---
title: Simultaneous Energy Harvesting & Sensing
url: https://www.emergentmind.com/topics/simultaneous-energy-harvesting-and-sensing-sehs
type: topic
---

# Simultaneous Energy Harvesting & Sensing

Simultaneous Energy Harvesting and Sensing (SEHS) refers to systems wherein a single physical transducer both scavenges ambient energy to supply a local energy store and concurrently provides a voltage/current waveform encoding information about environmental or user context. In such systems, the dual-use of the harvesting element as a sensor yields “energy-positive” operation: the system harvests more energy than is consumed during signal acquisition, enabling battery-free or self-sustaining deployment. SEHS frameworks span radio-frequency, kinetic, piezoelectric, thermal, triboelectric, solar, and engineered metastructural systems, with representative applications in wireless IoT, wearable health, infrastructure monitoring, and machine condition sensing.

## 1. Physical Principles and Taxonomy of SEHS

SEHS systems exploit intrinsic coupling between an energy-harvesting mechanism and a measurable signal correlated to the sensed context. The harvested energy $H(t)$ and acquired signal are both functions of the underlying environmental excitation (e.g., RF power, mechanical strain, vibration, temperature gradient). Key categories by source and signal type include:

- **Energy Source**:  
  - RF (Radio-Frequency Energy Harvesting, RFEH): voltage waveform encodes amplitude/diffraction changes due to motion in the RF field [2409.00086].
  - Kinetic (Kinetic Energy Harvesting, KEH): piezoelectric or electromagnetic harvesters convert motion/vibration to AC voltage/current profiles [2009.02752, 2002.05109, 2507.05081].
  - Thermal (Thermoelectric, TEH): DC voltage derived from thermal gradients, with temperature or context modulating signal amplitude [2004.05728].
  - Solar (Photovoltaic, SEH): output modulations encode illumination, often for coarse environmental or presence sensing [2004.05728].
- **Signal Points**:
  - Open-circuit voltage $V_{oc}$, rectified voltage $V_{rec}$, storage node voltage $V_{cap}$, and harvesting current $I_{har}$ each expose different context sensitivities and distortion properties [2002.05109].

By exploiting the transducer’s contextual sensitivity (e.g., diffraction-induced voltage fluctuations in RF, or vibration spectral content in piezo systems), SEHS systems enable single-hardware platforms to simultaneously provide self-powering and context-aware sensing functionalities [2002.05109, 2409.00086].

## 2. Canonical Architectures and Signal Flow

A typical SEHS node consists of: (i) harvesting transducer; (ii) impedance-matching, rectification, and energy storage circuitry; (iii) low-power microcontroller (MCU) with ADC for waveform acquisition; (iv) data logger or wireless transceiver for communication; (v) optional power management units (PMU). Circuit variants are source-dependent:

- **RF-based SEHS** (“REHSense”):  
  - Wi-Fi access point emits 2.4 GHz OFDM signals; a dipole antenna receives incident RF; matching network minimizes reflection; multi-stage Schottky rectifier produces $V_{DC}(t)$; MCU samples $V_{DC}$ at 12-bit, 200 Hz [2409.00086].
  - Sensing pipeline applies low-pass filtering, segmentation by activity (variance thresholding), normalization, and either variance-peak analysis for respiration or 1D CNNs for activity/gesture recognition.
- **KEH/Piezoelectric-based SEHS**:  
  - A piezoelectric cantilever is stressed via mechanical loading, routed through a bridge rectifier into a storage capacitor; MCU samples either AC terminals, rectified voltage, or harvesting current (via shunt and op-amp) [2009.02752, 2002.05109].
  - Sensing can exploit voltage (with compensation for storage-induced distortion), or current signals cleanly decoupled from storage via converter-based topologies.
- **Modular Vibration SEHS** (ViPSN 2.0):  
  - Hot-swappable ETUs for PZT, EMG, or TENG harvesters; configurable PMU provides energy flags at storage capacitor thresholds to coordinate task scheduling and checkpointing; peripherals are attached via standardized interfaces [2507.05081].

## 3. Mathematical Modeling and Performance Metrics

### Energy-positive Sensing Formalism

The energy-positivity of SEHS is quantified by the Acquisition Power Ratio (APR):
$$
APR = \frac{P_{har}}{P_{acq}}
$$
where $P_{har}$ is the average harvested power, and $P_{acq}$ the power consumed by data acquisition (ADC + low-power processing). APR $>1$ defines the “energy-positive” regime, allowing surplus energy for sensing, computation, or communication [2004.05728, 2002.05109].

### Source-Specific Dynamics

- **RF SEHS** ([2409.00086]):
  - Received power via Friis:
    $$
    P_{in} = P_t G_t G_r \frac{\lambda^2}{(4\pi d)^2}
    $$
  - Rectifier efficiency and harvested DC:
    $$
    \eta(P_{in}) = \frac{P_{DC}}{P_{in}},\quad
    V_{DC} = \sqrt{P_{harv} \cdot R_{load}}
    $$
- **Piezoelectric/KEH SEHS** ([2009.02752], [2205.06974], [2511.13291]):
  - Charged capacitor:
    $$
    E_{cap}(t) = \frac{1}{2} C [V_{cap}(t)]^2
    $$
  - Electromechanical plate dynamics model via modal coordinates, with coupled voltage and modal equations (see full state-space formulations in [2205.06974], [2511.13291]).
- **Duty-cycle and storage sizing** ([2507.05081]):
  $$
  E_{[V_a \to V_b]} = \frac{1}{2} C_{storage} (V_a^2 - V_b^2)
  $$
  $$
  C_{storage} \geq \frac{2(E_{static} + E_{task})}{\eta(V_{Pstart}^2 - V_{Pclose}^2)}
  $$
  
### Quantitative Energy and Sensing Results

| Platform         | Harvested power   | Sensing energy draw | APR (energy-positive?) | Classifier accuracy     |
|------------------|------------------|---------------------|-----------------------|------------------------|
| REHSense RF [2409.00086] | 4.5 mW (d=1m)      | 11.3–12.6 mW         | APR ≈ 0.36           | ≈95% (HAR), 4.6% RMS resp. err |
| KEH (converter) [2002.05109] | 13.2 μW (avg)    | 2.7 μW               | APR ≈ 4–10           | 97% (HAR, CB-i)        |
| Piezo insole [2009.02752]     | 164 μW (dual-PEH) | 18.1 μW               | APR ≈ 9.1            | 98.7–98.9% recall     |
| ViPSN2.0 Beac. [2507.05081]   | 125 μW           | –                    | –                    | 100% BLE shot          |
| SHM PEH [2511.13291]   | 4 μW (sensing) | 4 μW (PEH), 33 μW (accel.) | 8.25        | 93% (PEH-VAE) (+13% vs accel.)  |

In kinetic/piezo scenarios, converter-based current sensing achieves both high accuracy and strong energy-positivity [2002.05109]. For structural health monitoring via PEH, state-of-the-art models demonstrate a 98% reduction in sensing power and a 13% improvement in accuracy over accelerometer-based baselines [2511.13291].

## 4. Signal Processing and Machine-Learning Methodologies

SEHS implementations generally require customized pipelines to mitigate signal distortion induced by concurrent harvesting and to extract robust features:

- **RF SEHS**: Savitzky-Golay filtering, sliding-window segmentation, dynamic variance threshold for activity detection, with 1D-CNN for gesture/activity/respiration discrimination [2409.00086].
- **PEH/KEH SEHS**:  
  - Amplitude normalization filters compensate for capacitor charging effects:
    $$
    \tilde V_{A,B}(t) = 
      \begin{cases} 
        V_{A,B}(t)-V_C(t)+V^*  & V_{A,B}(t)\geq V_C(t) \\
        V^* \frac{V_{A,B}(t)}{V_C(t)} & V_{A,B}(t)<V_C(t)
      \end{cases}
    $$
    where $V_C$ is the capacitor voltage, $V^*$ a reference.
  - Feature extraction: time/frequency-domain descriptors (RMS, skewness, IQR, etc.), CWT for spectral images, or direct voltage time-frequency representations [2009.02752, 2511.13291].
  - Classifiers: BiLSTM and CNNs (activity/gait), AlexNet on CWT images (traffic speed, vibration classes), and unsupervised CVAE for damage detection [2009.02752, 2205.06974, 2511.13291].
- **Energy/Task Scheduling**: Schedulers adapt duty cycles, sampling rates, and task execution based on instantaneous energy, predicted harvesting profiles, and APR [2507.05081, 2004.05728].

## 5. Trade-Offs, Bi-Objective Design, and Optimization

Fundamental to SEHS system design is the energy-vs-sensing trade-off: maximizing harvested power and sensing accuracy are not always concordant objectives, as demonstrated in structural health monitoring and environmental contexts [2205.06974, 2511.13291]. Bi-objective frameworks employ multi-objective optimization with Pareto analysis, frequently using NSGA-II or surrogate metamodeling (kriging):

- **Design Variables**: harvester length, width, aspect ratio, tip mass, external load resistance, PMU thresholds [2205.06974, 2511.13291].
- **Pareto Fronts**: distinct device geometries or circuit topologies optimize one objective at the expense of the other; e.g., short beams yield higher $E/A$, longer ones richer spectra for sensing [2205.06974].
- **Context Sensitivity**: Road roughness, noise, damage location, and ambient excitation spectra alter the Pareto surface, necessitating adaptable or context-specific designs [2511.13291].

Bi-objective optimization thus directs practitioners to select device parameters and signal features that best match deployment requirements along the energy–sensing axis.

## 6. Scheduling, Adaptation, and Control Algorithms

SEHS systems, particularly for IoT and intermittent-compute nodes, require energy-aware scheduling and adaptation that jointly manage harvesting variability, transient storage, and deadline-constrained tasks [2507.05081, 2004.05728]:

- **Scheduling Approaches**:  
  - Dynamic Voltage and Frequency Scaling (DVFS), task decomposition/combination, duty-cycling, lazy scheduling, and online greedy heuristics balance energy arrival against computational demands [2004.05728].
- **Energy Indication and Checkpointing**:  
  - Use of hardware comparators, ADC-based polling, and explicit voltage flags to trigger transitions among cold-start, accumulation, task-execution, checkpoint, and shutdown [2507.05081].
- **Adaptation Strategies**:  
  - Dynamic sampling/braking, threshold retraining, and CNN model fine-tuning accommodate environmental drift, SNR drop, and context migration [2409.00086].
  - In battery-less intermittent setups, PMU-controlled checkpoint/restart cycles allow seamless operation under unpredictable harvest [2507.05081].
- **Open Problems**:  
  - Real-time prediction of harvest in KEH/TEH/RFEH, multi-source fusion scheduling, MPP tracking under dual-use constraints, cross-layer OS for energy-pool sensor integration [2004.05728].

## 7. Application Domains and Extensions

Representative SEHS deployments include:

- **RF-based Sensing**: Battery-free wireless respiration/activity/gesture recognition with up to 95% accuracy and a 98.7% reduction in front-end power versus conventional Wi-Fi receivers [2409.00086].
- **Wearable Gait and Motion Recognition**: SEHS-enabled insoles attain 98.9% recall and energy harvest of ~164 μW per step [2009.02752].
- **Infrastructure Monitoring**: Combined energy and unsupervised damage sensing in bridges achieves PEH-based CVAE accuracy up to 13% > accelerometer baselines, with 98% power savings [2511.13291].
- **Distributed IoT**: Modular platforms (ViPSN 2.0) demonstrate light/heavy/streaming task regimes, dynamic hardware swaps, and checkpointed battery-free imaging under marine/wind/fingertip energy profiles [2507.05081].
- **Engineered Metastructures**: Graded-resonant metasurfaces achieve broadband “rainbow trapping” for local displacement amplification, enhanced piezoelectric voltages, and both distributed sensing and energy harvesting [1907.09297].

SEHS principles and platform abstractions generalize across vibration, RF, solar, and thermal harvesters, enabling broad application in autonomous, energy-perpetual sensor networks.

---

In summary, the advancement of SEHS involves the convergence of physical modeling, circuit optimization, signal-processing pipelines, energy/task co-scheduling, and application-level adaptation. Progress in SEHS underpins sustainable IoT, enhances battery-free structural health monitoring, facilitates low-cost, maintenance-free wearable context sensing, and lays the groundwork for new metastructural energy/sensing materials [2409.00086, 2009.02752, 2002.05109, 2205.06974, 2511.13291, 2507.05081, 1907.09297, 2004.05728].

Source: https://www.emergentmind.com/topics/simultaneous-energy-harvesting-and-sensing-sehs