach to prolong the lifetime of networks by harvesting the energy from radio-frequency (RF) signals and converting it to direct current electricity through specialized hardware. The RF energy could be harvested from signals generated in the same or other networks. However, if the amount of harvested energy is not sufficient, solar-powered dedicated transmitters could be employed. In this way, we can achieve a favorable outcome by having both a zero-energy network operation and convenience in the charging of the wireless devices. Still, extensive investigation should be done in order to ensure that the communication performance is not affected. To that end, in this thesis, we study the communication performance in large-scale networks using tools from stochastic geometry. The networks that we study comprise wireless devices that are able to harvest the energy of RF signals. In the first part of the thesis, we present the effects of wireless energy harvesting from the transmissions of the cooperative network on the coverage probability and the network lifetime. In the second part of the thesis, we first employ batteryless nodes that are powered by dedicated RF energy transmitters to study the connectivity probability. Then, we assume that the dedicated transmitters are powered by solar energy to study the connectivity in a clustered network and investigate, for the first time, the reliability of zero-energy networks. Finally, we conclude the thesis by providing insightful research challenges for future works.Innegablemente, el progreso en las redes inalámbricas durante las últimas dos décadas es extraordinario. Sin embargo, la creciente tendencia al alza en el número de dispositivos inalámbricos que abarcarán todos los ámbitos de nuestra vida cotidiana, como la automatización de edificios, la gestión del tráfico, la atención sanitaria, etc., introducirá varias cuestiones en términos de comunicación y suministro de energía que se debe tener en cuenta con antelación. Respecto a los problemas de comunicación, es imprescindible asegurar el correcto funcionamiento de la vasta colección de nodos, especialmente para las aplicaciones vitales. Dos métricas bien conocidas que pueden caracterizar suficientemente la fiabilidad de la red son la probabilidad de cobertura y la de conectividad, que se derivan teniendo en cuenta la topología de la red, las condiciones del canal entre cada par transmisor-receptor y la interferencia de otros nodos. Sin embargo, considerar todos esos factores no es sencillo. Últimamente, la geometría estocástica ha llegado a la prominencia como un metodo de análisis, que es una herramienta matemática para estudiar el rendimiento promedio de la red sobre muchas realizaciones espaciales, teniendo en cuenta todos los factores mencionados. Además, la otra cuestión crucial para los despliegues de alta densidad de las redes futuras es su suministro de energía. La carga o el intercambio de baterías para los dispositivos inalámbricos es inconveniente y daña el medio ambiente, especialmente si tenemos en cuenta el enorme número de nodos utilizados. Por lo tanto, se deben encontrar nuevas soluciones utilizando fuentes de energía renovables para reducir el consumo de electricidad. La recolección de energía inalámbrica es un método conveniente y respetuoso con el medio ambiente para prolongar la vida útil de las redes recolectando la energía de las señales de radiofrecuencia (RF) y convirtiéndola en electricidad de corriente continua mediante un hardware especializado. La energía de RF podría ser obtenida a partir de señales generadas en la misma o en otras redes. Sin embargo, si la cantidad de energía obtenida no es suficiente, podrían emplearse transmisores de energía inalambricos que la obtuvieran mediante paneles fotovoltaicos. De esta manera, podemos lograr un resultado favorable teniendo tanto una operación de red de energía cero como una conveniencia en la carga de los dispositivos inalámbricos. Por lo tanto, una investigación exhaustiva debe hacerse con el fin de garantizar que el rendimiento de la comunicación no se ve afectada. En esta tesis se estudia el rendimiento de la comunicación en redes de gran escala utilizando técnicas de geometría estocástica. Las redes que se estudian comprenden dispositivos inalámbricos capaces de recoger la energía de las señales RF. En la primera parte de la tesis, presentamos los efectos de la recolección de energía inalámbrica de las transmisiones de la red cooperativa sobre la probabilidad de cobertura y la vida útil de la red. En la segunda parte de la tesis, primero empleamos nodos sin baterías que son alimentados por transmisores de energía de RF para estudiar la probabilidad de conectividad. A continuación, asumimos que los transmisores dedicados son alimentados por energía solar para estudiar la conectividad en una red agrupada (clustered network) e investigar, por primera vez, la fiabilidad de las redes de energía cero. Finalmente, concluimos la tesis aportando nuevas lineas de investigación para trabajos futuro

    Recent Advances in Wearable Sensing Technologies

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    Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we discuss open challenges that must be addressed to further improve the efficacy of wearable sensing systems in the future

    Printed Spiral Coil Design, Implementation, And Optimization For 13.56 MHz Near-Field Wireless Resistive Analog Passive (WRAP) Sensors

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    Noroozi, Babak. Ph.D. The University of Memphis. June 2020. Printed Spiral Coil Design, Implementation, and Optimization for 13.56 MHz Near-Field Wireless Resistive Analog Passive (WRAP) Sensors. Major Professor: Dr. Bashir I. Morshed.Monitoring the bio-signals in the regular daily activities for a long time can embrace many benefits for the patients, caregivers, and healthcare system. Early diagnosis of diseases prior to the onset of serious symptoms gives more time to take some preventive action and to begin effective treatment with lower cost. These health and economy benefits are achievable with a user-friendly, low-cost, and unobtrusive wearable sensor that can easily be carried by a patient with no interference with the normal life. The easy application of such sensor brings the smart and connected community (SCC) idea to existence. The spread of a designated disease, like COVID-19, can be studied by collecting the physiological signals transmitted from the wearable sensors in conjunction with a mobile app interface. Moreover, such a comfortable wearable sensor can help to monitor the vital signals during fitness activities for workout concerns. The desire of such wearable sensor has been responded in many researches and commercial products such as smart watch and Fitbit. Wireless connection between the sensor on the body and the scanner is the key and common factor of all convenient wearables. This essential feature has been currently addressed by the costly techniques which is the main impediment to be widely applicable. The existing wireless methods including WiFi, Bluetooth, RFID, and NFC impose cost, complexity, weight, and extra maintenance including battery replacement or recharging, which drove us to propose a low-cost, convenient, and simple technique for wireless connection suitable for battery-less fully-passive sensors. Using a pair of coils connected by the near-field magnetic induction has been copiously used in wireless power transfer (WPT) for medical and industrial applications. However, near field RFID and NFC rely on this technique with active circuits. In contrast, we have proposed a wireless resistive analog passive (WRAP) sensor in which a resistive transducer at the secondary side, affects the primary quality factor (Q) through the inductive connection between a pair of square-shaped Printed Spiral Coils (PSC). The primary 13.56 MHz (ISM band) signal is modulated in response to the continuous change of bio-signal and the amount of response to the unit change in transducer resistance is defined as sensitivity. A higher sensitivity enables the system to respond to the smaller bio-signals and increases the coils maximum relative mobilities. The PSCs specifications and circuit components determine the sensitivity and its tolerance to the coils displacements. We first define and formulize the objective function for coil and components optimization to achieve the maximum sensitivity. Although the optimization methods do not show much different results, due to the speed and simplicity, the Genetic Algorithm (GA) technique is chosen as an advanced method. Then in second optimization stage, the axial and lateral distances that affect the mutual inductance are introduced to the optimization process. The results as a pair of PSCs profiles and the associated circuit components are obtained and fabricated that produced the maximum sensitivity and misalignment tolerance. For the sake of patient comfort, the secondary coil size is fixed at 20 mm and the primary coil is optimized at 60 mm with the maximum (normalized) sensitivity 1.3 m for 16 mm axial distance. If the Read-Zone is defined as the space in which the center of secondary coil can move and the sensitivity keeps at least half of its maximum value, the best Read-Zone has a conical shape with the base radius 22.5 mm and height 14 mm. The analytical results are verified by the measurement results on the fabricated coils and circuits

    Game theory for cooperation in multi-access edge computing

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    Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

    Energy Harvesting and Energy Storage Systems

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    This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

    Wrist-based Phonocardiogram Diagnosis Leveraging Machine Learning

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    With the tremendous growth of technology and the fast pace of life, the need for instant information has become an everyday necessity, more so in emergency cases when every minute counts towards saving lives. mHealth has been the adopted approach for quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this research is to diagnose the heart condition based on phonocardiogram (PCG) analysis using Machine Learning techniques assuming limited processing power, in order to be encapsulated later in a mobile device. The diagnosis of PCG is performed using two techniques; 1. parametric estimation with multivariate classification, particularly discriminant function. Which will be explored at length using different number of descriptive features. The feature extraction will be performed using Wavelet Transform (Filter Bank). 2. Artificial Neural Networks, and specifically Pattern Recognition. This will also use decomposed version of PCG using Wavelet Transform (Filter Bank). The results showed 97.33% successful diagnosis using the first technique using PCG with a 19 dB Signal-to-Noise-Ratio. When the signal was decomposed into four sub-bands using a Filter Bank of the second order. Each sub-band was described using two features; the signal’s mean and covariance. Additionally, different Filter Bank orders and number of features are explored and compared. Using the second technique the diagnosis resulted in a 100% successful classification with 83.3% trust level. The results are assessed, and new improvements are recommended and discussed as part of future work.Teknologian valtavan kehittymisen ja nopean elämänrytmin myötä välittömästi saatu tieto on noussut jokapäiväiseksi välttämättömyydeksi, erityisesti hätätapauksissa, joissa jokainen säästetty minuutti on tärkeää ihmishenkien pelastamiseksi. Mobiiliterveys, eli mHealth, on yleisesti valjastettu käyttöön nopeaksi diagnoosimenetelmäksi mobiililaitteiden avulla. Käyttö on kuitenkin ollut haastavaa korkean datan laatuvaatimuksen ja suurten tiedonkäsittelyvaatimuksien, nopean laskentatehon ja sekä suuren virrankulutuksen vuoksi. Tämän tutkimuksen tavoitteena oli diagnosoida sydänsairauksia fonokardiogrammianalyysin (PCG) perusteella käyttämällä koneoppimistekniikoita niin, että käytettävä laskentateho rajoitetaan vastaamaan mobiililaitteiden kapasiteettia. PCG-diagnoosi tehtiin käyttäen kahta tekniikkaa 1. Parametrinen estimointi käyttäen moniulotteista luokitusta, erityisesti signaalien erotteluanalyysin avulla. Tätä asiaa tutkittiin syvällisesti käyttäen erilaisia tilastotieteellisesti kuvailevia piirteitä. Piirteiden irrotus suoritettiin käyttäen Wavelet-muunnosta ja suodatinpankkia. 2. Keinotekoisia neuroverkkoja ja erityisesti hahmontunnistusta. Tässä menetelmässä käytetään myös PCG-signaalin hajoitusta ja Wavelet-muunnos -suodatinpankkia. Tulokset osoittivat, että PCG 19dB:n signaali-kohina-suhteella voi johtaa 97,33% onnistuneeseen diagnoosiin käytettäessä ensimmäistä tekniikkaa. Signaalin hajottaminen neljään alikaistaan suoritettiin käyttämällä toisen asteen suodatinpankkia. Jokainen alikaista kuvattiin käyttäen kahta piirrettä: signaalin keskiarvoa ja kovarianssia, näin saatiin yhteensä kahdeksan ominaisuutta kuvaamaan noin yhden minuutin näytettä PCG-signaalista. Lisäksi tutkittiin ja verrattiin eriasteisia suodattimia ja piirteitä. Toista tekniikkaa käyttäen diagnoosi johti 100% onnistuneeseen luokitteluun 83,3% luotettavuustasolla. Tuloksia käsitellään ja pohditaan, sekä tehdään niistä johtopäätöksiä. Lopuksi ehdotetaan ja suositellaan käytettyihin menetelmiin uusia parannuksia jatkotutkimuskohteiksi.fi=vertaisarvioitu|en=peerReviewed

    Selected Papers from the 5th International Electronic Conference on Sensors and Applications

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    This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

    Addressing training data sparsity and interpretability challenges in AI based cellular networks

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    To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization
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