Edge Computing: Converting Big Data into Relevant Data Efficiently

In our previous article, we highlighted how automotive OEMs have harnessed the power of IoT to address operational challenges and enhance their products. With the increasing adoption of connected machines, an enormous volume of data is being generated. While this data holds immense potential for improving machine performance and user experiences, it also presents significant challenges. In this article, we will focus on the data deluge in IoT and explore why traditional cloud-centric approaches are not fit for all use-cases. Instead, we will introduce edge computing as a game-changing solution that brings relevance and efficiency to converting big data into actionable insights.

Let's understand what the real problem is. Consider a smart factory where real-time monitoring and control of critical machinery are essential for efficient operations. The factory relies on a cloud-based IoT system to collect and process sensor data from the machines. However, due to a sudden network outage or latency issues, the connection to the cloud server is disrupted. This has the potential to derail the factory operations and the decision-making process.?

When we talk about cloud-based IoT systems, we have to rely on the internet connectivity always, which throws problems related to bandwidth limitation depending on the volume of data being transmitted, network congestion, latency(the round trip time between the machine and the cloud) and sometimes internet failure. Another big factor which may not be visible is the cost of cloud infrastructure. Cloud-based data storage and transmission come with associated costs. As the volume of data generated by connected vehicles increases, the expenses related to data transmission, storage, and processing in the cloud can become significant, making it financially burdensome for automotive OEMs.

Now, Let's understand what Edge Computing is and how it can fix these limitations. As you can guess from the word, Edge means closer to the source. Edge Computing brings data processing and analytics closer to the source, at the edge of the network. This can fundamentally eliminate the problems arising from bandwidth, latency, network failure etc. The higher computing capability at edge can also help in reducing the cost related to transmission and storage in the cloud.

Tesla, known for its advanced autopilot features, utilises a combination of cloud and edge computing to power its self-driving capabilities. While cloud-based systems provide access to vast computing resources and advanced algorithms, they heavily rely on stable and high-bandwidth network connections. In situations where the vehicle encounters areas with poor or no network coverage, the reliance on the cloud alone could lead to delays in processing data and making crucial driving decisions.

To address this challenge, Tesla incorporates edge computing capabilities directly into their vehicles. The onboard computing system, known as the "Tesla Full Self-Driving (FSD) Computer," enables real-time data processing and analysis directly within the vehicle. This allows the car to make critical decisions quickly, such as identifying obstacles, analysing traffic patterns, and executing autonomous driving functions, without being entirely dependent on cloud connectivity.

In the era of connected vehicles, smart healthcare edge computing emerges as a powerful solution for many applications. By bringing data processing and analytics closer to the source, edge computing overcomes the limitations of traditional cloud-centric approaches. Edge computing also improves data security and privacy by keeping sensitive information localised, mitigating concerns about data breaches and compliance. Though we are reducing cloud costs with Edge computing, we may have to invest in more sophisticated hardware devices at the edge to support computing and storage. In the next article, we will talk about the right approach between choosing edge computing vs cloud-only systems for IoT projects.?

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