The proposed PIR-MPPT model directly predicts the maximum power point voltage from irradiance and temperature conditions, reducing the computational complexity associated with conventional iterative MPPT techniques.
Abstract
The increasing deployment of self-powered Internet of Things (IoT) devices has created a demand for efficient photovoltaic (PV) energy harvesting and power management systems. This paper presents a Physics-Informed Regression-Based Maximum Power Point Tracking (PIR-MPPT) approach integrated with a low-power power management unit for photovoltaic energy harvesting applications. The proposed PIR-MPPT model directly predicts the maximum power point voltage from irradiance and temperature conditions, reducing the computational complexity associated with conventional iterative MPPT techniques. The complete energy harvesting system consists of a voltage-controlled oscillator (VCO), non-overlapping clock generator, ramp charge pump, Banba bandgap reference (BGR), and programmable low-dropout regulator (LDO). The system is modeled using Verilog-A and Cadence Virtuoso and implemented using 45-nm GPDK technology. Simulation results demonstrate accurate maximum power point prediction with an $R^{2}$ value of 0.9082, RMSE of 20.56 mV, and MAE of 9.71 mV. The proposed ramp charge pump boosts the PV input voltage from 1.0–1.35 V to an output voltage range of 3.3–3.5 V with high conversion efficiency. The programmable LDO subsequently generates regulated output voltages of 1.2 V, 1.8 V, and 2.5 V to support diverse low-power IoT loads. Furthermore, the Banba BGR provides a stable 0.8 V reference voltage with excellent temperature stability, while the integrated system achieves reliable end-to-end operation, low output ripple, and efficient power conversion. These results validate the proposed architecture as a promising solution for self-powered IoT sensor nodes and low-power embedded systems.
The proposed intelligent energy harvesting framework provides an efficient and sustainable power solution for next-generation Internet of Things (IoT) devices by integrating multi-source ambient energy harvesting, Maximum Power Point Tracking (MPPT), hybrid energy storage, and machine learning-based energy management. The framework effectively harvests energy from solar, thermal, radio frequency (RF), vibration, and wind sources while optimizing power utilization through adaptive energy prediction and intelligent task scheduling. Experimental evaluation demonstrates that the proposed system achieves higher energy utilization, lower power consumption, improved communication reliability, and extended operational lifetime compared with conventional battery-powered IoT systems. Furthermore, the integration of cloud and edge computing enables real-time monitoring, predictive analytics, and scalable deployment across diverse IoT applications. Overall, the proposed framework offers a reliable, cost-effective, and environmentally sustainable solution for smart cities, healthcare, industrial automation, environmental monitoring, and precision agriculture, while providing a strong foundation for future research on AI-driven energy optimization and next-generation wireless-enabled self-powered IoT networks.
B. Vaishnavi, K. Siddhartha, B. Ramprasad· International Journal of Cre...· 0 citations
Autonomous marine Internet-of-Things (IoT) platforms must preserve sensing and telemetry functions under a limited and weather-dependent energy supply. This paper develops an energy-aware power-management assessment for a multi-parameter smart buoy using only the implemented design parameters and field records of the prototype. The platform combines a 20-Wp photovoltaic array, an MPPT charge controller, a 12-V 12-Ah VRLA battery, an LM2596 conversion stage, a multi-sensor acquisition node, GNSS, microSD storage, and Iridium Short Burst Data communication. A datasheet- and design-based operating-state model estimated 46.62 Wh/day at the 5-V load side for a nominal 30-min cycle and 52.98 Wh/day from the battery when a conservative 88% DC–DC efficiency is included. With 98% MPPT efficiency, analytical solar input for 5, 4, and 2 peak-sun-hours/day was 98, 78, and 39 Wh/day, corresponding to energy balances of +45, +25, and −14 Wh/day. The 72-Wh usable battery window yields a theoretical solar-free autonomy of approximately 1.36 days. A voltage-threshold policy maps the system into normal, power-saving, emergency, and critical-shutdown states. During a 2-h-18-min coastal observation phase, 47 battery records remained between 12.07 and 12.19 V (mean 12.13 V), with a net start-to-end change of −0.07 V and no power-induced interruption. The field evidence demonstrates short-duration supply continuity, while the daily energy-neutrality result remains an analytical design estimate that requires multi-day current, irradiance, and charging measurements for validation.
Dimas Aditya Wiranata, Anton Widodo· Internet of Things and Artif...· 0 citations
This work presents a multi-mode energy harvesting-assisted edge computing architecture, integrated with a joint optimization of energy consumption and communication behaviour, aimed at enhancing the sustainability, reliability and autonomy of operation in an industrial IoT context.
Dr. Deepa, M. Mehfooza, Padmavathy Thiruppathi Raj· Microsystem Technologies· 0 citations
The increasing demand for sustainable energy solutions is growing day by day, so this project focuses on using heat loss from electronic and automotive systems to produce electrical energy. The suggested system works by harnessing the Seebeck effect to harvest heat energy and transform it into electricity through the use of thermoelectric generators (TEG), wherein the difference in temperature of the TEG produces the output voltage. A cooling system is required to maintain the temperature gradient for generating voltage and improving efficiency. A boost converter is deployed to step up lower output voltage levels to usable levels, which supports low-power devices. To monitor how much output energy is being generated, an INA219 sensor is used to measure voltage and current, which is interfaced with an ESP8266 module, and the readings are shown live on an LCD display. An ESP8266 module is included in the system, which enables wireless control of electrical loads through a mobile application called SinricPro. The setup can be understood in three sections: one-part deals with generating energy, the second part deals with monitoring, and the other handles load controlling using IoT. A relay is used to switch on and off devices based on the signals received from the app. Meanwhile, the system keeps tracking the output values continuously. The system offers the feasibility of combining waste heat recovery with IoT technology, contributing to sustainable energy management despite challenges like low conversion efficiency and dependence on temperature gradient.
Sathish Devireddy, Vuppala Krithisha, Riya Kumari et al.· International Conference on...· 0 citations
The growing deployment of Internet of Things (IoT) devices has intensified the demand for energy-efficient communication systems that ensure reliable operation under stringent power and maintenance constraints. Traditional transceiver designs often struggle to achieve long-term energy sustainability, resulting in frequent battery replacements, high operational costs, and service disruptions, particularly in remote or difficult-toaccess IoT deployments. This paper presents a novel energyefficient transceiver architecture tailored for IoT applications, integrating hybrid modulation with intelligent power amplifier (PA) control to reduce energy consumption. By selectively deactivating the PA for statistically dominant “typical” symbols, the proposed system achieves up to 30% energy savings without sacrificing communication quality. To support efficient signal decoding, two receiver architectures are introduced: (i) an energy detectionbased approach with a closed-form Bit Error Rate (BER) model, and (ii) a deep learning-based approach leveraging denoising and classification for enhanced robustness. Simulation results confirm that the proposed design maintains BER performance comparable to conventional schemes while preserving spectral efficiency and significantly extending battery life. This solution offers a scalable, low-power communication method suitable for IoT deployments that require reliable and long-duration operation.
Ahmed Badawy, Muhammad Jamal Shehab, Amr Mohamed· International Conference on...· 0 citations
Residential energy conservation is frequently hindered by a lack of real-time visibility into usage, widening the "behavior-action gap" between daily electricity utilization and delayed monthly utility statements. This paper presents the development of a low-cost, open-source, residential solar-powered Internet of Things (IoT) monitoring system. The proposed architecture segments data collection tasks into a Direct Current (DC) renewable generation node and an Alternating Current (AC) appliance-level consumption interface using dual ESP32 microcontrollers. The system collects data from precision inline INA226 shunt sensors on the solar and battery storage paths, alongside a ZMPT101B transformer and an SCT-013 current transformer wrapped in a physical 5-turn wire loop to lift low-power current boundaries above standard resolution floors. Telemetry is streamed asynchronously via Message Queuing Telemetry Transport (MQTT) over a cloud HiveMQ broker. To optimize edge node efficiency, chronological timestamping is completely decoupled from the hardware layer and managed server-side upon database ingestion into InfluxDB. Real-time and historical analytics are visualised using a centralized Grafana web dashboard interface. Experimental results show that the hardware sensing layer maintained high measurement precision, yielding low absolute errors (Verror ≤ 0.38% and Ierror ≤ 32%) and successfully resolving low-power household standby states down to single-digit wattages (e.g., 9W). The event-driven alert system achieved zero-latency multi-tier warnings, verifying that the proposed framework delivers an accessible, high-fidelity sub-metering platform for urban electrification programs.
A. Ja'afar, Aiman Haqeem Ahmad Tarmizi, H. Yusof et al.· International journal of res...· 0 citations
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