Low Cost and Reliable Wireless Sensor Networks for Environmental Monitoring

This thesis utilizes wireless sensor network systems to learn of changes in wireless network performance and environment, establishing power efficient systems that are low cost and are able to perform large scale monitoring. The proposed system was built at the University of Maine’s Wireless Sensor Networks (WiSe-Net) laboratory in collaboration with University of New Hampshire and University of Vermont researchers. The system was configured to perform soil moisture measurement with provision to include other sensor types at later stages in collaboration with Alabama A & M University. In the research associated with this thesis, a general relay energy assisted scenario is considered, where a transmitter is powered by an energy source through both direct and relay links. An energy efficient scheduling method is proposed for the system model to determine whether to transmit data or stay silent based on the stored energy level and channel state. An analytical expression has been derived to approximate outage probability of the system in terms of energy and data thresholds. In addition, we propose a model for evaluating the outage probability of a solar powered base station, equipped with a selected photo voltaic panel size and battery configuration. The energy harvesting environment location has been selected as the state of Maine, during a variety of weather conditions, considering base station loading during different days of the week. Simulation results shows the required photo-voltaic panel size and number of batteries for specific tolerable outage probability of the system. The fundamental contribution of this work is in development of hardware and software based on new methodologies to optimize network longevity using AI/ML. One of the most important metrics to define longevity and reliability is the outage probability of a network. We have derived equations for the outage probability, based upon power configuration panel size, battery capacity and the environmental factors, meteorological and diurnal. This will impact the observed cost function which is outage probability. The system models proposed in this thesis result in much more energy efficient systems with less outage probabilities compared to the current systems

Dimensioning Renewable Energy Systems to Power Mobile Networks

To face the huge increase in the mobile traffic demand, denser cellular access networks are extensively deployed by mobile operators, entailing high cost for energy supply. Hence, renewable energy (RE) sources are often adopted to power base stations (BSs), in order to make them more self-sufficient and reduce the energy bill. Nevertheless, sizing an RE generation system is a critical task, and the dimensioning methods available in the literature are based on simulation or optimization approaches, hence resulting time consuming or computationally complex. This paper proposes and validates a simple still effective analytical method that, based on the location dependent mean value and variance of RE production, allows to find feasible combinations of photovoltaic (PV) panel and battery sizes, suitable to power a BS and decrease the storage depletion probability below a target threshold. Furthermore, the application of this method highlights the role of RE production variance. Higher values of the variance require larger PV panels, almost doubled with respect to locations with low variance. However, only locations with higher variance benefit from increasing the battery size and relaxing the constraint on energy self-sufficiency, with the scope of reducing the required PV panel capacity and the capital expenditures

Network resource allocation policies with energy transfer capabilities

During the last decades, mobile network operators have witnessed an exponential increase in the traffic demand, mainly due to the high request of services from a huge amount of users. The trend is of a further increase in both the traffic demand and the number of connected devices over the next years. The traffic load is expected to have an annual growth rate of 53% for the mobile network alone, and the upcoming industrial era, which will connect different types of devices to the mobile infrastructure including human and machine type communications, will definitely exacerbate such an increasing trend. The current directions anticipate that future mobile networks will be composed of ultra dense deployments of heterogeneous Base Stations (BSs), where BSs using different transmission powers coexist. Accordingly, the traditional Macro BSs layer will be complemented or replaced with multiple overlapping tiers of small BSs (SBSs), which will allow extending the system capacity. However, the massive use of Information and Communication Technology (ICT) and the dense deployment of network elements is going to increase the level of energy consumed by the telecommunication infrastructure and its carbon footprint on the environment. Current estimations indicates that 10% of the worldwide electricity generation is due to the ICT industry and this value is forecasted to reach 51% by 2030, which imply that 23% of the carbon footprint by human activity will be due to ICT. Environmental sustainability is thus a key requirement for designing next generation mobile networks. Recently, the use of Renewable Energy Sources (RESs) for supplying network elements has attracted the attention of the research community, where the interest is driven by the increased efficiency and the reduced costs of energy harvesters and storage devices, specially when installed to supply SBSs. Such a solution has been demonstrated to be environmentally and economically sustainable in both rural and urban areas. However, RESs will entail a higher management complexity. In fact, environmental energy is inherently erratic and intermittent, which may cause a fluctuating energy inflow and produce service outage. A proper control of how the energy is drained and balanced across network elements is therefore necessary for a self-sustainable network design. In this dissertation, we focus on energy harvested through solar panels that is deemed the most appropriate due to the good efficiency of commercial photovoltaic panels as well as the wide availability of the solar source for typical installations. The characteristics of this energy source are analyzed in the first technical part of the dissertation, by considering an approach based on the extraction of features from collected data of solar energy radiation. In the second technical part of the thesis we introduce our proposed scenario. A federation of BSs together with the distributed harvesters and storage devices at the SBS sites form a micro-grid, whose operations are managed by an energy management system in charge of controlling the intermittent and erratic energy budget from the RESs. We consider load control (i.e., enabling sleep mode in the SBSs) as a method to properly manage energy inflow and spending, based on the traffic demand. Moreover, in the third technical part, we introduce the possibility of improving the network energy efficiency by sharing the exceeding energy that may be available at some BS sites within the micro-grid. Finally, a centralized controller based on supervised and reinforcement learning is proposed in the last technical part of the dissertation. The controller is in charge of opportunistically operating the network to achieve efficient utilization of the harvested energy and prevent SBSs blackout.Durante las últimas décadas, los operadores de redes móviles han sido testigos de un aumento exponencial en la demanda de tráfico, principalmente debido a la gran solicitud de servicios de una gran cantidad de usuarios. La tendencia es un aumento adicional tanto en la demanda de tráfico como en la cantidad de dispositivos conectados en los próximos años. Se espera que la carga de tráfico tenga una tasa de crecimiento anual del 53% solo para la red móvil, y la próxima era industrial, que conectará diferentes tipos de dispositivos a la infraestructura móvil, definitivamente exacerbará tal aumento. Las instrucciones actuales anticipan que las redes móviles futuras estarán compuestas por despliegues ultra densos de estaciones base (BS) heterogéneas. En consecuencia, la capa tradicional de Macro BS se complementará o reemplazará con múltiples niveles superpuestos de pequeños BS (SBS), lo que permitirá ampliar la capacidad del sistema. Sin embargo, el uso masivo de la Tecnología de la Información y la Comunicación (TIC) y el despliegue denso de los elementos de la red aumentará el nivel de energía consumida por la infraestructura de telecomunicaciones y su huella de carbono en el medio ambiente. Las estimaciones actuales indican que el 10% de la generación mundial de electricidad se debe a la industria de las TIC y se prevé que este valor alcance el 51% para 2030, lo que implica que el 23% de la huella de carbono por actividad humana se deberá a las TIC. La sostenibilidad ambiental es, por lo tanto, un requisito clave para diseñar redes móviles de próxima generación. Recientemente, el uso de fuentes de energía renovables (RES) para suministrar elementos de red ha atraído la atención de la comunidad investigadora, donde el interés se ve impulsado por el aumento de la eficiencia y la reducción de los costos de los recolectores y dispositivos de almacenamiento de energía, especialmente cuando se instalan para suministrar SBS. Se ha demostrado que dicha solución es ambiental y económicamente sostenible tanto en áreas rurales como urbanas. Sin embargo, las RES conllevarán una mayor complejidad de gestión. De hecho, la energía ambiental es inherentemente errática e intermitente, lo que puede causar una entrada de energía fluctuante y producir una interrupción del servicio. Por lo tanto, es necesario un control adecuado de cómo se drena y equilibra la energía entre los elementos de la red para un diseño de red autosostenible. En esta disertación, nos enfocamos en la energía cosechada a través de paneles solares que se considera la más apropiada debido a la buena eficiencia de los paneles fotovoltaicos comerciales, así como a la amplia disponibilidad de la fuente solar para instalaciones típicas. Las características de esta fuente de energía se analizan en la primera parte técnica de la disertación, al considerar un enfoque basado en la extracción de características de los datos recopilados de radiación de energía solar. En la segunda parte técnica de la tesis presentamos nuestro escenario propuesto. Una federación de BS junto con los cosechadores distribuidos y los dispositivos de almacenamiento forman una microrred, cuyas operaciones son administradas por un sistema de administración de energía a cargo de controlar el presupuesto de energía intermitente y errático de las RES. Consideramos el control de carga como un método para administrar adecuadamente la entrada y el gasto de energía, en función de la demanda de tráfico. Además, en la tercera parte técnica, presentamos la posibilidad de mejorar la eficiencia energética de la red al compartir la energía excedente que puede estar disponible en algunos sitios dentro de la microrred. Finalmente, se propone un controlador centralizado basado en aprendizaje supervisado y de refuerzo en la última parte técnica de la disertación. El controlador está a cargo de operar la red para lograr una utilización eficiente de energía y previene el apagón de SB

Green Mobile Networks: from self-sustainability to enhanced interaction with the Smart Grid

Nowadays, the staggering increase of the mobile traffic is leading to the deployment of denser and denser cellular access networks, hence Mobile Operators are facing huge operational cost due to power supply. Therefore, several research efforts are devoted to make mobile networks more energy efficient, with the twofold objective of reducing costs and improving sustainability. To this aim, Resource on Demand (RoD) strategies are often implemented in Mobile Networks to reduce the energy consumption, by dynamically adapting the available radio resources to the varying user demand. In addition, renewable energy sources are widely adopted to power base stations (BSs), making the mobile network more independent from the electric grid. At the same time, the Smart Grid (SG) paradigm is deeply changing the energy market, envisioning an active interaction between the grid and its customers. Demand Response (DR) policies are extensively deployed by the utility operator, with the purpose of coping with the mismatches between electricity demand and supply. The SG operator may enforce its users to shift their demand from high peak to low peak periods, by providing monetary incentives, in order to leverage the energy demand profiles. In this scenario, Mobile Operators can play a central role, since they can significantly contribute to DR objectives by dynamically modulating their demand in accordance with the SG requests, thus obtaining important electricity cost reductions. The contribution of this thesis consists in investigating various critical issues raised by the introduction of photovoltaic (PV) panels to power the BSs and to enhance the interaction with the Smart Grid, with the main objectives of making the mobile access network more independent from the grid and reducing the energy bill. When PV panels are employed to power mobile networks, simple and reliable Renewable Energy (RE) production models are needed to facilitate the system design and dimensioning, also in view of the intermittent nature of solar energy production. A simple stochastic model is hence proposed, where RE production is represented by a shape function multiplied by a random variable, characterized by a location dependent mean value and a variance. Our model results representative of RE production in locations with low intra-day weather variability. Simulations reveal also the relevance of RE production variability: for fixed mean production, higher values of the variance imply a reduced BS self-sufficiency, and larger PV panels are hence required. Moreover, properly designed models are required to accurately represent the complex operation of a mobile access network powered by renewable energy sources and equipped with some storage to harvest energy for future usage, where electric loads vary with the traffic demand, and some interaction with the Smart Grid can be envisioned. In this work various stochastic models based on discrete time Markov chains are designed, each featuring different characteristics, which depend on the various aspects of the system operation they aim to examine. We also analyze the effects of quantization of the parameters defined in these models, i.e. time, weather, and energy storage, when they are applied for power system dimensioning. Proper settings allowing to build an accurate model are derived for time granularity, discretization of the weather conditions, and energy storage quantization. Clearly, the introduction of RE to power mobile networks entails a proper system dimensioning, in order to balance the solar energy intermittent production, the traffic demand variability and the need for service continuity. This study investigates via simulation the RE system dimensioning in a mobile access network, trading off energy self-sufficiency targets and cost and feasibility constraints. In addition, to overcome the computational complexity and long computational time of simulation or optimization methods typically used to dimension the system, a simple analytical formula is derived, based on a Markovian model, for properly sizing a renewable system in a green mobile network, based on the local RE production average profile and variability, in order to guarantee the satisfaction of a target maximum value of the storage depletion probability. Furthermore, in a green mobile network scenario, Mobile Operators are encouraged to deploy strategies allowing to further increase the energy efficiency and reduce costs. This study aims at analyzing the impact of RoD strategies on energy saving and cost reduction in green mobile networks. Up to almost 40% of energy can be saved when RoD is applied under proper configuration settings, with a higher impact observed in traffic scenarios in which there is a better match between communication service demand and RE production. While a feasible PV panel and storage dimensioning can be achieved only with high costs and large powering systems, by slightly relaxing the constraint on self-sustainability it is possible to significantly reduce the size of the required PV panels, up to more than 40%, along with a reduction in the corresponding capital and operational expenditures. Finally, the introduction of RE in mobile networks contributes to give mobile operators the opportunity of becoming prominent stakeholders in the Smart Grid environment. In relation to the integration of the green network in a DR framework, this study proposes different energy management policies aiming at enhancing the interaction of the mobile network with the SG, both in terms of energy bill reduction and increased capability of providing ancillary services. Besides combining the possible presence of a local RE system with the application of RoD strategies, the proposed energy management strategies envision the implementation of WiFi offloading (WO) techniques in order to better react to the SG requests. Indeed, some of the mobile traffic can be migrated to neighbor Access Points (APs), in order to accomplish the requests of decreasing the consumption from the grid. The scenario is investigated either through a Markovian model or via simulation. Our results show that these energy management policies are highly effective in reducing the operational cost by up to more than 100% under proper setting of operational parameters, even providing positive revenues. In addition, WO alone results more effective than RoD in enhancing the capability to provide ancillary services even in absence of RE, raising the probability of accomplishing requests of increasing the grid consumption up to almost 75% in our scenario, twice the value obtained under RoD. Our results confirm that a good (in terms of energy bill reduction) energy management strategy does not operate by reducing the total grid consumption, but by timely increasing or decreasing the grid consumption when required by the SG. This work shows that the introduction of RE sources is an effective and feasible solution to power mobile networks, and it opens the way to new interesting scenarios, where Mobile Network Operators can profitably interact with the Smart Grid to obtain mutual benefits, although this definitely requires the integration of suitable energy management strategies into the communication infrastructure management

Micro-scale inductorless maximum power point tracking DC-DC converter

In this study, the authors propose a simple maximum power point tracking (MPPT) DC-DC converter amenable for micro-scale photovoltaic applications. The solution avoids the use of inductors and exploits a charge pump as a voltage boost element. To take into account the temperature dependence of the MPP voltage, a passive temperature compensation circuit is also included. To validate the idea a prototype was realised with commercial off-the-shelf components. A system efficiency better than 83% for output power above 90 mW is obtained. The results show the viability of the proposed approach which could be further improved through a full custom integrated-circuit design

Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version

Resource management techniques for sustainable networks with energy harvesting nodes

Premi extraordinari doctorat UPC curs 2015-2016, àmbit Enginyeria de les TICThis dissertation proposes novel techniques for assigning resources of wireless networks by considering that the coverage radii are small, implying that some power consumption sinks not considered so far shouldnow be introduced, and by considering that the devices are battery-powered terminals provided with energy harvesting capabilities. In this framework, two different configurations in terms of harvesting capabilities are considered. First, we assume that the energy source is external and not controllable, e.g. solar energy. In this context, the proposed design should adapt to the energy that is currently being harvested. We also study the effect of having a finite backhaul connection that links the wireless access network with the core network. On the other hand, we propose a design in which the transmitter feeds actively the receivers with energy by transmitting signals that receivers use for recharging their batteries. In this case, the power transfer design should be carried out jointly with the power control strategy for users that receive information as both procedures, transfer of information and transfer of power, are implemented at the transmitter and make use of a common resource, i.e., power. Apart from techniques for assigning the radio resources, this dissertation develops a procedure for switching on and off base stations. Concerning this, it is important to notice that the traffic profile is not constant throughout the day. This is precisely the feature that can be exploited to define a strategy based on a dynamic selection of the base stations to be switched off when the traffic load is low, without affecting the quality experienced by the users. Thanks to this procedure, we are able to deploy smaller energy harvesting sources and smaller batteries and, thus, to reduce the cost of the network deployment. Finally, we derive some procedures to optimize high level decisions of the network operation in which variables from several layers of the protocol stack are involved. In this context, admission control procedures for deciding which user should be connected to which base station are studied, taking into account information of the average channel information, the current battery levels, etc. A multi-tier multi-cell scenario is assumed in which base stations belonging to different tiers have different capabilities, e.g., transmission power, battery size, end energy harvesting source size. A set of strategies that require different computational complexity are derived for scenarios with different user mobility requirements.Aquesta tesis doctoral proposa tècniques per assignar els recursos disponibles a les xarxes wireless considerant que els radis de cobertura són petits, el que implica que altres fonts de consum d’energia no considerades fins al moment s’hagin d’introduir dins els dissenys, i considerant que els dispositius estan alimentats amb bateries finites i que tenen a la seva disposició fonts de energy harvesting. En aquest context, es consideren dues configuracions diferents en funció de les capacitats de l’energia harvesting. En primer lloc, s’assumirà que la font d’energia és externa i incontrolable com, per exemple, l’energia solar. Els dissenys proposats han d’adaptar-se a l’energia que s’està recol·lectant en un precís moment. En segon lloc, es proposa un disseny en el qual el transmissor és capaç d’enviar energia als receptors mitjançant senyals de radiofreqüència dissenyats per aquest fi, energia que és utilitzada per recarregar les bateries. A part de tècniques d’assignació de recursos radio, en aquesta tesis doctoral es desenvolupa un procediment dinàmic per apagar i encendre estacions base. És important notar que el perfil de tràfic no és constant al llarg del dia. Aquest és precisament el patró que es pot explotar per definir una estratègia dinàmica per poder decidir quines estaciones base han de ser apagades, tot això sense afectar la qualitat experimentada pels usuaris. Gràcies a aquest procediment, es possible desplegar fonts d'energy harvesting més petites i bateries més petites. Finalment, aquesta tesis doctoral presenta procediments per optimitzar decisions de nivell més alt que afecten directament al funcionament global de la xarxa d’accés. Per prendre aquestes decisions, es fa ús de diverses variables que pertanyen a diferents capes de la pila de protocols. En aquest context, aquesta tesis aborda el disseny de tècniques de control d’admissió d’usuaris a estacions base en entorns amb múltiples estacions base, basant-se amb la informació estadística dels canals, i el nivell actual de les bateries, entre altres. L'escenari considerat està format per múltiples estacions base, on cada estació base pertany a una família amb diferents capacitats, per exemple, potència de transmissió o mida de la bateria. Es deriven un conjunt de tècniques amb diferents costos computacionals que són d'utilitat per a poder aplicar a escenaris amb diferents mobilitats d’usuaris.Award-winningPostprint (published version

Microalgae-based wastewater treatment systems at demonstrative scale : gravity harvesting and thickening of biomass, and advanced design of bioreactors

Microalgae culture is currently receiving considerable attention for its potential in wastewater treatment and production of algal biomass from which high-value bioproducts and bioenergy can be obtained, as well as the consequent carbon dioxide sequestration via photosynthesis process. However, biomass harvesting is one of the bottlenecks in microalgae culture and microalgae-based wastewater treatment systems. Energy intensive technologies are required to separate the solid-liquid phase due the low density of microalgae. Low-cost processes, such as sedimentation, are not efficient enough due to the low settling velocities of the microalgae. Sedimentation coupled to coagulation and flocculation has been widely studied on lab-scale in order to increase the microalgae settling velocity. However, few studies have addressed the scaling up of these experimental results in order to industrializing the process. The thesis has been divided into two main parts. A first study addressed the physical and theoretical principles of sedimentation used for the operation and optimization of biomass harvesting from a microalgae culture for wastewater treatment at demonstration scale in the framework of the INCOVER research project "Innovative Eco-technologies for Resource Recovery from Wastewater" (GA 689242) (https://incover-project.eu/), which aimed to validate innovative technologies at demonstration scale to convert wastewater into an alternative energy source and value-added products. A second study focused on the operation and optimization of the downstream thickening process of biomasspreviously harvested in the same facilities. Finally, the second part consist of a study and optimization of the behavior of mixed liquor in its transit through a high rate algae pond for wastewater treatment using Computational Fluid Dynamics modeling for its implementation in the city of Aligarh. This study has been carried out under the H2020 PAVITR project (http://www.pavitr.net; GA 821410), which aims at validation of sustainable natural and advanced technologies for water and wastewater treatment, monitoring and safe reuse of water in India. In the first study of the first part, the physical and theoretical principles of sedimentation were addressed to be used for the operation and optimization of harvesting biomass in lamella settler (700 L) from a microalgae culture for wastewater treatment on a three semi-closed tubular photobioreactor (11.7 m3 each) at demonstrative scale. During 6 months the inflow (6900 m3·day-1), coagulant dosage (1-12 mg·L-1) and purges of the biomass (60-240 L·day-1) were adjusted in order to achieve a proper separation of the solid-liquid phase. Results in this section evidenced the efficiency of the Lamella in the solid-liquid separation task obtaining an outlet turbidity below of 5 NTU after the optimization period. In the second part, two thickeners were operated and optimized in order to achieve a proper concentration (20 g·L-1) of previous harvested biomass for subsequent anaerobic digestion process at the same installations. The scrapers and purges were optimized in four periods during two months. Results showed an eventually concentrations of 26.5 g·L-1 in last period due a minimized use of scrapers in order to avoid the particles resuspension allowing a proper compression settling. In the second part, demonstrative-scale HRAP system was designed to be implemented in Aligarh (India) with a treatment capacity of 50 m3·day-1. The objective of the study was to assist, verify and optimize the conventional dimensioning of the High Rate Algae Ponds (HRAP) by means of biokinetic modelling and hydrodynamic analysis using Computational Fluid Dynamics (CFD). According to the biokinetic model simulations, 4 days was the optimal hydraulic retention time to enhance nutrient removal. A 3D model of the HRAP was built to analyze the hydrodynamic behavior of 36 different carousel designs. The different combinations of baffle numbers on the reversals, center wall widths and tear-shape sizes were simulated. The presence of low velocity zones as well as the useful area vs. the total occupied area were quantify. Two baffles and tear-shapes with a diameter equal to ¼ of the channel width was the most efficient configuration. In addition, a techno-economic assessment of the system determined an investment cost of € 483 per population equivalent (PE) and an operational cost of € 0.19 per m3 of treated wastewater.El cultiu de microalgues està rebent actualment una atenció considerable pel seu potencial en el tractament d'aigües residuals i la producció de biomassa d'algues de la qual es poden obtenir bioproductes d'alt valor i bioenergia, així com el segrest consegüent de diòxid de carboni mitjançant el procés de fotosíntesi. Tot i això, la recol·lecció de biomassa és un dels colls d'ampolla del cultiu de microalgues i dels sistemes de tractament d'aigües residuals basats en elles. La separació de la fase sòlida-líquida requereix tecnologies d'alt consum energètic a causa de la baixa densitat de les microalgues. Els processos de baix cost, com la sedimentació, no són prou eficaços a causa de la baixa velocitat de sedimentació de les microalgues. La sedimentació combinada amb la coagulació i la floculació s'ha estudiat àmpliament a escala de laboratori per augmentar la velocitat de sedimentació de la biomassa algal. Tot i això, pocs estudis han abordat l'augment d'escala d'aquests resultats experimentals per industrialitzar el procés. La tesi ha estat dividida en dues parts. La primera està formada per dos estudis i és el tema principal d'aquesta tesi. Un primer estudi va abordar els principis físics i teòrics de la sedimentació que s'utilitzen pel funcionament i optimització de la recol·lecció de biomassa d'un cultiu de microalgues pel tractament d'aigües residuals a escala demostrativa en el marc del projecte de recerca INCOVER "Innovative Eco-technologies for Resource Recovery from Wastewater" (GA 689242) (https://incover-project.eu/), l'objectiu del qual era validar tecnologies innovadores a escala demostrativa per convertir les aigües residuals en una font d’energia alternativa i en productes de valor afegit. Un segon estudi es va centrar en el funcionament i optimització del procés d'espessiment posterior de la biomassa prèviament collida a les mateixes instal·lacions mitjançant dos espessidors treballant en línia. Finalment, la segona part va consistir en l'estudi i l'optimització del comportament del licor barrejat en el trànsit per un estany d'algues d'alta taxa pel tractament d'aigües residuals mitjançant la modelització de la Dinàmica de Fluids Computacional per a la seva implantació a la ciutat d’Aligarh. Aquest estudi s'ha realitzat en el marc del projecte H2020 PAVITR (http://www.pavitr.net; GA 821410), l'objectiu del qual és la validació de tecnologies naturals i avançades sostenibles pel tractament de l'aigua i de les aigües residuals, control i la reutilització segura de l’aigua a l’Índia. Al primer estudi de la primera part, es van abordar els principis físics i teòrics de la sedimentació per utilitzar-los en el funcionament i l'optimització de la collita de biomassa en sedimentador de làmines (700 L) d'un cultiu de microalgues pel tractament d'aigües residuals en un fotobioreactor tubular semitancat de tres (11,7 m3 cadascun) a escala demostrativa. Durant 6 mesos es va ajustar el flux d'entrada (6900 m3-dia-1), la dosi de coagulant (1-12 mg·L-1) i les purgues de la biomassa (60-240 L·dia-1) per aconseguir una adequada separació de la fase sòlid-líquida. Els resultats d'aquest apartat van evidenciar l'eficàcia de les lamel·les en la tasca de separació sòlid-líquid obtenint una terbolesa de sortida inferior a 5 NTU després del període d'optimització. En el segon estudi, es van operar i optimitzar dos espessidors per aconseguir una concentració adequada (20 g·L-1) de la biomassa recol·lectada prèviament pel posterior procés de digestió anaeròbia a les mateixes instal·lacions. Els espessidors i les purgues es van optimitzar en quatre períodes durant dos mesos. Els resultats van mostrar una concentració final de 26,5 g·L-1 a l'últim període a causa d'un ús minimitzat dels rascadors per evitar la resuspensió de les partícules permetent una adequada sedimentació per compressió. A la segona part, es va dissenyar una Llacuna d'Alta Càrrega a escala demostrativa per ser implementada a Aligarh (Índia) amb una capacitat de tractament de 50 m3・dia-1. L'objectiu de l'estudi era assistir, verificar i optimitzar el dimensionament convencional de les llacunes d'alta càrrega mitjançant la modelització biocinètica i l'anàlisi hidrodinàmica mitjançant dinàmica de fluids computacional (CFD). Segons les simulacions del model biocinètic, el temps de retenció hidràulica òptim per millorar l'eliminació de nutrients va ser de 4 dies. Es va construir un model 3D de la llacuna per analitzar el comportament hidrodinàmic de 36 dissenys en forma de carrusel amb diferents configuracions. Es van simular les diferents combinacions de nombres de deflectors en les inversions, amples de paret central i mides de forma de llàgrima als extrems del mur central. Es va quantificar la presència de zones de baixa velocitat, així com l'àrea útil davant de l'àrea total ocupada. La configuració més eficient va ser la composta per dos deflectors i formes de llàgrima amb un diàmetre igual a . de l'amplada del canal. A més, una avaluació tecno-econòmica del sistema va determinar un cost d'inversió de 732 euros per població equivalent (PE) i un cost operatiu de 0,19 euros per m3 d'aigua residual tractada.El cultivo de microalgas está recibiendo actualmente una atención considerable por su potencial en el tratamiento de aguas residuales y la producción de biomasa de algas de la que se pueden obtener bioproductos de alto valor y bioenergía, así como el consiguiente secuestro de dióxido de carbono mediante el proceso de fotosíntesis. Sin embargo, la recolección de biomasa es uno de los cuellos de botella del cultivo de microalgas y de los sistemas de tratamiento de aguas residuales basados en ellas. La separación de la fase sólida-líquida requiere tecnologías de alto consumo energético debido a la baja densidad de las microalgas. Los procesos de bajo coste, como la sedimentación, no son lo suficientemente eficaces debido a la baja velocidad de sedimentación de las microalgas. La sedimentación combinada con la coagulación y la floculación se ha estudiado ampliamente a escala de laboratorio para aumentar la velocidad de sedimentación de la biomasa algal. Sin embargo, pocos estudios han abordado el aumento de escala de estos resultados experimentales con el fin de industrializar el proceso. La tesis se ha dividido en dos partes principales. La primera está conforma por dos estudios y es el tema principal de esta tesis. Un primer estudio abordó los principios físicos y teóricos de la sedimentación que se utilizan para el funcionamiento y la optimización de la recolección de biomasa de un cultivo de microalgas para el tratamiento de aguas residuales a escala demostrativa en el marco del proyecto de investigación INCOVER "Innovative Eco-technologies for Resource Recovery from Wastewater" (GA 689242) (https://incover-project.eu/), cuyo objetivo era validar tecnologías innovadoras a escala demostrativa para convertir las aguas residuales en una fuente de energía alternativa y en productos de valor añadido. Un segundo estudio se centró en el funcionamiento y optimización del proceso de espesamiento posterior de la biomasa previamente cosechada en las mismas instalaciones mediante dos espesadores trabajando en línea. Por último, la segunda parte consistió en el estudio y optimización del comportamiento del licor mezclado en su tránsito por un estanque de algas de alta tasa para el tratamiento de aguas residuales mediante la modelización de la Dinámica de Fluidos Computacional para su implantación en la ciudad de Aligarh. Este estudio se ha realizado en el marco del proyecto H2020 PAVITR (http://www.pavitr.net; GA 821410), cuyo objetivo es la validación de tecnologías naturales y avanzadas sostenibles para el tratamiento del agua y de las aguas residuales, el control y la reutilización segura del agua en la India. En el primer estudio de la primera parte, se abordaron los principios físicos y teóricos de la sedimentación para utilizarlos en el funcionamiento y la optimización de la cosecha de biomasa en sedimentador de láminas (700 L) de un cultivo de microalgas para el tratamiento de aguas residuales en un fotobiorreactor tubular semicerrado de tres (11,7 m3 cada uno) a escala demostrativa. Durante 6 meses se ajustó el flujo de entrada (6900 m3·día-1), la dosis de coagulante (1-12 mg·L-1) y las purgas de la biomasa (60-240 L·día-1) para conseguir una adecuada separación de la fase sólido-líquida. Los resultados de este apartado evidenciaron la eficacia de las lamelas en la tarea de separación sólidolíquido obteniendo una turbidez de salida inferior a 5 NTU tras el periodo de optimización. En el segundo estudio, se operaron y optimizaron dos espesadores para conseguir una concentración adecuada (20 g·L-1) de la biomasa recolectada previamente para el posterior proceso de digestión anaerobia en las mismas instalaciones. Los espesadores y las purgas se optimizaron en cuatro periodos durante dos meses. Los resultados mostraron una concentración final de 26,5 g·L-1 en el último periodo debido a un uso minimizado de los rascadores para evitar la resuspensión de las partículas permitiendo una adecuada sedimentación por compresión. En la segunda parte, se diseñó una Laguna de Alta Carga a escala demostrativa para ser implementada en Aligarh (India) con una capacidad de tratamiento de 50 m3·día-1. El objetivo del estudio era asistir, verificar y optimizar el dimensionamiento convencional de las Lagunas de Alta Carga mediante la modelización biocinética y el análisis hidrodinámico mediante Dinámica de Fluidos Computacional (CFD). Según las simulaciones del modelo biocinético, el tiempo de retención hidráulica óptimo para mejorar la eliminación de nutrientes fue de 4 días. Se construyó un modelo 3D de la laguna para analizar el comportamiento hidrodinámico de 36 diseños en forma de carrusel con diferentes configuraciones. Se simularon las diferentes combinaciones de números de deflectores en las inversiones, anchos de pared central y tamaños de forma de lágrima en os extremos del muro central. Se cuantificó la presencia de zonas de baja velocidad, así como el área útil frente al área total ocupada. La configuración más eficiente resultó ser la compuesta por dos deflectores y formas de lágrima con un diámetro igual a ¼ de la anchura del canal. Además, una evaluación técnico-económica del sistema determinó un coste de inversión de 732 euros por población equivalente (PE) y un coste operativo de 0,19 euros por m3 de agua residual tratada.Postprint (published version

Wireless sensor networks with energy harvesting: Modeling and simulation based on a practical architecture using real radiation levels

This paper presents a new energy-harvesting model for a network simulator that implements super-capacitor energy storage with solar energy-harvesting recharge. The model is easily extensible, and other energyharvesting systems, or different energy storages, can be further developed. Moreover, code can be conveniently reused as the implementation is entirely uncoupled from the radio and node models. Real radiation data are obtained from available online databases in order to dynamically calculate super-capacitor charge and discharge. Such novelty enables the evaluation of energy evolution on a network of sensor nodes at various physical world locations and during different seasons. The model is validated against a real and fully working prototype, and good result correlation is shown. 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