Moving Artificial Intelligence to the EDGE: Transforming Data Processing and Efficiency

Artificial intelligence (AI) is advancing rapidly, solving scenarios that seemed impossible just a short while ago. In this newsletter, we delve into how AI, typically deployed in cloud networks, is now being harnessed in edge computing environments. Discover the benefits of moving AI to the edge and explore real use cases that demonstrate its potential.

Understand what Edge Computing means

Edge computing refers to the action that brings the computing and data storage closer to the location where it is needed. This is done because it can improve response times and save bandwidth. It compares to what has perhaps become a more traditional scenario of cloud computing setups, where data is sent to a centralized data center for processing and storage. However, with edge computing, data processing and storage are performed closer to the source of the data, often at or near the "edge" of the network, such as on IoT devices, routers, or servers located in proximity to end-users.

Edge computing for applications with Artificial Intelligence

Edge computing has specific advantages which certain artificial intelligence – AI scenarios and use case can take advantage of.

?? Local Data Processing: Instead of sending raw data to the cloud for analysis, edge devices can run AI algorithms directly on the data they collect. This is particularly important for applications requiring real-time processing.

?? Reduced Latency: By processing data closer to where it is generated, edge computing reduces the time it takes for data to travel between the source and the processing point. Again, this will benefit real-time processing scenarios.

?? Bandwidth Optimization: Edge computing can help reduce the amount of data that needs to be transmitted to centralized data centers, thereby alleviating network congestion and reducing bandwidth costs. This is especially beneficial in AI scenarios where large volumes of data are generated.

?? Improved Reliability: By distributing computing across multiple edge devices, the system becomes more resilient to failures. If one edge device fails, others can continue to operate independently, reducing the risk of downtime. This ensures that critical AI functionalities remain available regardless of network conditions, enhancing system reliability and resilience.

?? Data Privacy and Security: Edge computing can enhance data privacy and security by keeping sensitive data localized and minimizing the need to transmit it over public networks, when this type of data needs to be worked on by AI applications.

?? Scalability & Flexibility: Edge computing enables scalability by distributing computing resources across a network of edge devices. This allows for flexible allocation of resources based on demand, ensuring that applications can scale efficiently to accommodate varying workloads of any artificial intelligent- AI applications.

?? Edge-to-Cloud Integration: While AI processing primarily occurs on edge devices, edge computing can also complement cloud-based AI services by offloading certain processing tasks to the cloud when necessary. This hybrid approach enables a seamless integration of edge and cloud resources, optimizing the overall AI infrastructure for performance, scalability, and cost-effectiveness.

?Use Case that deploy Edge computing with Artificial Intelligence

The number of use case that can use edge computing with artificial Intelligence – AI are numerous. However, there are three very specific use cases, that we have come across. These are computer vision, onboard diagnostics and network traffic anomalies.

??? Computer vision

Computer vision is a field of artificial intelligence (AI) and computer science that enables computers to interpret and understand visual information from the real world. It involves the development of algorithms and techniques that allow machines to analyze, process, and extract meaningful insights from digital images or videos.

The process begins with the capturing of images or videos using cameras or other imaging devices. Once the images are captured, preprocessing techniques may be applied to enhance the quality of the images or videos. This may involve tasks such as noise reduction, image resizing, color correction, or image normalization to standardize the visual data. Then relevant features or patterns are extracted from the images to represent meaningful information and they can then be transformed into a format that can be understood and processed by machine learning algorithms.

The machine learning models are trained to recognize patterns, objects, or activities in the visual data. After training, the machine learning models can be deployed to perform inference on new, unseen data. During inference, the trained models analyze input images or videos and make predictions or classifications based on the patterns they have learned from the training data. This allows computer vision systems to recognize objects, detect anomalies or track motion for example.

Finally, post-processing techniques may be applied to refine the output of the computer vision system or visualize the results. This could involve tasks such as filtering out noise, refining object boundaries, or overlaying annotations on the images to convey additional information.

A real use case scenario could be applied to a logistics company. For example, to control all the packages that go through a hub and to make sure that the correct type of packages go to the correct vehicles.

???Onboard diagnostics

Onboard diagnostics refers to the capability of a vehicle to self-diagnose and report issues related to its performance. OBD systems can monitor various components and systems of a vehicle, such as the engine, transmission, emissions controls, and more. They can detect and report malfunctions or performance issues.

Onboard diagnostics systems continuously collect data from various sensors and onboard communications platforms on the vehicle. They can monitor parameters such as engine speed, coolant temperature, oxygen levels, or fuel injection timing among other issues. With edge computing, the collected data is processed locally within the vehicle, allowing for real-time analysis of the data without needing to transmit it to external servers for processing. AI algorithms deployed on the edge computing platform can analyze the sensor data to detect patterns, anomalies, or potential issues with the vehicle's performance.

AI algorithms can be trained to recognize complex patterns and correlations within the sensor data that may indicate underlying problems or malfunctions in the vehicle. Machine learning techniques, such as supervised learning or anomaly detection, can be employed to train AI models on historical data to accurately diagnose issues and predict potential failures.

Once a potential issue is detected, the AI system can provide real-time feedback to the vehicle's onboard display or diagnostic interface, alerting the driver or service technician to take appropriate action. This proactive approach to diagnostics can help prevent costly breakdowns, reduce maintenance costs, and improve overall vehicle reliability and safety.

Indeed, the collected data from vehicles can be transmitted to centralized fleet managers for further analysis and monitoring. AI-based predictive maintenance algorithms can identify trends and patterns across a fleet of vehicles, enabling proactive maintenance scheduling and optimization of maintenance activities.

???Traffic anomalies

Using edge computing and AI to detect traffic anomalies is a powerful approach that can improve the efficiency of transportation systems, enhance safety, and reduce congestion.

Traffic data, including vehicle speed, volume, and flow, can be collected from various sources. Edge computing devices analyze the collected traffic data in real-time using AI algorithms.

These algorithms can detect anomalies by comparing current traffic patterns with historical data or predefined thresholds. For example, sudden changes in vehicle speed, unexpected congestion, or irregular traffic flow patterns, all of which may indicate an anomaly. This can be detected and processed by the edge computing device and AI algorithms using machine learning models for example. They would generate real-time alerts to notify traffic management authorities, transportation agencies, or even the drivers.

This would enable adaptive traffic management systems that could be integrated into navigation apps to provide alternative routes to drivers and minimize delays.

Overall, edge devices can fuse data from multiple sources, such as traffic sensors, weather stations, and event databases, to improve the accuracy of anomaly detection. By integrating diverse data streams, AI algorithms can better distinguish between normal fluctuations in traffic patterns and genuine anomalies caused by incidents or emergencies. Moreover, Edge computing enables distributed deployment of AI algorithms across a network of edge devices, ensuring scalability and resilience in traffic anomaly detection systems.

Teldat has dedicated substantial resources to developing edge computing and AI solutions. Our Celer edge computing devices and software are designed to address your challenges efficiently and effectively.

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