Improving Energy Harvesting System from Ambient RF Sources in Social Systems with Overcrowding

The utilization of ambient energy sources for powering small-scale electronic devices and sensors has garnered significant attention due to its potential for sustainable and autonomous operation. This research focuses on enhancing the efficiency of energy harvesting from ambient sources, such as vibrations, heat differentials, and electromagnetic fields, through the integration of advanced materials and nanotechnology into energy harvesting systems. The study begins with a comprehensive review of existing energy harvesting technologies, highlighting their limitations in terms of efficiency, scalability, and adaptability to various ambient energy sources. It identifies the key challenges faced in achieving high energy conversion rates and explores the potential of advanced materials and nanoscale structures to address these challenges. One aspect of the research involves the development and characterization of novel materials with superior energy conversion properties. This includes the synthesis of piezoelectric materials for vibration energy harvesting, thermoelectric materials for heat-to-electricity conversion, and nanomaterials for enhancing electromagnetic energy harvesting efficiency. Advanced characterization techniques, such as scanning electron microscopy (SEM) and X-ray diffraction (XRD), are employed to analyze the structural and electrical properties of these materials. Furthermore, the study investigates the design and fabrication of nanostructured energy harvesting devices optimized for specific ambient energy sources. This involves the integration of nanoscale components, such as nanostructured electrodes, into energy harvesting systems to improve energy capture and conversion rates. Finite element analysis (FEA) simulations and experimental testing are conducted to evaluate the performance and efficiency of these nano-enhanced energy harvesting devices under real-world conditions. The research also addresses the optimization of energy harvesting system parameters, including resonance tuning, impedance matching, and energy management circuits, to maximize energy extraction and utilization. Computational modeling techniques, such as multiscale modeling and finite element simulations, are utilized to optimize system configurations and improve overall energy harvesting efficiency. Overall, this research contributes to the advancement of energy harvesting technologies by leveraging advanced materials and nanotechnology, paving the way for sustainable and autonomous power generation from ambient energy sources for a wide range of applications.

Ambient mechanical sources typically vibrate below the frequency of 200 Hz, posing challenges for thin film piezoelectric sensors, including low power, high resonant frequency, and small bandwidth. To optimize the electrical energy harvesting from the ambient sources, it is crucial to reduce the resonant frequency of the energy harvester to match that of the ambient sources. In this study, the energy harvester’s resonant frequency dependency on proof mass is thoroughly investigated using the finite element method (FEM). Further, the FEM results are experimentally validated through a custom-designed vibration set-up. Different ambient vibration energy sources, their vibrating frequencies, and accelerations are examined to harness direct mechanical energy and convert it into electric energy using the piezoelectric sensor. Further, the effective proof mass and position are determined to achieve the targeted frequency obtained from ambient sources. Consequently, the harvester is utilized for direct energy harvesting from the ambient sources. The addition of proof mass can lower the resonant frequency of the harvester from 160 Hz to 40 Hz allowing the harvester to vibrate at maximum amplitude to obtain maximum output voltage. Significant enhancement of output power is observed after the tuning of harvester resonant frequency, harvesting a maximum output power of 19.29 μW when mechanically sourced from the bike mirror, measured at an acceleration of 4.50 g at 43 Hz.

Conventional energy architectures that utilize multiple ambient energy sources are initiated either by an external power supply or through the addition of an extra power source (e.g., battery) to the architecture. However, these interventions compromise the goal of a self-sustainable energy harvesting system. Moreover, conventional architectures are not effective in situations where space is limited (e.g., an artificial heart) or when access to this space is difficult (e.g., human implantable devices), due to their large battery size. Thus, conventional energy combiner circuits that use multiple energy sources are not well suited for supplying power to most applications. This paper presents a fully batteryless energy combiner architecture with a single inductor for the use of multiple ambient energy sources, including a solar cell and a microbial fuel cell. For each energy source, an auxiliary circuit (i.e. a charge pump) is implemented in order to provide a power supply to a digital control circuit, which consecutively connects each ambient energy source to a power converter. This novel architecture is completely self-starting and requires no additional extra power source or battery. This architecture has been designed and verified using a 0.13-$\mu$m CMOS process and a peak end-to-end efficiency of 79.33% for two ambient sources is achieved. This proposed system is applicable to numerous loads utilized in energy harvesting systems.

Energy harvesting is a process of generating energy from various ambient sources. Vibration energy harvesting has been a dynamic field of studying interest over the past decade due to the mitigation of power crisis from society. To harvest electrical energy various energy harvesting techniques have arisen. Vibration energy harvesting has been focused by researchers due to the power generation capability and high power density of vibration. Energy is crucial for stimulating sensor nodes, Sensor networks, low-power electronics and traffic regulators. As a matter of fact, the number of research on energy harvesting from vibration has increased in the last decade. This paper presents a magnificent review on various energy harvesting techniques, energy harvesting sources, state-of-the art and various challenges of vibration energy harvesting. In the theory section various renewable energy sources, energy conversion and their introduction have been presented. In the third section various energy harvesting techniques for example mechanical vibrations, electromagnetic, piezoelectric and electrostatic have been discussed. Third section has explained various challenges of energy harvesting. In the conclusion, the author tried to explain the comparison among the harvesting techniques. Finally it can be said that, all of the things such as ambient renewable sources, energy harvesting techniques and facing challenges are crucial for new coming researchers in the energy harvesting field to solve the energy exigency from the society.

Ambient radio frequency (RF) energy harvesting methods have drawn significant interests due to their ability to provide energy to wireless devices from ambient RF sources. This paper considers ambient RF energy harvesting wireless sensor networks where a sensor node transmits data to a data sink using the energy harvested from the signals transmitted by the ambient RF sources. We analyze the performance of the network, i.e., the mean of the harvested energy, the power outage probability, and the transmission outage probability. In many practical networks, the locations of the ambient RF sources are spatially correlated and the ambient sources exhibit repulsive behaviors. Therefore, we model the spatial distribution of the ambient sources as an $\alpha $ -Ginibre point process ( $\alpha $ -GPP), which reflects the repulsion among the RF sources and includes the Poisson point process as a special case. We also assume that the fading channel is Nakagami- $m$ distributed, which also includes Rayleigh fading as a particular case. In this paper, by exploiting the Laplace transform of the $\alpha $ -GPP, we introduce semi-closed-form expressions for the considered performance metrics and provide an upper bound of the power outage probability. The derived expressions are expressed in terms of the Fredholm determinant, which can be computed numerically. In order to reduce the complexity in computing the Fredholm determinant, we provide a simple closed-form expression for the Fredholm determinant, which allows us to evaluate the Fredholm determinant much more efficiently. The accuracy of our analytical results is validated through simulation results.

Successful deployment of Wireless Sensor Network (WSN) depends on the availability of power sources. Conventional battery-based WSN components has several drawbacks, such as limited life-span, bulky size and hazardous to the environment. Hence, energy harvesting from ambient sources attracts enormous attention. But energy harvesting depends on the availability of the ambient sources. In most cases energy harvesting from a single source is not enough to produce sufficient energy to power up WSN components. This paper describe about the design, and implementation consideration of a hybrid energy harvester for an autonomous sensing system. The sensing components of WSN are connected with the hybrid energy harvester on the same structure to generate required energy from the ambient environment such as solar and chemical reaction. As a case study, the power requirements of in-house developed WSN components [1] are measured. Based on the power requirement a hybrid energy harvester based autonomous system is designed [2], and a functional prototype of the system is implemented. In the implemented prototype, energy is being harvested from the ambient solar and chemical sources. From the evaluation of the developed system, it is found that powering WSN components, hybrid energy harvester produces an additional amount of 10491.93 J (equivalent to 2.91 Wh) of energy, which is capable to fill-up a 971 mA-hr storage in one day operation. This is enough for the WSN components to draw power subsequently, when there is not enough ambient sources available for next few days.

This paper reviews innovative methodologies in the realm of renewable energy harvesting and storage from ambient sources. One focal area is the untapped potential of water wave energy, a globally distributed renewable energy source. Current technologies, primarily reliant on electromagnetic generators, face challenges, especially in irregular environments and at low frequencies. However, the advent of the triboelectric nanogenerator (TENG) offers a promising solution, especially for low-frequency water wave motions. TENG technology not only presents a new avenue for large-scale blue energy harvesting but also signifies a shift towards the era of the Internet of Things, where energy is derived from various sources including human motion and vibrations. Another domain delves into the history and evolution of energy harvesting, tracing back to traditional methods like water wheels and windmills. The modern era witnesses a resurgence in interest due to advancements in lowpower electronics, wireless standards, and miniaturization. Lastly, wearable biosensors, poised to redefine personalized healthcare and telemedicine, necessitate innovative power solutions. Wearable energy harvesters, capable of converting ambient energy sources into electricity, are emerging as pivotal components in self-powered wearable sensors, paving the way for real-time health monitoring and human-machine interfaces.

Harvesting small scale energy from otherwise wasted ambient energy sources has attracted immense research efforts for battery-powered wireless sensor networks for various applications such as structural health monitoring, industrial condition monitoring and healthcare. The power level which those applications may reach ranges from microwatts to milliwatts. Energy scavenged from ambient sources may be able to recharge or even eliminate the battery to power up those devices perpetually. Sources of energy for harvesting include, but are not limited, to light, thermal gradient, vibration, and radio frequency radiation. The fluctuation and intermittence of these ambient energy sources bring in challenging technical issues to make self-powered systems practical. Energy storage devices like rechargeable batteries or supercapacitors and efficient power management circuitry are indispensable to convert a dynamic environmental energy input into a stable power source. This presentation reviews principles of energy harvesting and practices for small scale energy harvesters and self-powered wireless sensor modules developed recently. Industry trends and possible research issues for further developments are discussed in order to give a technical insight into energy harvesting techniques and their applications.

In this paper the performance of a single port antenna array is analysed for energy harvesting. Two types of feeds used to feed a flexible patch antenna array are compared. The array consists of 4 rectangular patch antennas made from conductive fabric and Polydimethylsiloxane (PDMS), which are flexible. The proposed array is for energy harvesting for wearable systems. Energy harvesting from ambient energy sources needs an antenna array for collecting a sufficient power level and for wearable applications the array materials must be flexible. For this reason, conductive fabric and PDMS are chosen for conductive and non-conductive parts of the array. However, the array can be fed from a single port signal or individual elements can be fed from simultaneous individual port signals. If the array with a single port has good electromagnetic performance then it is more useful for energy harvesting because it requires single rectifier, which will reduce microwave to DC conversion loss. In this paper these two types of feeding methods are compared with respect to array matching, peak gain and radiation performance. From this comparison we can observe the performance of single port array system for energy harvesting in wearable applications.

The advent of Internet of Things (IoT) technologies has revolutionized the concept of smart buildings, integrating diverse sensors and devices for enhanced automation and efficiency. Avinash B. Raut, Energy harvesting and storage technologies have emerged as promising solutions to address the power requirements of IoT devices in smart buildings. This abstract provides a comprehensive overview of machine learning-driven energy harvesting and storage system design tailored specifically for IoT applications in smart buildings. Traditional energy management systems often face challenges such as suboptimal energy utilization, limited scalability, and lack of adaptability to dynamic environmental conditions. In contrast, machine learning techniques offer adaptive, data-driven solutions to optimize energy harvesting, storage, and distribution in smart buildings. [1] This paper explores various machine learning algorithms, including supervised learning, reinforcement learning, and deep learning, and their application in optimizing energy harvesting from ambient sources such as solar, kinetic, and thermal energy. Moreover, machine learning enables predictive energy demand modeling by analyzing historical data and environmental factors, thus enhancing the efficiency of energy storage and distribution systems. Real-world case studies and experimental results are presented to demonstrate the effectiveness and potential of machine learning-driven energy management systems in improving energy efficiency, reliability, and autonomy in IoT-enabled smart buildings DOI: https://doi.org/10.52783/tjjpt.v45.i02.6318

Systems that rely on energy harvested from ambient sources are gaining increased attention due to their possible usage in low-power applications. Owing to the unreliable nature of such ambient energy sources, these systems suffer from supply voltage degradation and power interruptions. Therefore, the computational scheme for such energy-harvesting systems is divided into small incremental steps along with nonvolatile memory elements that conserve the data between subsequent power interruptions. Toward that end, we propose a new nonvolatile flip-flop (NVFF) that exhibits better energy efficiency and denser area compared to previous designs. The NVFF utilizes a single spin Hall effect-based magnetic tunnel junction (SHE-MTJ) because of its favorable device characteristics, like high spin injection efficiency and decoupled read-write paths. We also propose a new restore mechanism, wherein a CMOS inverter is used as a gain element to attain reliable restore operation with single SHE-MTJ per NVFF. A detailed device-circuit simulation, including variational analysis, proves the reliability of the proposed NVFF.

Internet of things (IoT) devices are popular in several high-impact applications such as mobile healthcare and digital agriculture. However, IoT devices have limited operating lifetime due to their small form factor. Harvesting energy from ambient sources is an effective method to supplement the battery. Energy harvesting necessitates development of energy management policies to manage the harvested energy. Designing optimal policies for energy management is challenging for two key reasons: (1) ambient energy sources are highly stochastic; therefore, energy management policies must consider the associated uncertainty; (2) energy management policies must consider future energy availability while making decisions to ensure that sufficient energy is available when there is no ambient energy. Prior approaches typically consider energy in the immediate future (e.g., 1 hour) and do not account for the uncertainty in future energy harvest. This article proposes novel machine learning and dynamic optimization-based approaches to handle the two challenges. Specifically, we first develop a novel set of features and use it in a low-power neural network architecture to predict future energy availability and uncertainty. The energy predictions and uncertainty are used in a dynamic optimization algorithm to optimally allocate the harvested energy. Experiments on solar energy data over 5 years from Golden, Colorado, show that the proposed energy prediction model achieves 3.4 J mean absolute error while having a coverage of 80%. Moreover, our energy management algorithm provides energy allocations that are within 2.5 J of an optimal Oracle with 2.65 mJ to 36.54 mJ of energy overhead.

This paper presents ultra low power hybrid energy harvesting start-up circuit for 1V , implemented in a standard 0.18 µm CMOS technology. It consists of solar and RF energy harvesters, capable to harvest ambient solar and RF sources respectively. The start-up circuit enables operation of the system even in the absence of any one of these ambient energy sources. The integration of solar and RF on a common platform provides improved charging time in crucial start-up phase. In general, to charge the super capacitor (Cstartup=100pF) to 1V , the solar energy harvester alone (with minimum input voltage of 317mV ) will take a charging time of 542.3µs. With the addition of RF interface, the charging time is drastically reduced to 86.8µs, for -26dBm input power level, at 953MHz providing a 83.9% reduction in start-up time. The start-up circuit delivers output power of 7.4µW with an efficiency of 68.5% for the combined solar and RF input at 317mV and −20dBm respectively.

For wireless sensor network nodes (WSNNs), which are usually distributed over wide area and transmit intermittently, radio frequency energy harvesting (RFEH) from ambient sources such as base stations, TV towers, access points etc. seem to be practical and promising approach. Such RFEH enabled WSNNs need intelligence to choose optimal frequency band(s) for RFEH from wideband spectrum. Furthermore, for minimizing energy consumption, number of frequency band switching (FBS) should be as minimum as possible. To meet these requirements, a new multi-stage decision making policy (DMP) for RFEH enabled WSNNs has been proposed in this paper. The proposed DMP uses Bayes-UCB algorithm to identify the subbands with high RFEH potential. Another novelty of the proposed DMP is that RFEH duration is made tunable based on the RF potential of the chosen subband(s) leading to significant improvement in harvested energy and number of FBS. Simulation results show that the proposed DMP offer 10%–18% improvement in total harvested RF energy. Furthermore, the number of FBS in the proposed DMP are 25%–50% of that in existing DMPs making the proposed DMP a preferred choice for resource-constrained WSNNs.

The demand for power is increasing due to the rapid growth of the population. Therefore, energy harvesting (EH) from ambient sources has become popular. The reduction of power consumption in modern wireless systems provides a basis for the replacement of batteries with the electromagnetic energy harvesting (EMEH) approach. This study presents a general review of the EMEH techniques for autonomous sensor (ATS) applications. Electromagnetic devices show great potential when used to power such ATS technologies or convert mechanical energy to electrical energy. As its power source, this stage harvests ambient energy and features a self-starting and self-powered process without the use of batteries. Therefore, it consumes low power and is highly stable for harvesting energy from the environment with low ambient energy sources. The review highlights EMEH circuits, low power EMEH devices, power electronic converters, and controllers utilized in numerous applications, and described their impacts on energy conservation, benefits, and limitation. This study ultimately aims to suggest a smart, low-voltage electronic circuit for a low-power sensor that harvests electromagnetic energy. This review also focuses on various issues and suggestions of future EMEH for low power autonomous sensors.