Many edge devices we come across are as small as a flash drive. They are adaptable to integrations and are available as boards and development kits. They can, therefore, easily be plugged in and installed into any system without any intrusion on the functioning of the main hardware. Challenges to using Edge devices While edge devices offer many benefits compared to current methods, there are some challenges with using this technology. Edge intelligence risk Edge intelligence refers to analyzing data close to where it is collected. While this provides advantages in computing performance, physical data poses a significant challenge, especially where edge technology is continuously mobile. For example, a drone monitor- ing territory of an enemy state close to its border could malfunction or be shot down, allowing it to be collected by the enemy military, resulting in a major strategic and surveillance failure. Security Protecting an edge device from advanced adversarial attacks and hacking by malicious users requires very sophisticated encryption of the edge device, the data, and the model, which can be costly and time-consuming to develop and implement. Data scarcity As with all ML applications, high-quality data is neces- sary for high-quality training. So, another challenge in edge ML is the scarcity of real-time training data for the model. The data is collected and processed in cloud-based services with a vast central database. But ML applications use real-time data for training/updating models on edge devices, which is usually self-collected by the device, thus having limited storage and process- ing capabilities compared to the large servers used in cloud computing. Federated learning Federated learning solves the problem of gaps in gathered data by the edge device. It enables the development of a single model trained on several different data sets from various sources without the parties ever needing to exchange their critical data. However, federated learning is only suitable for group training since there are still some concerns about the privacy and security of the data. Therefore, it is not
exceptionally suited for ML operations that are highly clandestine or top-secret.
Data consistency Data consistency is another challenge, and it occurs primarily due to the inefficiency of the sensors of the edge device – noise in the background or environment gets superimposed on “useful” collected data. To overcome this issue, companies will have to use data augmentation to effectively teach the model how to filter out the noise. The breakthrough: Fractal’s forward-looking IVA models Fractal has developed deep learning-based image and video analysis (IVA) models deployed on edge devices, allowing data processing at the source of data capturing. We have already implemented IVA models for some of our clients. A cut above: IdeaForge drone surveillance IdeaForge is one of India’s leading manufacturers and was ranked 7th among the top dual-use drone manufacturers in the world by Drone Industry Insight. They are a key supplier of UAV technology to the Indian Armed Forces, focused on surveillance and mapping solutions. Fractal is IdeaForge’s strategic partner in developing drone technolo- gy, especially for difficult terrains and border patrol areas, and is helping to realize the Indian Army’s “Year of Transformation” goal for 2023.
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