Edge-native AI – Solving Telecom Intelligence Challenges

The explosion in connected IoT devices, combined with telecom networks’ transition to 5G, has created new opportunities and challenges for telecom operators. To better manage the millions of devices within their ecosystems some operators have constructed separate IoT device-management networks. In many cases, those networks have been augmented by cloud-based artificial intelligence (AI) and machine learning (ML) solutions.

For the development of next-generation telecom solutions operators are looking to move their AI and ML capabilities much closer to the components and assets within their IoT networks. Doing so will provide the levels of real-time intelligence and analytics required to maximize the performance, reliability, and security of these new networks.

Telecom Challenges

In most cases the telecom segment has relied on legacy—cloud-based—AI and ML solutions. Those solutions provided operators with the ability to leverage the intelligence produced by those solutions to improve operational efficiencies and to deliver new/improved solutions to their customers. However, those legacy solutions had limitations that have created challenges for network operators. Those challenges have included:

• Collection and aggregation of device analytics: No ability to automate the collection and visualization of performance analytics on devices that share a common IMEI TAC.
• Intelligence gaps in network and device performance monitoring: The lack of device and network AI enablement and training at the extreme edge makes it difficult for operators and device OEMs to acquire the real-time performance insights that would bring additional value to their customers.
• Network, device, and data security: Cloud-based AI solutions can increase exposure of networks and devices to todays’ sophisticated cyber-threats.

The Solution

By moving intelligence enablement and training much closer to a network and its devices, an Edge-native AI solution provides a more intimate, and more interconnected, approach to building and managing an IoT device network. AI at the extreme edge will deliver next-generation capabilities that include:

• Dynamic performance optimization: AI and machine learning embedded into the network and integrated with smart device data improves the performance of the entire telecom ecosystem.
• Real-time, holistic, visibility: AI at the extreme edge provides network operators and device OEMs with deeper insights into the complex interactions between devices and their networks.
• Enhanced cyber-security: Self-learning algorithms provide the real-time intelligence needed to protect networks, devices, and data from today’s sophisticated Zero-Day cyber-attacks.
• IMEI TAC aggregation: Edge-native AI device intelligence that is aggregated, synthesized, and visualized by network operators and OEMs, enabling performance assessment on devices that share a common IMEI TAC.

The Impact

The transition from cloud-dependent solutions to Edge-native AI solutions will allow telecom companies to develop differentiated offerings while also improving operational efficiencies and improving customer stickiness. Transformational benefits will include:

• Improved network performance: Edge-native AI embeds and trains AI and ML algorithms that produce predictive analytics that improve network performance and reduce costs.
• Deeper network and device visibility: The ability for telecom operators to transmit device performance data back to device OEMs and customers in real time.
• Intelligent security: Self-learning security protocols embedded at the network and device levels deliver state-of-the-art security to operators, OEMs, and customers.
• Improved brand loyalty: Quality of Service (QoS) metrics generated by AI-enabled analytics power the fine tuning of the customer experience and improves brand loyalty.