We live in a data-driven society. Data is everywhere, and it is growing exponentially. It is generated by sensors, devices, applications, and users in various industries, such as healthcare, manufacturing, retail, and more. Data is valuable when it provides insights in a timely manner, or when organizations can utilize it to automate systems. However, managing big data creates challenges such as storage, bandwidth, latency, security, and privacy. How can we leverage the power of data without compromising its quality and usability?
One possible solution is to pre-process the data at the edge, before sending it to the cloud or a centralized server. Pre-processing is the process of transforming, filtering, aggregating, or enriching data to make it more suitable for further analysis or consumption.
While centralized systems are still necessary and provide many tools to analyze data, visualize it and distribute the results, pre-processing at the edge can be done for various purposes, such as:
Reducing the amount of data – Summarization. By summarizing the data at the edge, we can reduce the volume of data that needs to be transmitted and stored, saving costs and resources. For example, instead of sending every temperature reading from a sensor, we can reduce the amount of data by sending only what is relevant, such as the average, minimum, or maximum values over a certain time interval. Another example is surveillance video. Instead of backhauling large amounts of video stream data to the cloud, the upload can be reduced to just the relevant clips.
Finding outliers – Anomaly detection. By detecting anomalies at the edge, we can identify events or patterns that deviate from the normal behavior or expectation, such as faults, errors, or attacks. For example, instead of sending every heartbeat signal from a patient monitor, we can send only the signals that indicate an abnormal condition or a potential risk, enabling timely actions and thus increasing safety.
Anonymizing and PII reduction – Privacy and Data Sovereignty. By anonymizing or reducing the personally identifiable information (PII) at the edge, we can protect the privacy and security of the data owners and comply with the regulations and policies of different regions or countries. For example, instead of sending every face image from a camera, we can send only the features or attributes that are relevant for the application, such as age, gender, or emotion.
Proactive state of the edge – Pattern recognition, Log KPI processing. By recognizing data patterns and sequencing and understanding the characteristics of data generated at the edge, its timing, and spatial aspects, we can monitor and optimize the performance and health of the edge devices and networks, and provide feedback or alerts to the users or operators. For example, instead of sending every log entry from a router, we can send only the metrics or indicators that reflect the status or quality of service (QoS) of the network.
Aggregation and AI algorithms. Multiple data sources can be aggregated to provide a full picture, providing meaningful insights from the vast amount of data generated by edge devices while maintaining low latency and conserving bandwidth. AI algorithms provide predictive maintenance like functions for real-time data analysis, efficient resource usage, and quicker response times.
Pre-processing of data at the edge is not a one-size-fits-all solution. It depends on various factors such as:
Therefore, pre-processing of data at the edge requires careful design and implementation to balance between these factors and achieve the best results.