Description:FIELD OF INVENTION
The field of invention is AI-driven energy harvesting, sustainable power solutions, and smart energy management for wireless IoT sensor networks.
BACKGROUND OF INVENTION
Wireless IoT sensors are essential for various applications, including environmental monitoring, industrial automation, and smart cities. However, their reliance on batteries presents challenges related to limited lifespan, frequent replacements, and environmental concerns. Existing energy harvesting models use solar, thermal, piezoelectric, and radio frequency (RF) energy sources to extend sensor operation. While these methods improve power sustainability, they often suffer from low energy conversion efficiency, intermittent power supply, and suboptimal energy management.
Traditional models lack intelligent optimization, leading to inefficient energy utilization and unreliable sensor performance. AI-driven energy harvesting systems address these limitations by integrating machine learning algorithms to predict energy availability, optimize power allocation, and dynamically adjust sensor operations based on environmental conditions. By combining multiple energy sources and AI-driven management, the proposed system ensures a sustainable, self-sufficient power supply for wireless IoT sensors, reducing dependence on batteries and enhancing operational efficiency in diverse applications.
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SUMMARY
The AI-driven energy harvesting system is designed to provide a sustainable and self-sufficient power supply for wireless IoT sensors by integrating artificial intelligence (AI) with multi-source energy harvesting. The system utilizes renewable energy sources such as solar, piezoelectric, thermal, and RF energy to power IoT sensors. AI algorithms optimize energy collection, storage, and distribution by predicting energy availability, adapting to environmental changes, and dynamically managing power consumption. This approach enhances sensor longevity, reduces dependence on batteries, and ensures continuous operation in remote or inaccessible locations.
Objective of the Invention
The primary objective is to develop an intelligent, energy-efficient system that maximizes energy harvesting from multiple renewable sources and optimizes energy utilization using AI. The system aims to improve the reliability and sustainability of IoT sensor networks, extend their operational lifespan, reduce maintenance costs, and minimize environmental impact by reducing battery waste.
DETAILED DESCRIPTION OF INVENTION
Over the past few decades, electronic devices have undergone significant transformation, driven by advancements in sensing technology. Microelectromechanical systems (MEMS) have enabled the development of billions of miniature sensors with compact designs, low power consumption, and diverse functionalities. This evolution has made electronic devices smarter, lighter, and more efficient. Meanwhile, the rapid growth of wireless communication, particularly in the 5G era, has interconnected massive numbers of sensors, paving the way for IoT, Industry 4.0, smart cities, and smart homes, significantly enhancing convenience and intelligence in daily life.
However, powering these vast networks of wireless sensor nodes presents a major challenge, especially when sensors are deployed in harsh environments such as within the human body, on high-rise buildings, or in underground tunnels. Traditional batteries, while widely used, face limitations including low power density, the need for frequent replacement, and environmental concerns. These drawbacks hinder the scalability of IoT systems and drive research toward self-sustainable solutions through energy harvesting and self-powered sensing.
Energy harvesters (EHs) have been explored since the early 1900s as an alternative or supplementary power source, converting ambient energy into electricity. Key energy sources include mechanical motion and vibrations, thermal gradients, radiant energy from sunlight and electromagnetic waves, and biochemical energy from biological processes. MEMS-based energy harvesters, gaining traction since the 1990s, have focused on vibration energy harvesters (VEHs) utilizing electrostatic, electromagnetic, and piezoelectric mechanisms, as well as thermoelectric energy harvesters (TEHs) that generate electricity from temperature differences. Researchers have worked on improving their efficiency through structural innovations such as nonlinear springs, frequency upconversion, and novel material designs.
Since the 1980s, MEMS sensors have been widely commercialized, initially based on silicon and capacitance variance for accelerometers, gyroscopes, and pressure sensors. The 1990s saw the rise of piezoelectric-based MEMS sensors, which convert mechanical stimuli into electrical signals and vice versa. These have been applied in high-precision sensing and actuation technologies.
With the expansion of IoT applications, sensor deployment now extends beyond industrial settings to wearable and implantable devices that monitor physiological parameters such as movement, temperature, respiration, blood pressure, and heart rate. However, traditional MEMS-based energy harvesters and sensors face limitations in meeting these new demands. MEMS-based EHs typically operate in high-frequency ranges and produce only microwatt-level power, whereas human motion is low-frequency and irregular, requiring milliwatt-level energy for wearable devices. Additionally, while MEMS sensors offer high accuracy and stability, the vast data they generate increases power demands for collection, processing, and transmission. Addressing these challenges requires further advancements in self-powered sensor technology and energy-efficient IoT solutions.

Figure 1: Evolution of energy harvesting technologies
Self-Powered Sensors and Wearable Energy Harvesting Solutions
To serve as promising platforms for self-powered sensors and flexible energy harvesters (EHs) for wearable and implantable devices, piezoelectric nanogenerators (PENG) and triboelectric nanogenerators (TENG) were proposed in 2006 by Wang et al. and 2012 by Fan et al., respectively.
Piezoelectric Nanogenerators (PENG)
PENGs operate based on piezoelectric materials with nanometer structures such as zinc oxide nanowires (NWs), which offer high sensitivity, durability, and large power density. Various piezoelectric materials have been developed, including single-crystal piezoelectric ceramic lead magnesium niobate-lead zirconate titanate (PMN-PZT), barium titanate (BaTiO3), zinc oxide (ZnO), poly(vinylidene fluoride) (PVDF), and its copolymer poly(vinylidene fluoride-trifluoroethylene) (PVDF-TrFE).
Since 2006, when Wang et al. first utilized ZnO nanowire arrays to develop PENGs for electricity generation from ambient movements, several structural designs have been proposed for wearable textile PENGs, primarily:
• Layer Stacking: A simple fabrication method consisting of a piezoelectric material layer and two electrode layers. Common piezoelectric layers include vertically grown ZnO nanorods, PVDF nanofibers, and PZT thin films. However, the multilayer structure limits wearability and breathability.
• Yarn Intersection: A more comfortable alternative where piezoelectric fibers are used to ensure wearability. Fiber-based textile PENGs have undergone optimizations, such as combining traditional fibers with PENG fibers to prevent short circuits, using BaTiO3 for higher output, and employing 3D textile structures to enhance deformation and energy generation.

Triboelectric Nanogenerators (TENG)
TENGs operate based on the triboelectric effect, a coupling of contact electrification and electrostatic induction due to the potential difference between materials. They stand out due to their broad material choices and simple fabrication processes. TENGs function in four main working modes:
• Vertical Contact-Separation Mode
• Contact-Sliding Mode
• Single-Electrode Mode
• Freestanding Mode
Several optimization strategies have been explored to enhance TENG efficiency and output:
• Structural Optimization: Bioinspired designs, such as photoelectric-electromechanical TENGs, introduce photogenerated electrons for increased surface charges, significantly improving current density.
• Material Enhancements: Using piezoelectric charges (e.g., from PCDF layers) to boost potential differences and electron transfer efficiency.
• Charge Multiplication Techniques: Applying Bennett’s doubler structure, which continuously doubles small initial charges through sequential contact-separation operations.
• Voltage Regulation: To address air breakdown caused by high output voltage, buffer capacitors have been used to regulate charge density effectively.
Both TENG and PENG do not require complex MEMS fabrication, making them cost-efficient and environmentally friendly. They can generate electricity while sensing mechanical parameters such as pressure, rotation angle, and vibration frequency, making them suitable for self-powered sensors.

Other Energy Harvesting Technologies
Besides TENG and PENG, several other energy harvesting technologies contribute to self-powered applications:
• Photovoltaic Energy Harvesting: Converts solar energy into electricity through semiconductor-generated electron-hole pairs. Used in self-powered gas sensors, photodetectors, and electronic skin.
• Thermoelectric Energy Harvesting: Converts thermal gradients into electricity, enabling thermoelectric self-powered wearable electronics, mercury ion sensors, and electronic skin.
• Pyroelectric Energy Harvesting: Converts temperature fluctuations into electricity for self-powered breathing and temperature sensors.
Integration with IoT and Wearable Technology
The evolution of energy harvesting and sensor technologies has facilitated self-powered IoT applications. Research efforts have focused on:
• Highly Efficient Energy Storage Units: Supercapacitors to bridge the gap between EH output and electronics power consumption.
• Multiple Reliable Self-Powered Sensors: Chemical and mechanical sensors powered by nanogenerators.
Challenges remain in improving energy efficiency and utilization in IoT devices. System-level innovations are essential to achieving a truly self-sustained IoT ecosystem.
Applications in Wearable Energy Harvesting
Wearable EHs have been widely explored for capturing energy from human body movements, respiration, and biochemical reactions. Some notable devices include:
• Exoskeleton-Based TENG Sensors: Enable multiple degrees of freedom in human motion sensing.
• Smart Glove with TENG Sensors: Provides piezoelectric haptic feedback for human-machine interfaces.
• Lower-Limb Rehabilitation System: Uses both TENG and PENG for biomechanical energy harvesting.
• TENG-Based Insole: Captures unused walking energy.
• Smart Sock with TENG Output: Monitors human motion status and user identity.
• Self-Powered Watch: Utilizes TENG and electromagnetic generators (EMG) for energy harvesting.
• Multi-Functional Armband with TENG: Enables wireless communication and vehicle control.
• Self-Powered Air Filter Mask: Integrates TENG technology for enhanced filtration.
• Eye-Motion-Based Communication System: Utilizes TENG for mechanosensation communication.
• Sweat-Based Hybrid Textile Device: Harvests biochemical energy from sweat.
• Wearable Perovskite Solar Cells: Scavenge infrared energy from light.
• Hybrid Thermo-Triboelectric Generators: Simultaneously utilize mechanical and thermal energy from body movements and heat.
Self-powered sensors and energy harvesters like TENG and PENG play a crucial role in wearable and implantable devices. Their integration with IoT systems paves the way for a new era of self-sustained smart technologies. While challenges such as energy output efficiency and effective utilization remain, ongoing research and innovation continue to drive advancements in this field.

Figure 2

Self-powered sensors have demonstrated impressive capabilities comparable to commercial sensors, with key parameters including sensitivity, linearity range for pressure, strain, humidity, and temperature, as well as light or temperature responsivity, gas detection limits, and durability for continuous operation. Integrating energy harvesters (EHs) into commercial products such as gloves, shoes, and exoskeletons has gained significant attention, particularly for limbs with large-amplitude motions. A hybridized energy harvesting mechanism is often employed to bridge the gap between energy generation and consumption.
Self-powered sensors designed for monitoring eye movements, respiration, and facial expressions are compact and lightweight, prioritizing precise sensing through multiple sensors and advanced data analysis. Future research envisions self-powered body sensor networks, integrating EHs and sensors to enhance functionality.

Self-Sustained IoT Systems and Emerging Approaches
By combining wireless self-powered sensors with EHs, self-sustaining IoT systems have been successfully deployed in urban and natural environments. Recent advancements include a self-powered gas-sensing wristband, an armband for electrocardiography, and wearable textiles for rehabilitation. These self-sustaining IoT systems can be categorized into three distinct approaches:
1. Energy Storage-Based Systems
The most common approach involves EHs charging an energy storage unit (e.g., a supercapacitor), which then powers all functional components, including sensors, processors, and wireless communication modules. For example, a walking stick equipped with two rotational EH units can power an IoT system comprising a GPS module, environmental sensors, and a wireless transmission module. However, such always-on systems demand high power, making them less feasible for long-term use. Traditional sensors, such as accelerometers, consume between 100 µW to 1 mW, depleting a 50 mAh button cell battery within a few days.
2. Zero-Power Event-Based Systems
To significantly reduce power consumption, a second approach employs a zero-power event-based switch. The system remains in sleep mode, continuously harvesting and storing ambient energy, with functional units only activating upon detecting a predefined event. An example is an inertial switch in a MEMS-based vibration energy harvester (VEH). When acceleration exceeds a threshold (e.g., a hammer falling), the system wakes up and transmits critical sensing data. While effective in reducing power consumption, this approach still requires energy storage units and wireless modules, adding complexity and size to the system.

3. Direct Wireless Transmission Systems
The third approach eliminates the need for energy storage and wireless modules by directly transmitting voltage signals generated by self-powered sensors. Through inductive coupling, the sensing output is transmitted without requiring an EH or additional energy storage. For example, a textile-based triboelectric nanogenerator (TENG) array can send force, strain, and pressure data through coil induction with a maximum transmission range of 1 meter. While this system is highly compact and power-efficient, it has limitations in data volume and communication range.
Application-Specific Selection of IoT Approaches
• For applications requiring continuous, high-resolution sensing (e.g., building monitoring), Approach 1 is ideal, with EHs extending battery life and self-powered sensors reducing power consumption.
• For event-based monitoring (e.g., fall detection), Approach 2 is preferable, offering a near-zero-power system with an event-triggered switch.
• For compact wearable and implantable devices (e.g., heart rate or body temperature monitoring), Approach 3 is the best choice, as it relies solely on self-powered sensors and inductive coupling for data transmission.
Achieving self-sustainability in IoT will significantly enhance the feasibility of deploying wireless sensing nodes in various environments, paving the way for a wider range of IoT applications.

Figure 3. Three Approaches for Self-Sustainable IoT Systems
Smart Homes
To develop an integrated and energy-efficient smart home system, various energy sources from both indoor and outdoor environments can be utilized to power sensor nodes. Rooftops, with their large surface area and exposure to solar, wind, and rain, offer significant energy-harvesting potential.
Lawn-structured triboelectric nanogenerators (TENGs) have been introduced to harness sweeping wind energy on rooftops, successfully powering multiple LEDs. Another approach combines Si-based solar panels with a flutter-structured TENG positioned beneath them, enabling simultaneous solar and wind energy harvesting. This hybrid method has proven more effective in charging Li-ion batteries than standalone solutions.
For rain energy harvesting, Si-based solar cells integrated with mutual electrodes, such as PEDOT:PSS in a single-electrode-mode TENG, have been developed. Another design features a moth-eye-mimicking TENG on solar cells to minimize the impact of the triboelectric layer through improved specular transmittance. Additionally, a TENG/Si tandem hybrid solar cell has been demonstrated by layering an Ag/PDMS electrode over a Si-based solar cell. This design enhances both photovoltaic and triboelectric performance, achieving a higher power conversion efficiency compared to conventional solar cells. Collectively, solar, wind, and rain energy can be converted into electricity to power sensor nodes in smart homes.
A practical example of this concept is a wireless, self-powered integrated nanostructured gas sensor network (SINGOR) designed for smart homes. The SINGOR system consists of a power supply module, gas-sensing module, microcontroller-analog readout circuit, and wireless data transmission module, all optimized for self-sustaining operation. The power management system efficiently harvests indoor light energy, charging a lithium-ion battery when sufficient illumination is available and conserving power when light is scarce. A 4×4 sensor array with ultra-low power consumption is integrated with a readout circuit board, while a microcontroller and Bluetooth module manage data acquisition, analog-digital conversion, and wireless transmission. By implementing different duty cycles, including continuous sensing, sleep mode, and Bluetooth shutdown, power consumption is significantly reduced. In a demonstration, multiple SINGOR nodes were deployed in a kitchen to form a smart gas-sensing network, enabling real-time gas leakage detection and source localization via a mobile phone.
Moreover, mechanical energy from indoor activities can also be harnessed for home monitoring. Contact-separation mode TENGs, made from plastic and electronic waste, have been developed and strategically placed throughout a home to capture mechanical energy from daily human activities. These devices not only generate power but also facilitate interaction monitoring between humans and smart home systems.

Figure 4

Smart Robotic Systems with Self-Powered Sensors
• Robotic grippers with real-time sensing of shape, size, and temperature can recognize objects like coffee cups and canned drinks with different temperatures.
• Triboelectric-based self-powered sensors improve the functionality of robotic manipulators:
o 3D motion control sensors track robotic movements using silicone rubber and hydrogel-based electrodes.
o Delta-Parallel Human-Machine Interface (DT-HMI) enables 3D sensing and VR/AR manipulations, including alphabet writing and remote control of vehicles/submarines.
o Angle sensors with nanoradian precision improve the accuracy of robotic arms for tasks like calligraphy duplication.
o Stretchable strip sensors (TSS) enhance control in nanomanipulation (e.g., in scanning electron microscopes).
Smart Transportation Systems
• Road Monitoring:
o A triboelectric speed bump acts as a self-powered warning system that detects vehicle speed and activates LED signals near pedestrian crossings.
o Embedded TENG rebars in concrete beams allow for structural health monitoring and energy harvesting.
o A wireless traffic monitoring system based on TENG and magnetic resonance coupling can detect flow rates and vehicle speeds with 94% accuracy.
• Driver Safety Monitoring:
o A self-powered smart safety belt monitors a driver's turning direction and angle to prevent accidents.
o TENG-based sensors on accelerators and brakes analyze driver behavior.
o A self-powered steering-wheel angle sensor (SSAS) detects driver fatigue based on steering patterns and provides alerts.
• Railway Applications:
o A Helmholtz resonator with a PVDF film converts low-frequency railway noise into energy.
o A wind barrier with 66 TENG units along roads and railways generates power and functions as a self-powered wind speed sensor.
o A rotating wind energy harvester tracks high-speed trains while generating energy.
With the rapid advancement of energy harvesting and sensor technologies, various self-powered sensors and IoT applications with self-sustaining capabilities are being developed to support the growth of smart cities. These innovations span multiple domains, including smart homes, human monitoring, robotics, transportation, blue energy, aviation, and aerospace. From fundamental mechanisms to practical performance, these applications not only extract electrical power and data from their environments but also integrate seamlessly with conventional hardware such as power management integrated circuits, microprogrammed control units, and wireless transmitters.
One of the major challenges in developing self-powered or self-sustaining systems is the dynamic nature of environmental conditions. Mechanical, solar, and thermal energy sources are often unstable, leading to fluctuations in energy output. Additionally, factors such as contamination, structural damage, and excessive stretching or compression further impact the reliability of energy harvesting and sensing. To overcome these issues, the use of self-cleaning and self-healing materials, along with robust mechanical designs and protective packaging, is essential to ensure stable performance and accurate sensing.
Looking ahead, several emerging trends may shape future applications:
1. Smart Homes – Walls and roofs embedded with energy harvesters can generate power from rain, wind, sunlight, and vibrations, creating an integrated system for energy management and monitoring.
2. Wearable Health Monitoring – Lightweight body sensor networks, enhanced with AI algorithms, can enable self-sustaining systems for real-time motion tracking and health condition monitoring.
3. Smart Greenhouses – Large-scale energy harvesters can support sensors and controllers that monitor plant growth and wirelessly manage irrigation and lighting conditions.
4. Early Warning Systems – Sensors deployed in forests and mountainous regions can utilize wind and rain energy to extend their operational lifespan, providing critical alerts for natural disasters such as wildfires and landslides.
5. Space Exploration – Energy-harvesting units integrated into robotics and astronaut equipment can serve as both sensing devices and supplementary power sources, supporting continuous exploration and research beyond Earth.

DETAILED DESCRIPTION OF DIAGRAM
Figure 1: Evolution of energy harvesting technologies
Figure 2. Self-Powered Sensors and Energy Harvesters: (a) Wearable devices on clothing and exoskeletons; (b) Glove-integrated sensors; (c) Lower-limb energy harvesting structures; (d) Shoe and insole-based harvesters; (e) TENG-powered smart socks; (f) Hybrid self-powered watch; (g) TENG armband for human-machine interaction; (h) Self-powered air filter mask; (i) Eye-motion sensing glasses; (j) Sweat-based biochemical energy harvester; (k) Flexible perovskite solar cell; (l) Thermoelectric generator for body heat harvesting.
Figure 3. Three Approaches for Self-Sustainable IoT Systems
Figure 4. Smart Home Applications: (a) Roof-based solar and wind energy harvesting, (b) Wireless self-powered gas sensor network, (c) Self-powered access control using Gray code and hybrid energy harvesting, (d) Non-contact control interface with triboelectric-electromagnetic nanogenerator, (e) AI-Toilet for integrated health monitoring, (f) Smart mats for scalable floor monitoring with deep learning, (g) Contactless human tracking using triboelectric sensing. , Claims:1. AI-Driven Energy Harvesting System for Sustainable Powering of Wireless IoT Sensors claims that the system integrates AI algorithms to optimize energy harvesting efficiency based on real-time environmental conditions.
2. It employs multiple energy sources, such as solar, wind, and vibration, to ensure continuous power supply for IoT sensors.
3. AI-driven adaptive power management dynamically adjusts energy storage and distribution to prevent power shortages.
4. Self-sustaining wireless IoT sensors operate independently without the need for external power sources or frequent battery replacements.
5. The energy harvesting module is designed with high-efficiency materials to maximize energy conversion rates.
6. AI-based predictive analytics enhance power efficiency by forecasting energy availability and sensor workload.
7. The system includes smart energy storage solutions that intelligently manage excess power and optimize discharge rates.
8. Wireless communication protocols ensure seamless data transmission while minimizing energy consumption.
9. Real-time monitoring and diagnostics allow for proactive maintenance and fault detection to enhance system reliability.
10. The scalable and modular design enables easy deployment across diverse environments, from smart cities to industrial applications.