Sensor Fusion and Its Role In Rugged Edge AI

What is sensor fusion?

When multiple sensor devices are all pooled together to produce data information, this is named as the concept of sensor fusion. Sensor fusion, or sensor data fusion, is a technique that combines data from multiple sensors to create a more accurate and reliable model. By using software algorithms, deployments can utilize the strengths of many types of sensors.  

Different Types of Sensors & Data 

Sensors range from all types of IoT devices. These include video cameras, LiDAR (Light Detection and Ranging), Radar, GPS, or any other types of devices that output signals to detect events and changes in its environment. These sensors all collect data and send the respective information to other electronics, commonly a computer, to process information. Sensor fusion creates all types of data and the type of data that is fed and processed defines what sensor fusion is.  

Why is it important? 

Sensor fusion’s functionality and significance can be thought of as a human body’s five senses. Each of the five senses allow a person to make sense of the environment around them and accurately process the information that surrounds them. Each sense is unique as they observe and collect information in a different way. By combining our different sensory functions (sight, sound, taste, smell, and touch) our body can form a more complete picture of information to make a decision based on the received data. All senses work together to send information to our brain to then make an informed decision. For example, if we were in a room with a gas leak, although we are unable to see the gas, the smell coming from the origin of the leak would alert our brain that we will have to quickly exit the room.  

Each sensory input has its strengths that work together to build a more complete picture and helps compensate for individual weaknesses. Our brain is the central processing unit that pieces the pieces of the puzzle together. The same concept applies when we attribute various types of individual sensors joining together to help make informed decisions and ensure safety and reliability in industrial applications.  

The core concept leverages the strengths of each type of sensor while compensating for their weaknesses. For instance, a camera can provide high-resolution images but struggle in low-light conditions, while a LiDAR camera can accurately measure distances, but struggle with identifying color or texture. By fusing data from these sensors, a more holistic and accurate representation can be obtained. 

Key Principles of Sensor Fusion  

Without diving into a rabbit hole of technicalities, sensor fusion follows strict methods to properly combine data from multiple sensors efficiently and effectively. Different methods vary in degree of complexity, compute requirements and level of accuracy that they are able to provide. The intricate nature of sensor fusion is what allows for a robust way to paint a complete understanding of surroundings and situations at hand. In this blog, we will cover a range of different ways sensor fusion is classified and categorized for data processing.  

Types of Sensor Fusion Configuration and Classifications 

Sensors all reside in various locations and placements around a certain application. These devices operate differently depending on the application as well. However, they perform the necessary function to collect data in some way. In order to understand sensor fusion more, we must investigate how people integrate sensor fusion and pick how they collect the necessary data for their application.  

Centralized, Decentralized, and Distributed Fusion 

In sensor fusion, centralized, decentralized, and distributed is a common classification of that many integrators may choose to follow when they want to answer the question: “WHERE should sensor fusion take place?” This can range between having a main computer, independent sensors, or a mix of both to process collected data. 

  • Centralized – All data is fed to a central unit that processes data.  
  • Decentralized – Each sensors processes (fuses) data then forwards it to the next one. 
  • Distributed – Sensor nodes communicate at set intervals, processing data locally then sending it to the next unit.  

Below is a simple graphic of where each sensor fusion takes place to help better visualize the location of sensor fusion. 

(Image Credit 

Data Level Sensor Fusion  

Another way to categorize sensor fusion processing is by determining the level of data variance. The level of sensor fusion usually will ask, “WHEN should data be fused?”  

Low-Level (Data-level): At low level, sensor fusion aims to fuse all raw data produced by the sensors that is then processed. This introduces the most basic form of data collection, ensuring every piece of information is captured then processed. The one disadvantage is that this requires processing a large amount of data.  

Mid-level (feature-level): At a mid-level, data is fused when it has been first interpreted by a processor or sensor, rather than immediately from raw data. This fusion level involves extracting relevant features from each sensor's data first, then combined to create a unified picture.  

High-level (Decision-level): Decision-level fusion involves combining decisions or results made independently by individual sensors or processing units. Instead of directly merging raw data or features, decision-level fusion focuses on the final outcomes of each sensor's processing and decision-making process. 

Sensor Fusion Competition Level 

The final classification of sensor level deals with WHAT the fusion should do. This usually represents a preferred outcome and coordination flow between sensors.  

Complementary: This type of sensor fusion consists of independent sensors that aren’t dependent on one another, but when their output is combined, it creates a more complete image. For instance, several cameras placed around a room and focused on different parts of the room can collectively provide a picture of what the room looks like. The advantage of this type is that it typically offers the greatest level of accuracy.

Competitive/Redundant: When sensor fusion is set up in a competitive or redundant arrangement, sensors provide independent measurements of the same target. By doing so, each output’s combined data provides a high level of completeness.

Cooperative: Cooperative fusion involves independent sensors providing data that when taken together, delivers information that wouldn’t be available from a single sensor. Each sensor provides a different viewpoint that is used to generate overall information. This type of fusion is the most complex, as it is sensitive to possible inaccuracies and error. However, cooperative fusion can provide unique perspectives not found in other techniques.

(Image credit: sciencedirect) 

Sensor Fusion Algorithms 

For sensor fusion to take place, data scientists use algorithms to properly manage and mathematically merge the data to be processed. Sensor fusion algorithms play an important role that determine how the data is processed together. The choice of algorithm depends on the specific application, the nature of the sensors, and the desired level of accuracy and robustness. Some algorithms are more suitable for linear systems, while others excel in handling non-linear or uncertain environments. 

Below are just a few of the most commonly used types of algorithms that are used in sensor fusion.  

  • Kalman Filter 
  • Bayesian Network 
  • Central Limit Theorem 
  • Neural Network

Fusion algorithms are complex and require a great deal of mathematical concepts behind them. To learn more about the intricate nature of fusion algorithms, we recommend this scientific journal covering the algorithms in more detail.

Challenges/Limitations of Sensor Fusion 

While sensor fusion offers numerous benefits, it also comes with its own set of challenges. Synchronizing data from different sensors, dealing with sensor inaccuracies, and managing computational complexity are some of the hurdles that researchers and engineers need to overcome.  

Sensor fusion is complex as a variety of different sensor technologies come together. The primary challenge that is tied to sensor fusion is the complexity of data that multiple sensors produce. This results in increased need for processing and storage, latency, and performance, which can increase overall costs and complexity. 

Another challenge that sensor fusion brings with its complex nature is the data noise and relevance of data. Different sensors contribute data differently and in various methods, which create difficult requirements to produce proper and accurate results. Sensors can also introduce noise, which can affect the overall integrity of the fused data. 

Rugged Edge Computing: Empowering Sensor Fusion in Challenging Environments 

Rugged edge computing plays a crucial role in enabling and enhancing sensor fusion, especially in environments where traditional computing infrastructures are not practical. Together with IoT sensors, they form a relationship that brings processing power directly to the sensor source.  

Understanding Rugged Edge Computing 

Rugged edge computing is the deployment of computing resources to the edge, closer to where the point of data generation is. Rugged edge computing brings real-time processing into deployments where low-latency, robustness, and mission-critical decision making is essential to operations. These specialized pieces of hardware are purpose-built to withstand non-traditional environments where harsh conditions exist. Dust, debris, water, wide voltage, shock, and vibration are all factors that dramatically affect the performance of a computer.  

Combining data from multiple sensors requires significant resources to collect, analyze, and process data in real-time. Rugged edge industrial computers provide the necessary platform that allows IoT sensors to not only seamlessly integrate, but also the processing power to enable seamless communication with sensor fusion techniques to form the necessary conclusion. This is especially true when it comes to a centralized sensor fusion technique, where all data is forwarded to a singular compute unit.  

By processing the data right at the point of generation, rugged edge computing helps mitigate the challenges posed by latency, bandwidth, and security vulnerabilities associated with transmitting data to cloud centers. By effectively eliminating the challenges associated, enterprises can achieve more efficient productivity and automation across their operations.  

Benefits of Rugged Edge Computing: 

  • Reduced Latency 
  • Data Privacy/Security 
  • Bandwidth optimization 
  • Reliability in Harsh environments 
  • Redundancy and Robustness 
  • Scalability  
  • Cost Efficiency 

Rugged edge computing forms a powerful combination with sensor fusion techniques that helps address the challenges with real-time processing. As sensory data continues to increase every year, rugged edge computing helps empower industries to harness the full potential of sensor fusion, driving innovation in the ever-changing landscape of technology.  

Rugged Edge AI   

One of the biggest trends that we constantly cover is Rugged Edge AI. It has been recognized as a leading trend for the productivity revolution in Industry 4.0 by leading research firm Gartner. It is no exception that sensor fusion can play an integral role in driving intelligent decision–making at the source of data. Deploying AI algorithms to edge devices enables rapid analysis and response to events as they occur. Sensor data, ranging from images, video, and audio becomes a valuable input for Edge AI systems. These systems employ machine learning models to extract meaningful insights, detect patterns, and make informed decisions instantly. From identifying anomalies in industrial machinery to recognizing objects in surveillance videos, Edge AI transforms raw sensor data into actionable intelligence at the edge, opening avenues for enhanced efficiency, security, and automation in real-world applications. 

Applications of Sensor Fusion 

Sensor fusion finds use in a wide range of industrial applications, each benefiting from its ability to provide more accurate and reliable insights. Some notable applications include: 

  • Industrial Automation 
  • Robotics  
  • Smart Cities  
  • Intelligent Transportation & Railway 
  • Autonomous Vehicles 

Sensor fusion is a powerful technique that enables us to make sense of the world by combining data from various sensors. Its applications span across industries and play a pivotal role in shaping the future of technology. As we continue to innovate, sensor fusion will undoubtedly remain a cornerstone of enhanced perception and decision-making in our increasingly sensor-rich world.