LoLiPoP-IoT: Advancing the energy-efficient Internet of Things

Renewable energy sources, such as wind, tide, solar cells, etc, are the primary research areas that deliver enormous amounts of energy for our daily usage and minimize the dependency upon fossil fuel. Paralley, harnessing ambient energy from our surroundings must be prioritized for small powered systems. Nanogenerators, which use waste energy to generate electricity, are based on such concepts. We refer to these nanogenerators as energy harvesters. The purpose of energy harvesters is not to outcompete traditional renewable energy sources. It aims to reduce reliance on primary energy sources and enhance decentralized energy production. Energy storage is another area that needs to be explored for quickly storing the generated energy. Supercapacitor is a familiar device with a unique quick charging and discharging feature. Encouraging advancements in energy storage and harvesting technologies directly supports the efficient and comprehensive use of sustainable energy. Yet, self-optimization from independent energy harvesting and storage devices is challenging to overcome. It includes instability, insufficient energy output, and reliance on an external power source, preventing their direct application and future development. Coincidentally, integrating energy harvesters and storage devices can address these challenges, which demand their inherent action. This review intends to offer a complete overview of supercapacitor-based integrated energy harvester and storage systems and identify opportunities and directions for future research in this subject.

Wireless Sensor Networks (WSNs) have drawn a lot of interest from a variety of industries, such as industrial automation, healthcare, and environmental monitoring. Typically, these networks are made up of sensor nodes that run on batteries and depend on energy-efficient operation to extend their lifetime. Renewable and sustainable energies are suitable for wireless sensor networks. Energy harvesting from dispersed renewable sources, such as solar, wind, biomass, and vibration, has emerged as a possible approach to alleviate the limits associated with limited battery life. The state-of-the-art methods and difficulties associated with energy harvesting in wireless sensor networks (WSNs) from a variety of distributed renewable sources, such as solar, wind, vibration, and temperature gradients, are thoroughly reviewed in this study. The paper discusses the many techniques for extracting and converting energy from these sources, highlighting the benefits and drawbacks of each. This paper explores several energy harvesting techniques and challenges. The study also discusses the difficulties in integrating energy harvesting, including adaptive power management, energy forecast, intermittent energy supply, and integration issues. The assessment also highlights research gaps and potential future initiatives in the field of energy harvesting from renewable sources. Researchers, technologists, and policymakers working in the fields of renewable energy and wireless sensor networks would find this thorough assessment to be quite insightful. It illuminates how energy harvesting technologies may improve sensor network autonomy and sustainability, leading to breakthroughs in environmental monitoring and other vital applications. The development of sustainable, independent, and effective sensing systems is greatly aided by the investigation of methods and obstacles in energy harvesting for wireless sensor networks. In addition to addressing current issues, this research opens doors for innovation, fostering a more sustainable approach to data collection and monitoring, and having a positive effect on a number of industries.

Energy harvesting is one of the most rapidly growing of the emerging technologies. This field has arrived at the hybrid and multi-source era, where hybrid structures and novel materials are able to boost the energy conversion efficiency and/or make the harvesters capable of benefitting from multiple energy sources simultaneously. Such hybrid and multi-source energy harvesters have not frequently been reviewed in the past, potentially because of the small number of publications compared to that of their single-source and individual counterparts. However, as their number is becoming larger, it is now necessary to give sufficient and frequent reviews of developments in the field. Furthermore, an increasing number of developed energy harvesters are moving out of the laboratory into industrial markets. In practice, energy harvesters need to be integrated with energy storage and/or end users such as sensors and wireless sensor networks. Therefore, the harvester-storage and harvester-sensor integration systems also need to be reviewed frequently. This mini-review includes works reported in the first half of 2018 and provides a timely update to the published review. It focuses on the above-mentioned hybrid and multi-source energy harvesters as well as on integrated harvesters, energy storage systems and end users (e.g. sensors), including CMOS (complementary metal-oxide-semiconductor) technology-based harvesters and systems.

The increasing development of ingestible medical devices for gastrointestinal diagnostics, drug delivery, and physiological monitoring has created a growing demand for reliable and long-lasting power sources. Conventional batteries limit device lifetime, increase capsule size, and raise safety concerns, making biomechanical energy harvesting from gastrointestinal motility a promising alternative for self-powered ingestible systems. This review aims to provide a comprehensive overview of biomechanical energy harvesting from gastrointestinal mechanical activity for powering ingestible biomedical devices, with emphasis on energy sources, transduction mechanisms, materials, system integration, limitations, and future research directions. Recent literature on gastrointestinal biomechanics and energy harvesting technologies was analyzed, focusing on major transduction mechanisms such as piezoelectric, triboelectric, and electromagnetic generators. The review also evaluates material selection, device architectures, encapsulation strategies, and power management circuits from a system-level integration perspective. Piezoelectric, triboelectric, and electromagnetic energy harvesters demonstrate the ability to convert low-frequency gastrointestinal mechanical energy into electrical energy suitable for ultra-low-power biomedical devices. Hybrid energy harvesting systems improve energy reliability and output performance. However, several challenges remain, including low energy density, variability in gastrointestinal mechanical forces, miniaturization constraints, material durability, electrical conversion losses, and lack of standardized testing protocols. Biomechanical energy harvesting has significant potential to enable battery-free ingestible biomedical devices. Future developments in hybrid energy systems, ultra-low-power electronics, biodegradable materials, and adaptive power management are expected to support the development of fully autonomous self-powered ingestible medical devices.

Highly electronegative borophene/PVDF composite hybrid nanofibers based triboelectric nanogenerator for self-powered sensor for human motion monitoring and energy harvesting from rain

—In recent years development of sustainable energy sources is getting extensive research interest due to the ever-growing demand for energy. As an alternative energy source to power small electronic devices, ambient energy harvesting from vibration or human body motion is considered to be a potential candidate. Despite the enormous progress in the field of battery research in terms of safety, lifecycle, and energy density in about three decades, it has not reached the level to conveniently power wearable electronic devices such as smart watches, bands, hearing aids, etc. For this reason, the development of self-charging power units with excellent flexibility, and integrated energy harvesting and storage is crucial. Self-powering is a key idea that makes possible the system to operate sustainably, which is now getting more acceptance in many fields in the area of sensor networks, internet of things (IoT) and implantable in-vivo medical devices.For solving this energy harvesting issue, the self-powering nanogenerators (NGs) were proposed and proved their high effectiveness. Usually, sustainable power is delivered through energy harvesting and storage devices by connecting them to the power management circuit, as for energy storage, Li-ion battery (LIB) is one of the most effective technologies. Through the movement of Li ions under the driving of an externally applied voltage source, the electrochemical reactions generate the anode and cathode, storing the electrical energy as the chemical energy. In this paper, we present a simultaneous process of converting the mechanical energy into chemical energy in a way that NG and LIB are combined as an all-in-one power system.The electrospinning method was used as an initial step for the development of such a system with β-PVDF separator. The obtained film showed promising voltage output at different stress frequencies. X-Ray diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FT-IR) analysis showed a high percentage of β phase of PVDF polymer material. Moreover, it was found that the addition of 1 wt.% of BTO (Barium Titanate) results in higher quality fibers. When comparing pure PVDF solution with 20 wt.% content and the one with BTO added the latter was more viscous. Hence, the sample was electrospun uniformly without any beads. Lastly, to test the electrical output of such film, a particular testing device has been developed. With this device, the force of a finger tap can be applied at different frequencies, so that voltage generation across the surfaces of the film is validated. Further, this piezoelectric PVDF film can be used as a separator for LIB. Acknowledgements This work was supported by the project 240919FD3914 “Self-Charging Rechargeable Lithium-ion Battery” from Nazarbayev University, Kazakhstan.

In this paper, it is integrated a piezoelectric energy harvester and a supercapacitor storage device on a flexible substrate with a connection through an innovative alternative current (AC) to direct current (DC) boosting power management system for wearable biosensors' power supply. Flexible substrates can conform to irregular surfaces or shapes, enabling energy harvesting and storage devices to be integrated into a variety of form factors, including curved or bendable surfaces. Having an integrated energy harvester and storage system ensures a reliable and portable power source, providing power autonomy. The proposed element was layer-by-layer design including silver electrode, polyvinylidene fluoride-trifluoroethylene/multiwall carbon nanotubes, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate: carbon nanotubes, aluminium oxide, graphene and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate: carbon nanotubes (Ag/PVDF-TrFE:MWCNT/PEDOT:PSS:CNT/Al2O3/Gr/PEDOT:PSS:CNT), prepared by spray coating. A voltage rectifier with a low-pass filter and a direct current to direct current (DC-DC) converter was used as a power management system and intermediate unit between the harvester and storage part of the element. The type of the electronic circuit is voltage-doubler rectifier. It was found that piezoelectric harvester can generates voltage with a magnitude of 2V at loading of 110g/cm2@10Hz and with the proposed electronic circuit can be determined the workability of the created element during repeated charging and discharging, without introducing interfering changes in the capacity. The behaviour of the supercapacitor part is dependent on the thickness of Al2O3 and demonstrates more favourable characteristics at the thicker film of 750nm, where the charging time is short (6s), the voltage ripples are small (±0.50mV), and the maximum output voltage after charging almost reached the input supply voltage (∼1.94V output voltage at 2V input voltage). In addition, it resists up to 15500 cycles and shows a stable retention capacitance of 1.63mF. The devices retain their capacity at multiple bending (1000) to 93% and 91%, according to the aluminium oxide film thickness, which is suitable for wearable devices.

The burgeoning fields of smart structures and soft robotics promise a future of adaptive, compliant, and intelligent systems for applications ranging from biomedical implants to remote environmental monitoring. However, a critical bottleneck constraining their long-term deployment and autonomy is the reliance on conventional, rigid batteries, which necessitate frequent recharging or replacement, thereby limiting operational lifespan and increasing maintenance complexity. This review article posits that the integration of energy harvesting technologies directly into the fabric of smart structures and the constitutive materials of soft robots is the pivotal step towards realizing truly self-sufficient systems. We present a comprehensive analysis of the synergy between three core domains: advanced energy harvesting mechanisms (piezoelectric, triboelectric, pyroelectric, and bio-chemical), multifunctional smart materials (shape memory alloys, electroactive polymers, hydrogels), and the architectural principles of soft robotics. The article systematically reviews recent breakthroughs in material science that enable dual-functionality, where a material can simultaneously act as a sensor, an actuator, and an energy harvester. We explore innovative system-level designs that transform structural deformations, ambient thermal fluctuations, and even biochemical energy from the environment into usable electrical power to sustain onboard computation, sensing, and actuation. Furthermore, the paper addresses the significant challenges of power management, energy storage at the micro-scale, and system-level integration efficiency. By synthesizing the current state-of-the-art and projecting future research trajectories, this work aims to provide a foundational framework for the next generation of autonomous, maintenance-free intelligent systems capable of perpetual operation in unpredictable and inaccessible environments.

Increasing the bandwidth of the vibration energy harvesters is one of the research emphases to maximize the energy harvested from the ambient. Here we design a Two-Degree of Freedom Vibro-impact Triboelectric energy harvester with a double-impact configuration, which combines multi-modality and piecewise linearity to improve the harvesting bandwidth of triboelectric energy harvesters. The harvester structure consists of primary and secondary cantilever beams with two integrated energy harvesters. The two beams are designed to operate at close natural frequencies, and under the effect of the impact, triboelectricity is generated, and the bandwidths of the resonators are combined to create a wide bandwidth. The double impact system is investigated numerically to examine the structure’s dynamic behavior at different excitation levels, separation distance, and surface charge density to extract an optimal parameter for achieving a wide combined bandwidth. The system demonstrates the capability of connecting multi-modality and piecewise linearity to significantly broaden the triboelectric energy harvester’s bandwidth.

Brain–computer interfaces (BCIs) have gained popularity in recent years. Among noninvasive BCIs, EEG-based systems stand out as the primary approach, utilizing the motor imagery (MI) paradigm to discern movement intentions. Initially, BCIs were predominantly focused on nonembedded systems. However, there is now a growing momentum towards shifting computation to the edge, offering advantages such as enhanced privacy, reduced transmission bandwidth, and real-time responsiveness. Despite this trend, achieving the desired target remains a work in progress. To illustrate the feasibility of this shift and quantify the potential benefits, this paper presents a comparison of deploying a CNN for MI classification across different computing platforms, namely, CPU-, embedded GPU-, and FPGA-based. For our case study, we utilized data from 29 participants included in a dataset acquired using an EEG cap for training the models. The FPGA solution emerged as the most efficient in terms of the power consumption–inference time product. Specifically, it delivers an impressive reduction of up to 89% in power consumption compared to the CPU and 71% compared to the GPU and up to a 98% reduction in memory footprint for model inference, albeit at the cost of a 39% increase in inference time compared to the GPU. Both the embedded GPU and FPGA outperform the CPU in terms of inference time.

The emergence of the fifth generation of communication networks (5G) has paved the way for the fast development of Internet of Things (IoT) as well as many real‐time and mission‐critical applications. Because the status update required to be as timely as possible, a new metric termed as age of information (AoI) was born with the ability of capturing the timeliness. However, in practical implementation the sensor is energy constrained and transmits very low‐power signals, which yield high AoI so that the received status update can be stale or even outdated. To overcome this problem, this work proposes an IoT system model by integrating energy harvesting (EH), amplify‐and‐forward (AF) relay cooperation, and short packet communication (SPC) techniques. In the proposed scheme, the relay that is connected to fixed power grid, transfers energy radio frequency signals to charge the sensor and assists forwarding status update by using time switching scheme. The employment of SPC overcomes the freshness loss caused by relay cooperation and EH. Then, this work investigates the AoI and energy efficiency of the proposed scheme under finite block length communications over Nakagami‐fading channels by specially considering channel state information (CSI) and nonlinear EH model. Under these practical constraints, the tractable AF relay transmission model and the end‐to‐end approximated signal‐to‐noise ratio are found. The end‐to‐end block error probability of finite block length transmission is derived, too. Third, under the simultaneous consideration of nonlinear EH and outdated CSI, the statistical descriptions of the time duration for the sensor fully charging its battery are derived as well as the ones of the time intervals of update packet delivery. As a result, the average AoI is achieved with closed‐form expression and the tradeoff model of AoI to energy efficiency is established by simultaneously exploiting outdated CSIs and nonlinearity of EH circuit. The presented numerical analysis exploits the impact of the outdated CSIs and nonlinear EH model and gives the insights of such model.

The need for sustainable and long-term environmental monitoring has driven the development of energy-autonomous sensors, which either operate passively or integrate energy harvesting (EH) solutions. In many applications, the energy cost of data transmission is a critical factor in autonomous sensing systems. To address this challenge, optical passive sensors, which exploit changes in reflectivity to monitor physical parameters, offer self-sustained operation without requiring an external power source. Similarly, RF-based passive sensors, both chipless and with minimal circuitry, enable wireless monitoring with low power consumption. When more energy is available, EH techniques can be combined with active optical sensors. Infrared laser-based CO2 sensors, as well as drone-mounted optical systems, demonstrate how EH can power precise environmental measurements. Beyond optics, other sensing modalities also benefit from EH, further expanding the range of self-powered environmental monitoring technologies. This review discusses the trade-offs between passive and EH-assisted sensing strategies, with a focus on optical implementations. The outlook highlights emerging solutions to enhance sensor autonomy while minimizing the energy cost of data transmission, paving the way for sustainable and scalable environmental monitoring.

Concurrent high force detection accuracy and extended battery lifetime are strongly expected in wearable gait monitoring systems, which are important for many Internet of Health Things (IoHT) applications. In this article, a piezoelectric insole device and rectifying circuitry based technique is presented to achieve these two ultimate goals. Here, walking induced positive and negative charges are separated for plantar stress detection and energy harvesting respectively, realizing the two functions concurrently. Experimental results demonstrate that first, the high detection sensitivity of 55 mN and responsivity of 231 mV/N are achieved, satisfying the need for diagnosing various diseases; second, energy of 1.6 pJ is stored during a walking event, consequently extending the battery lifetime. The developed technique enhances the development of gait monitoring in IoHT.

Energy availability and long term operation are key challenges for wireless sensor networks and for all the applications where the devices are battery operated. For this reason energy harvesting is becoming very important for powering ubiquitously deployed sensor networks and mobile electronics. One of most important goal for the next generation of power supply units for standalone embedded systems is to power nearly perpetually the devices when the scavenger is exposed to reasonable environmental energy conditions. However, due to the unpredictable nature of the environmental sources, prolonged lacks of energy intake usually happen. The last frontiers of perpetual operating systems is combining different energy harvesters in a single unit and using green energy supply with high energy density as micro hydrogen fuel cells. In this paper we introduce a Smart Power Unit (SPU) for embedded system which incorporates energy harvesters from sun and wind and uses hydrogen fuel cell as alternative energy storage. The power unit can work as a long-term battery or providing serial communication to exchange power information and to perform power management. In fact the core of the SPU is an ultra low power micro controller which is in charge to do the power activities such as Maximum Power Point Tracking for the harvesters, fuel cell activation, energy prediction, adaptive power management on board, battery monitoring and communications with powered systems. Experimental results and simulations shows the high efficiency (up to 90 %) of the power conversion subsystem. Finally a real deployment in a structural health monitoring site in Switzerland shows as the energy neutral condition is achieved on field.KeywordsFuel CellWireless Sensor NetworkPower UnitEnergy HarvesterStructural Health MonitoringThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Energy harvesting offers an important design option for creating sensing and control elements without a requirement for custom wiring or batteries. An exciting possibility creates a “self-powered” sensor node with an integrated energy harvester that can extract power from the magnetic fields around a power line to a load, in the manner of a current transformer. However, this “current transformer” provides not just current sensing, but also power for a sensor package, all without ohmic contact. This paper provides a technique for design optimization for maximizing power harvest, revealing a critical result: For any given core in any particular application, power harvest is maximized when the core is permitted to saturate at an opportune time in the line cycle. Circuits for optimizing this power transfer window and experimental results supporting the analysis are presented in this paper.