DEVELOPMENT OF SOLAR ENERGY HARVESTING FOR WIRELESS SENSOR

To have a successful wireless sensor networks (WSN), we should have an energy supply which provided by batteries. Batteries have small size and provide sufficient energy for the motes, but batteries cannot sustain the energy for the (WSN) to operate long time. The reason is that the batteries have limit storage capacity and it used up by time. So to save the sustainability of the system we harvest energy from surrounding environment such as light, thermal, or vibration. All these are renewable and green types of energy that does not cause pollution to the environment. In our project, a solar energy harvesting system have been introduced to provide energy requirement for the (WSN) to operate. A photovoltaic (PV) module, solar charge controller and energy storage are elements that used for the solar energy harvesting system. And according to calculations, a suitable PV module, batteries, and solar charging circuit are determined. On the other hand to get the highest and the maximum efficiency of the energy harvested, we use a maximum peak power tracking or maximum power point tracking technique (MPPT), to charge our rechargeable batteries which for our project is lithium-ion battery

DEVELOPMENT OF SOLAR ENERGY HARVESTING FOR WIRELESS SENSOR

To have a successful wireless sensor networks (WSN), we should have an energy supply which provided by batteries. Batteries have small size and provide sufficient energy for the motes, but batteries cannot sustain the energy for the (WSN) to operate long time. The reason is that the batteries have limit storage capacity and it used up by time. So to save the sustainability of the system we harvest energy from surrounding environment such as light, thermal, or vibration. All these are renewable and green types of energy that does not cause pollution to the environment. In our project, a solar energy harvesting system have been introduced to provide energy requirement for the (WSN) to operate. A photovoltaic (PV) module, solar charge controller and energy storage are elements that used for the solar energy harvesting system. And according to calculations, a suitable PV module, batteries, and solar charging circuit are determined. On the other hand to get the highest and the maximum efficiency of the energy harvested, we use a maximum peak power tracking or maximum power point tracking technique (MPPT), to charge our rechargeable batteries which for our project is lithium-ion battery

Hybrid of Solar Energy Harvesting using IoT and WSN Technology

Many experts consider WSNs to be one of the most important scientific topics. To fully utilize WSNs to increase the lifespan of sensors, however, is challenging due to the significant energy limits. Numerous methods for energy collecting, energy transfer, and energy conservation have been proposed.  The internet of things (IoT) manages a vast infrastructure of web-enabled smart devices. It does this by using data collected from its surroundings. Therefore, such devices are composed of scalable, light, and power-efficient storage nodes that, in order to operate practically, need electricity and batteries. The effectiveness and durability of IoT devices are undoubtedly greatly influenced by energy harvesting. The LEACH protocol is used along with the solar EH approach. The battery's charging and discharging curves as well as the nodes' energy state are depicted graphically. The converter receives switching pulses from the microcontroller, which also displays output current, solar panel voltage, and supercapacitor voltage. The simulation findings show that after using the solar EH approach, the battery and network lifetimes are increased

Solar Energy Harvesting for Wireless Sensor Network

Typically wireless sensor motes are operated by using small batteries because batteries are small in size and able to provide the sufficient energy for the motes. However batteries could not sustain the energy for the motes to operate for a long period. This is because the degradation of batteries could reduce the useable lifetime of the motes system. Besides that, batteries have limited energy capacity which could used up eventually. Aside from relying on the batteries to power up motes, one of the way to sustain the system is to harvest the sources of energy from the environment such as light, vibration, and thermal. These energies are renewable energy which does not cause pollution to the environment. In this project, solar energy harvesting have been proposed to sustain the energy requirement for the motes to operate. Experiments have been conducted to observe the solar panel charging characteristic to improve the efficiency and effectiveness of charging circuit to minimize the power loss. There are three main element in solar energy harvesting which are photovoltaic (PV) modules, solar charge controller and also energy storage. Based on the calculation, suitable PV modules, charging circuit and batteries are determined. Maximum Power Point Tracking (MPPT) technique are used for the charging circuit to maximize the efficiency conversion of the energy harvested from solar panel to charge the rechargeable lithium ion battery

Feasibility of wireless horse monitoring using a kinetic energy harvester model

To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric energy harvester at the leg (or in the hoof) of the horse, allowing perpetual monitoring devices. This paper reports the average power that can be delivered to the node by energy harvesting for four different natural gaits of the horse: stand, walking, trot and canter, based on an existing model for a velocity-damped resonant generator (VDRG). To this end, 33 accelerometer datasets were collected over 4.5 h from six horses during different activities. Based on these measurements, a vibrational energy harvester model was calculated that can provide up to 64.04 mu W during the energetic canter gait, taking an energy conversion rate of 60% into account. Most energy is provided during canter in the forward direction of the horse. The downwards direction is less suitable for power harvesting. Additionally, different wireless technologies are considered to realize perpetual wireless data sensing. During horse training sessions, BLE allows continues data transmissions (one packet every 0.04 s during canter), whereas IEEE 802.15.4 and UWB technologies are better suited for continuous horse monitoring during less energetic states due to their lower sleep current

Solar Energy Harvesting for Wireless Sensor Network

Typically wireless sensor motes are operated by using small batteries because batteries are small in size and able to provide the sufficient energy for the motes. However batteries could not sustain the energy for the motes to operate for a long period. This is because the degradation of batteries could reduce the useable lifetime of the motes system. Besides that, batteries have limited energy capacity which could used up eventually. Aside from relying on the batteries to power up motes, one of the way to sustain the system is to harvest the sources of energy from the environment such as light, vibration, and thermal. These energies are renewable energy which does not cause pollution to the environment. In this project, solar energy harvesting have been proposed to sustain the energy requirement for the motes to operate. Experiments have been conducted to observe the solar panel charging characteristic to improve the efficiency and effectiveness of charging circuit to minimize the power loss. There are three main element in solar energy harvesting which are photovoltaic (PV) modules, solar charge controller and also energy storage. Based on the calculation, suitable PV modules, charging circuit and batteries are determined. Maximum Power Point Tracking (MPPT) technique are used for the charging circuit to maximize the efficiency conversion of the energy harvested from solar panel to charge the rechargeable lithium ion battery

Avaliação da geração de energia elétrica em geradores termoelétricos, utilizando como fonte térmica a dissipação de calor em superfície oposta à incidente solar de painéis fotovoltaicos conhecidos

O crescimento populacional mundial tem criado demanda no setor energético e, para suprir tal necessidade, recorrer as fontes renováveis de energia tem-se tornado cada vez mais atrativo. Atualmente, a energia eólica e a energia solar estão entre as mais promissoras, e têm recebido grandes incentivos do governo. No caso da energia solar fotovoltaica (FV) observa-se um crescente número de instalações em nível global, dada sua disponibilidade energética e potencial. A sua fonte de energia é o Sol, que atua com uma temperatura de superfície próxima de 5800 Kelvin, e provê, constantemente, em média, 1367 W/m2 de irradiância fora da atmosfera terrestre. Na Terra, a potência média recebida é próxima de 1,8×1011 MW. No Brasil, nos últimos anos, o setor FV vem acompanhando a tendência mundial e, portanto, seu crescimento é expressivo. A queda dos preços dos equipamentos (conversores estáticos / módulos fotovoltaicos) e melhora nas políticas públicas e avanços na tecnologia ajudam a impulsionar a energia solar. Não obstante, a eficiência do sistema FV pode ser considerada baixa, e depende da tecnologia de fabricação das células e módulos FVs, variando atualmente de 22,3% até 26,7%. Essa baixa eficiência ocorre por causa do aumento de temperatura do efeito de fluxo de calor entre a superfície incidente do material semicondutor e a superfície oposta dos módulos. Essa temperatura que chega à face oposta do painel FV é dissipada pelo ambiente. Dentro desse contexto, este trabalho, propõe o aproveitamento da temperatura dissipada pelos módulos FVs, para produzir variação de temperatura em dispositivos denominados geradores termoelétricos (Thermoelectric Generators - TEGs). Estes, realizam conversão termoelétrica a partir do gradiente de temperatura entre suas faces. Para verificar a temperatura a que os TEGs serão submetidos, são feitas medições da temperatura em pontos da face oposta à incidência solar, de um painel FV conhecido. Após as mensurações de temperatura, a superfície do TEG será submetida, em laboratório, pela média dessas mensurações. Por fim, a partir da temperatura proposta, medições de potência serão verificadas na conversão termoelétrica, verificando a possibilidade de associação dos TEGs aos sistemas FVs, de modo a assegurar aumento na eficiência do sistema

Machine learning for accelerating the discovery of high performance low-cost solar cells: a systematic review

Solar photovoltaic (PV) technology has merged as an efficient and versatile method for converting the Sun's vast energy into electricity. Innovation in developing new materials and solar cell architectures is required to ensure lightweight, portable, and flexible miniaturized electronic devices operate for long periods with reduced battery demand. Recent advances in biomedical implantable and wearable devices have coincided with a growing interest in efficient energy-harvesting solutions. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) techniques are touted as game changers in energy harvesting, especially in solar energy materials. In this article, we systematically review a range of ML techniques for optimizing the performance of low-cost solar cells for miniaturized electronic devices. Our systematic review reveals that these ML techniques can expedite the discovery of new solar cell materials and architectures. In particular, this review covers a broad range of ML techniques targeted at producing low-cost solar cells. Moreover, we present a new method of classifying the literature according to data synthesis, ML algorithms, optimization, and fabrication process. In addition, our review reveals that the Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) enables the design of the most promising low-solar cell architecture. Therefore, our review is a critical evaluation of existing ML techniques and is presented to guide researchers in discovering the next generation of low-cost solar cells using ML techniques

Battery-aware probabilistic access scheme with nature and RF energy harvesting for cognitive radio networks

A short-range unlicensed network is intended to coexist with a strong licensed link. It is assumed that nodes' density of the primary network is much lower than that of the secondary one. Each node implements an energy-based probabilistic access scheme powered with nature and/or RF energy harvesting. The goal is to decide a criterion for each nodes' access based on a Markov chain under some constraints on the battery finite capacity and the maximum admissible interference on the primary network link. The proposed setup will be simulated in Matlab in order to evaluate and opIn this work, a short-range unlicensed link intends to coexist with a directional-nodes licensed network, deployed as a 2D Homogeneous Poisson Point Process. The unlicensed transmitter uses two energy harvesting mechanisms and implements a probabilistic scheme in order to access the channel according to the available energy in its battery. The aim of this work is to optimize the throughput of the proposed cognitive radio link, employing linear programming methods, provided that the licensed network QoS constraint is satisfied. It is demonstrated the effectiveness of the mixed energy harvesting scheme for energy-restricted scenarios, and it is tested as well in which scenarios the energy detection would be useful, considering different packet length cases. It is also considered non-energy restricted scenarios in which is demonstrated that a Vehicle-To-Vehicle link can coexist with a wireless network.En este trabajo, un enlace de corta distancia sin licencia intenta coexistir con una red de nodos direccionales licenciada, desplegada como una red de Poisson homogénea 2D. El transmisor sin licencia utiliza dos técnicas de recolección de energía e implementa un esquema probabilístico de acceso al canal de acuerdo con la energía disponible en su batería. El objetivo de este trabajo es optimizar el throughput del enlace cognitivo, a través de métodos de programación lineal, cumpliendo con la restricción de QoS impuesta por la red licenciada. Se demuestra la efectividad del esquema mixto de recolección de energía para escenarios limitados por la energía de la batería y se prueba también en que escenarios la detección de energía tiene sentido, considerando servicios con paquetes de diferente longitud. Además, se consideran escenarios no limitados por la energía de la batería, donde se demuestra que un enlace V2V puede coexistir con una red inalámbrica.En aquest treball, un enllaç de curta distancia sense llicència intenta coexistir amb una xarxa de nodes direccionals llicenciada, desplegada com una xarxa de Poisson homogènia 2D. El transmissor sense llicència utilitza dues tècniques de recol·lecció d’energia i implementa un esquema probabilístic d’accés al canal d’acord amb l’energia disponible a la seva bateria. L’objectiu d’aquest treball és optimitzar el throughput de l’enllaç cognitiu, a través de mètodes de programació lineal, garantint que la restricció de QoS imposada per la xarxa llicenciada sigui satisfeta. Es demostra l’efectivitat de l’esquema mixt de recol·lecció d’energia per a escenaris limitats per l’energia de la bateria i es prova també en quins escenaris la detecció d’energia té sentit, considerant serveis amb paquets de diferents longituds. A més a més, es consideren escenaris no limitats per l’energia de la bateria, on es demostra que un enllaç V2V pot coexistir amb una xarxa sense cables

On Optimal Online Policies in Energy Harvesting Communication Systems: Effects of Quality of Service and Battery Requirements

We study the problem of finding optimal transmission policies in a point-to-point energy harvesting communication system with continuous energy arrivals in causal setting. In particular, we investigate bounds on the long-term achievable average throughput and corresponding power policies, where energy packets of random size arrive at the transmitter at random times, modelled as a compound Poisson dam. In this work, we also account for battery life and quality of service of the users. We thus formulate non-linear constrained maximization problems. Specifically, we limit the instantaneous battery depletion rate (i.e., transmission power) as well as its variation to account for prolonging the battery life. Moreover, we limit the variation of instantaneous throughput to maintain it to a constant level to account for improving the quality of service. Using the theory of calculus of variations as a powerful mathematical tool, we derive necessary conditions in the form of first order non-linear ODEs, for local and thus global optimality of solutions to the optimization problems. We also obtain numerical as well as analytical upper bounds for the problem of constrained proper functions of transmission power. Numerically solving the ODEs for the case of a Gaussian channel, we also compute achievable throughputs and locally optimal power policies as a function of battery capacity and remaining battery charge, respectively