Edge AI, which involves running AI algorithms on local devices rather than in centralized data centers, promises reduced latency, enhanced privacy, and lower bandwidth usage. However, several technical challenges hinder its widespread adoption. This post delves into these challenges, including computational constraints, data management issues and security concerns.

Computational Constraints

One of the foremost challenges in Edge AI is the limited computational power of edge devices compared to centralized cloud servers. Edge devices, such as smartphones, IoT devices, and sensors, typically possess less processing power, memory, and energy resources. This limitation affects the performance and capabilities of AI models deployed at the edge.

Many state-of-the-art AI models require substantial computational resources, which are often beyond the capacity of typical edge devices. Therefore, significant efforts are needed to develop models that are lightweight yet effective, allowing them to run efficiently on hardware with limited resources.

Although these remain formidable challenges, Aikaan has the necessary features that help the engineers to monitor and take actions, if critical resources like CPU or memory is abnormal. As seen in the screenshot below, on the Reports and Events from the main page of the Aikaan Controller, with a few simple clicks, the engineer can generate reports to monitor CPU Usage, device temperature, device uptime etc. 

The same is true for the Events tab as well. The Ops engineer can configure Events to trigger when specific sets of conditions are met. Furthermore, all the generated events can be forwarded to any mail id, slack id or whatsapp account too.

These two features help the engineer to constantly monitor his system and take necessary actions when needed.

Data Management Issues

Data management at the edge presents another significant challenge. Edge devices often operate in environments with intermittent connectivity, making it difficult to synchronize data with central servers. Additionally, edge devices generate large volumes of data, which need to be processed, stored, and managed locally.

Maintaining data consistency is critical, especially for applications requiring real-time or near-real-time data processing. Delays in data synchronization due to connectivity issues can impact the accuracy and timeliness of AI decisions. Therefore, robust solutions are required to ensure data is accurately and promptly synchronized between edge devices and central servers.

Aikaan provides tools for such scenarios. Aikaan’s Network Monitoring feature helps the engineers to keep an eye on connectivity issues of the entire EDGE AI network. On login to Aikaan Controller, you can see the list of devices and their connectivity status. The screenshot below shows the same and the “Status” field gives a birds eye view of the connectivity status of each of the devices connected to the platform. If a device loses connectivity the status immediately changes to a Red ‘X’ mark. This way the engineer can look out for devices with frequent or lengthy connectivity issues and address it straightaway, mitigating some of the Data Management issues.

Security Concerns

Security is a paramount concern for Edge AI, given the sensitive nature of the data processed on edge devices. Edge devices are often more vulnerable to physical and cyber-attacks compared to centralized data centers. Protecting AI models and data on these devices requires robust encryption, secure boot processes, and real-time anomaly detection.

Ensuring the security of data on edge devices is crucial, as unauthorized access can lead to significant privacy breaches. Additionally, securing the AI models themselves from tampering or theft is essential to maintaining the integrity and reliability of the AI systems deployed at the edge.

Aikaan provides all the necessary tools for an engineer to easily push the necessary security patches across multiple devices via OTA (Over The Air). The ‘Device Profile’ parameter helps the engineer to group the devices based on various parameters like OS Version, Hardware Version, Security patch version, etc. Next, the binary that needs to be pushed to the device can be further targeted towards each device profile. This is done by generating  different artifact for each of the device profiles defined.

The next step is to deploy these artifacts onto thousands of devices across the network. Aikaan manages these deployments internally and makes sure all the deployments are carried out with multiple retries along with re-initiation of failed deployments. Aikaan also provides a live logging of each of the deployments for easy monitoring and debugging.

With the help of Remote SSH sessions, the engineer can check the status of these deployments from a shell terminal as well. The same sessions can also be used to check the current patch level, OS versions,etc. Below is a screenshot of a shell terminal opened from within the Aikaan Controller:

Below is the OTA Upgrade section of the Aikaan Controller, which gives a list of artifacts ready to be deployed.

With the help of the flexible OTA Upgrade feature on Aikaan, the engineer can make sure the security and integrity of the whole Edge AI network is maintained at all times.

Conclusion

The adoption of Edge AI in the AI industry faces several technical challenges. Computational constraints necessitate efficient model optimization techniques. Data management issues require robust solutions for synchronization and consistency. Security concerns must be addressed to protect sensitive data and models. Overcoming these challenges is essential for realizing the full potential of Edge AI. Aikaan provides all the necessary tools and has various features to make this adoption of Edge AI smooth and productive.

To know more about Aikaan and its various features visit www.aikaan.io 

You can also open a free account and explore the platform by signing up at https://experience.aikaan.io/signup