Event-Driven Sensing Principles

Updated 24 November 2025

Event-driven sensing is a paradigm where sensor elements activate only upon detecting significant signal changes, reducing data redundancy and energy consumption.
By applying local thresholding and asynchronous reporting, these systems achieve microsecond-scale latency and over 120 dB dynamic range in challenging environments.
This approach enables efficient, low-power real-time processing in robotics, mobile vision, and wireless sensor networks, integrating seamlessly with neuromorphic computing.

Event-driven sensing is a sensing paradigm in which sensor elements asynchronously produce outputs only in response to significant changes in the measured signal, rather than through continuous or periodic sampling. This principle departs from conventional frame-based or uniformly sampled approaches by employing local thresholding and asynchronous reporting. Event-driven sensors, such as neuromorphic vision sensors, tactile event sensors, and low-power structural health monitors, achieve dramatic gains in temporal resolution, data sparsity, and energy efficiency by leveraging intrinsic signal redundancy and scene-driven adaptivity. Originating in bio-inspired engineering and neuromorphic computing, this approach has spawned a class of sensing and computing systems with microsecond-scale latency, dynamic range beyond 120 dB, and data-driven workflows that enable closed-loop control, sparse data transmission, and low-power edge processing across robotics, mobile sensing, communications, and large-scale wireless sensor networks (Gallego et al., 2019, Qin et al., 10 Feb 2025, Taunyazov et al., 2020, Lee et al., 2023, Sarwar et al., 2019, Muglikar et al., 2021, Wang et al., 29 Mar 2025, Chen et al., 2022, Tabrizchi et al., 2023, Funk et al., 2023).

1. Physical Principles and Sensing Mechanisms

Event-driven sensing is rooted in local, continuous evaluation of a signal against a defined contrast or change threshold, executed at the level of individual sensor elements (pixels, taxels, transducers). The canonical example is the Dynamic Vision Sensor (DVS), in which each pixel detects changes in the logarithm of incident intensity and triggers an event when that change exceeds a preset value:

$\Delta L(x,t) = L(x,t) - L(x,t-\Delta t)$

An event is emitted if $\lvert\Delta L(x,t)\rvert \geq C$ , where $L(x,t)$ is log-intensity and $C$ is the (programmable) contrast threshold (Gallego et al., 2019, Qin et al., 10 Feb 2025). Event polarity $p \in \{+1, -1\}$ encodes the direction of the change. The per-pixel implementation typically consists of a photodiode, logarithmic amplifier, differentiator, dual comparator (for positive and negative threshold crossings), local event latch, and asynchronous bus request logic.

Other modalities, such as tactile sensors and environmental transducers, apply the same operational logic to pressure, strain, or acceleration (Taunyazov et al., 2020, Sarwar et al., 2019). For example, the NeuTouch event tactile sensor triggers spikes when the change in sensed pressure $P_i(t) - P_i(t-\delta t)$ at taxel $i$ exceeds a threshold $\Delta P_{\mathrm{th}}$ .

2. Mathematical Models and Output Representations

The fundamental abstraction in event-driven sensing is the event tuple, typically represented as:

$e_k = (x_k, y_k, t_k, p_k)$

where $(x_k, y_k)$ specifies pixel or taxel address, $t_k$ the timestamp, and $p_k$ the polarity (Wang et al., 29 Mar 2025). Formally, the set of all events forms a sparse, asynchronous point process:

$E = \{\, e_k\ |\ t_0 \leq t_k \leq t_1\,\}$

Higher-level abstractions include:

Event frames: $F(x,y) = \sum_{e_k:\, (x_k,y_k) = (x,y),\, t_k \in [t_0,t_1]} p_k$
Time surfaces: $S(p, t) = t - t_{\mathrm{last}}(p)$
Voxel grids: $V(i,j,n)$ counts events in spatial bins over temporal slices

For feature detection, tracking, and fusion, mathematical frameworks such as probabilistic models over events and spatio-temporal density filters operate directly on the event stream (Roheda et al., 2019, Wang et al., 29 Mar 2025).

3. Energy Efficiency, Latency, and Scalability

A core advantage of event-driven sensing is minimization of energy expenditure and data throughput. Only sensor elements detecting significant changes become active, resulting in extreme data sparsity under static scenes. Front-end designs exploit this by limiting analog-to-digital conversion, local state updates, and bus arbitration to active regions or trigger events (Tabrizchi et al., 2023, Sarwar et al., 2019). Power consumption is adapted to scene/activity:

Vision (DVS front-end): Pixel-level latency $<10$ µs (Gallego et al., 2019), dynamic range $120$–$140$ dB (Qin et al., 10 Feb 2025, Gallego et al., 2019, Wang et al., 29 Mar 2025)
Tactile (NeuTouch): Constant 1 ms readout at up to 240 taxels due to asynchronous shared-channel encoding (Taunyazov et al., 2020)
Environmental sensors: Average current scaled as $I_\mathrm{avg} = I_\mathrm{sleep} + (I_\mathrm{active} - I_\mathrm{sleep})\lambda t_\mathrm{active}$ , where $\lambda$ is the event rate (Sarwar et al., 2019)

State-of-the-art wireless event sensor networks use code-division multiple access (CDMA) and asynchronous RF to support thousands of nodes, with per-chip energy $<30$ µW and low error rates even under aggressive spectral multiplexing (Lee et al., 2023).

4. System Architectures and Signal Processing Pipelines

Event-driven architectures span from pixel-to-backend stack:

Local sensing: Photodiode/amplifier, threshold detector, asynchronous event register (Qin et al., 10 Feb 2025, Wang et al., 29 Mar 2025)
In-pixel processing: On-chip accumulation of event frames or time surfaces, self-timed delta encoders, spatial/temporal filtering (Qin et al., 10 Feb 2025, Tabrizchi et al., 2023)
Data transmission: Address-Event Representation (AER) protocol or asynchronous wireless CDMA (Qin et al., 10 Feb 2025, Lee et al., 2023, Chen et al., 2022)
Back-end processing: Spiking neural networks (SNNs) tailored for sparse inputs, supporting microsecond reaction times and $<$ 1 mW compute power (Taunyazov et al., 2020, Wang et al., 29 Mar 2025, Chen et al., 2022)

Advanced sensors use wafer stacking, backside illumination (BSI), and on-chip AER logic for high-sensitivity, high-resolution event reporting (pixel pitches $<3\,\mu$ m, quantum efficiency $>90\%$ ) (Qin et al., 10 Feb 2025). Neuromorphic and FPGA accelerators exploit event sparsity with data-driven computation and memory access (Wang et al., 29 Mar 2025).

5. Applications Across Modalities

Event-driven sensing has enabled new forms of real-time, low-power, and context-adaptive operation in multiple domains:

Robotic perception: Low-latency visual-tactile fusion for object recognition and slip detection (Taunyazov et al., 2020, Funk et al., 2023)
Mobile vision/odometry: High-frequency feature extraction and object tracking under high dynamics and HDR conditions (Wang et al., 29 Mar 2025, Qin et al., 10 Feb 2025)
Adaptive depth sensing: Event-based cameras direct active illumination to regions-of-interest, yielding up to 90% reduction in power for structured light, LiDAR, and ToF scans (Muglikar et al., 2021)
Wireless sensor networks: Duty-cycled structural health monitoring nodes, with multimetric triggers and threshold-based wakeup for strain, vibration, and time (Sarwar et al., 2019)
Networked sensor populations: Asynchronous CDMA uplinks for spike streams support neuromorphic population coding and inference at high device counts (Lee et al., 2023)
Optical tactile sensing: Event cameras embedded in elastomeric skins afford high-temporal-resolution marker tracking for force/torque and slip control at 1 kHz (Funk et al., 2023)
Resource-constrained edge sensing: Near-sensor, event-driven background subtraction with NVM for power interruption resilience (Tabrizchi et al., 2023)

6. Advantages, Limitations, and Trade-offs

Advantages:

Microsecond-scale latency and high temporal resolution, enabling feedback control and real-time robotics (Gallego et al., 2019, Wang et al., 29 Mar 2025, Funk et al., 2023)
Extreme dynamic range ( $>$ 120 dB), noise robustness, and absence of motion blur due to thresholded reporting (Qin et al., 10 Feb 2025, Gallego et al., 2019, Muglikar et al., 2021)
Data and energy scaling with scene dynamics, rather than sensor count or area (Sarwar et al., 2019, Lee et al., 2023, Qin et al., 10 Feb 2025)
Direct compatibility with neuromorphic computing and SNNs, supporting event-based learning and inference (Taunyazov et al., 2020, Wang et al., 29 Mar 2025, Chen et al., 2022)

Limitations:

No absolute intensity or state in static scenes—zero output when the environment is unchanging (Wang et al., 29 Mar 2025, Qin et al., 10 Feb 2025). This requires hybridization with frame measurements or artificial motion in some tasks.
Elevated sensitivity to background noise, hot pixels, and parameter mismatch; requiring advanced spatio-temporal filtering (Wang et al., 29 Mar 2025, Qin et al., 10 Feb 2025)
Algorithmic incompatibility with classic frame-based vision pipelines; necessitates novel continuous-time, spike-based algorithms (Gallego et al., 2019, Wang et al., 29 Mar 2025)

7. Future Directions and Research Challenges

Research in event-driven sensing is focused on:

Scaling sensor density, throughput, and integration using BSI, 3D stacking, and on-chip SNNs (Qin et al., 10 Feb 2025, Wang et al., 29 Mar 2025)
Developing standardized benchmarks and representations for cross-comparison of event data and algorithms (Gallego et al., 2019, Wang et al., 29 Mar 2025)
Bio-inspired event abstraction, such as adaptive thresholding and multi-level event coding (Wang et al., 29 Mar 2025)
Extending event principles to multi-modal fusion (e.g., visual, tactile, audio) and robust operation under sensor damage or link loss (Taunyazov et al., 2020, Roheda et al., 2019)
Energy harvesting and ultra-low-power idle-mode optimization for perpetual operation in edge and wireless sensor networks (Sarwar et al., 2019, Tabrizchi et al., 2023)
Neuromorphic wireless semantic communications where events drive the entire sensor-to-inference pipeline, closing the loop from efficient front-end sensing to channel-adaptive SNN decoding (Chen et al., 2022)

The paradigm of event-driven sensing continues to redefine sensing-system design and implementation, offering substantial improvements in efficiency and performance for next-generation intelligent and autonomous systems.