In modern embedded systems, real-time data processing is no longer optional—it’s essential. Whether monitoring sensor values in an industrial control unit, analyzing medical signals in a health device, or filtering inputs in automotive ECUs, embedded devices are constantly dealing with streams of data that must be interpreted on-the-fly.
However, raw sensor values are often noisy, transient, or unreliable in isolation. To make meaningful decisions or display clean data, embedded systems must preprocess and smooth this data stream, often with minimal memory and processing overhead.
This brings us to the heart of today’s topic—Stream Data Treatment in Embedded Systems using Moving Average Calculators, Windowed Average Filters (with outlier removal) and Min/Max Value Finders.
In this article, we’ll explore the importance of these algorithms, real-world use cases, implementation challenges, and how RAPIDSEA Suite provides ready-to-use, efficient, and scalable modules for these functions.
Why Stream Data Treatment Matters in Embedded Systems
Real-world signals from sensors such as temperature, pressure, current, vibration, ECG, etc., are often:
- Subject to electrical noise
- Affected by sudden spikes or drops
- Continuously fluctuating within a range
- If used directly in control logic, displays, or logs, this can lead to:
- False triggering of alarms
- Flickering UI values
- Inefficient or unsafe system behavior
Therefore, embedded systems need to smooth, summarize, and filter the incoming data in real-time, using algorithms that balance accuracy, latency, and computational efficiency.
Moving Average Calculator
What It Does
A Moving Average (also known as a Rolling Average) calculates the average of the last N data points in a stream. As new data arrives, the oldest value is discarded, and the average is updated.
Why Use It
It smooths out short-term fluctuations, providing a more stable signal that reflects trends without reacting too quickly to transient noise.
How It Works
Maintain a fixed-size circular buffer of the last N samples
Update running sum as new values arrive
Divide by N to compute the average
avg = sum(last N values) / N
Use Cases
- Temperature smoothing for HVAC systems
- Battery voltage monitoring
- Real-time signal processing for IoT sensors
Advantages
- Simple to implement
- Fast and low on memory
- Zero latency if using cumulative update
Windowed Average Calculator (with Outlier Removal)
What It Does
Unlike a simple moving average, a Windowed Average Filter calculates the average of the latest N data points after removing extreme values—typically the highest and lowest. This improves the robustness of the average by discarding occasional spikes or sensor glitches.
Why Use It
In noisy environments or with unreliable sensors, extreme values can distort the average. This technique maintains responsiveness while improving accuracy and stability.
How It Works
Maintain a buffer of last N values
Sort or scan to find min and max
Exclude them and average the rest
avg = sum(all - min - max) / (N - 2)
Use Cases
- Automotive systems (e.g., wheel speed sensors with occasional dropout)
- Biomedical signals (ECG/PPG with spikes)
- Vibration or motion detection in industrial settings
Advantages
- Handles noise better than raw moving average
- Prevents false alarms due to sudden spikes
- Balances noise rejection and responsiveness
Min/Max Finders
What It Does
This module keeps track of the minimum and/or maximum value observed over a defined period or sample window.
Why Use It
Minimum and maximum values offer important insights into system performance and stress—often more valuable than averages.
How It Works
Maintain current min and max variables
Update as new values arrive
Optionally reset after a time window or command
if (new_val < min) min = new_val;
if (new_val > max) max = new_val;
Use Cases
- Peak voltage or current monitoring
- Environmental data logging
- Thermal profiling and alerts
- Event-driven logic (e.g., open alert if pressure > threshold)
Advantages
- Extremely fast (constant time)
- Zero memory usage beyond two variables
- Works well in both real-time and logging systems
Challenges in Implementing Stream Data Algorithms Manually
While these algorithms seem straightforward, real-world implementation on embedded systems—especially those with tight memory and performance constraints—brings several challenges:
Circular Buffer Management - Maintaining a fixed-length history of values without dynamic memory is error-prone and requires careful pointer management.
Overflow Handling - Summing many values can lead to overflows, especially in fixed-point or integer-based systems.
Efficiency vs. Accuracy - Balancing responsiveness (reacting to real trends quickly) against stability (not responding to noise) often needs tuning.
Outlier Detection - Detecting and discarding extremes in the windowed average requires extra logic, sorting, or at least scanning—which adds overhead.
Portability and Reuse - Creating modular, reusable code for multiple channels or applications often requires careful abstraction and configuration.
Stream Data Treatment Made Easy with RAPIDSEA
To simplify these common requirements, we have integrated stream data treatment modules into the RAPIDSEA Suite—a production-grade embedded software platform.
These modules are designed with performance, portability, and simplicity in mind, and can be easily integrated into any embedded application.
Key Features of RAPIDSEA Stream Processing Modules are they have Efficient Memory Usage with Fixed-size buffers with circular queue support and Compile-time or runtime configurable sample count. They offer High Performance as they Use cumulative sum for moving average (O(1) update), Optional fixed-point support for platforms without FPU and Fast scanning for windowed average with minimal CPU load.
They are Modular & Scalable as users can Instantiate multiple calculators independently, Separate configurations per sensor or channel and Works with RTOS or bare-metal environments. RAPIDSEA provides clear ready-to-Use APIs and Interrupt-Safe Design that can be safely called from ISR context for real-time data capture. Finally with no Dynamic Memory, it is Designed for static allocation and deterministic behavior.
Conclusion
Stream data treatment is a foundational requirement in almost every embedded system today. Whether you're stabilizing sensor outputs, extracting peaks, or filtering out noise, techniques like moving average, windowed average, and min/max tracking are invaluable.
But instead of building these algorithms from scratch and risking inefficiencies or bugs, embedded developers can now leverage RAPIDSEA’s proven, production-ready modules—saving time, ensuring reliability, and boosting application performance.
Explore RAPIDSEA Suite Documentation.