Data Fusion: Advantages and Disadvantages

This page explores the advantages and disadvantages of Data Fusion. It highlights the benefits and drawbacks associated with this technology.

What is Data Fusion?

Introduction: Data fusion is the process of integrating multiple data sources, typically from sensors, to produce more consistent, accurate, and useful information. By combining data from multiple sources, we can achieve better results compared to relying on individual data points alone.

Data fusion can be categorized into low, medium, and high levels depending on the processing stages involved. It essentially combines various types and forms of data to enhance data quality and improve decision-making.

In simpler terms, data fusion can be defined as the “theory, tools, and techniques used for combining multiple sensory data into a common representational format.” The rise of the Internet of Things (IoT) has led to the generation of vast amounts of data (big data), making data fusion increasingly necessary.

Figure 1: Single hop sensor data fusion system

In this system, each sensor transmits data directly to the data fusion center.

Figure 2: Multi hop sensor data fusion system

The multi-hop sensor data fusion system combines information from multiple sensor sources. Here, data from multiple sensors are collected by a hub sensor or aggregator, which is then connected to the data fusion system either directly or via the internet cloud.

Data fusion mathematical models are probability-based, AI (Artificial Intelligence)-based, or evidence theory-based. There are various data fusion stages, including:

• Decision Level: Fusion of decisions.
• Pixel Level: Fusion of information at the imaging device level.
• Feature Level: Fusion of information prior to decision-making.
• Signal Level: Fusion of information at the sensor node or within a local network.

Benefits or Advantages of Data Fusion

The following are the benefits or advantages of Data Fusion:

• Data fusion using multiple sensors makes information more decisive, intelligent, sensible, and precise compared to relying on a single sensor.
• Data fusion facilitates the statistical analysis of “N” independent observations, providing a more robust and reliable analysis.
• Since data fusion helps create highly accurate information, it allows the use of low-power sensors with relatively low accuracy in IoT networks, instead of high-accuracy, power-hungry sensors. This can lead to significant energy savings and cost reduction.
• Data fusion helps in handling the challenges associated with the large volumes of data generated by IoT-based systems. It allows for efficient processing and analysis of big data.
• It can help in hiding critical information by fusing it with other data sources making reverse engineering more difficult.

Drawbacks or Disadvantages of Data Fusion

The following are the drawbacks or disadvantages of Data Fusion. Addressing these challenges is crucial for building successful data fusion systems:

• IoT networks, consisting of hundreds or thousands of sensors, generate both trivial and non-trivial data. Processing trivial data can negatively affect the accuracy of data fusion algorithms. Therefore, it’s crucial to prioritize the most relevant features and important data for data fusion.
• Data fusion is not a static process. The dynamic nature of the data and the environment requires adaptive algorithms that can adjust to changing conditions.
• Conflicting data can lead to counter-intuitive results. Data fusion algorithms must handle conflicting data carefully, potentially using conflict resolution strategies.
• Data correlation and alignment should be handled before the data fusion process, especially in Wireless Sensor Networks (WSNs). This ensures that the data from different sensors is properly synchronized and aligned in time and space.
• Data imperfection and inconsistencies must be dealt with effectively while using data fusion algorithms. Techniques for handling missing data, outliers, and noisy data are essential for ensuring the reliability of the results.