Leveraging AI in Device Management: Enhancing Current Practices
Introduction
Device Management encompasses the processes and technologies used to remotely configure, monitor, update, and secure connected devices. It plays a critical role in ensuring smooth device operation and effective lifecycle management. While current device management solutions largely rely on established standards—most notably TR-069 (CPE WAN Management Protocol, CWMP) — the emergence of new device types, innovative use cases, and technological advancements is opening up exciting new opportunities in this domain.
Understanding the Challenges
A. The Value of Traditional Device Management
Traditional Device Management in the telecommunications sector allows Internet Service Providers (ISPs) to remotely configure, monitor, and manage Customer Premises Equipment (CPEs). Modern CPEs encompass routers, set-top boxes, VoIP phones, and smart home hubs, among other connected devices.
Key functionalities in this domain include:
Device onboarding and provisioning – Seamless setup and activation of new devices
Configuration and policy management – Centralized control over settings and operational policies
Firmware and software updates – Ensuring devices stay up to date and secure
Performance monitoring and diagnostics – Real-time insights into device health and network performance
Security management and compliance – Protecting devices from cyber threats and ensuring regulatory adherence
Remote control and troubleshooting – Resolving issues without requiring on-site intervention
As connected devices evolve, so do the challenges and opportunities in device management, driving the need for more scalable, secure, and intelligent solutions.
B. Addressing Operational Inefficiencies
The TR-069 standard, widely used in device management, faces several challenges that limit its effectiveness in modern network environments. One of the primary issues is its lack of strong default security, which can expose devices to potential threats and unauthorized access if not properly configured. The protocol also struggles with scalability, as its session-based communication relies on expensive handshakes for each transmitted message, leading to inefficiency and potential performance bottlenecks in large-scale deployments.
Another significant drawback is the difficulty in filtering data from devices. TR-069 typically forwards device data in large XML files, which can be bulky and inefficient. This approach makes it challenging to extract specific data, complicates data analysis, and increases network load, particularly when managing millions of devices. As networks expand and new device types and use cases emerge, these limitations highlight the need for more modern and flexible protocols.
How to Enhance Device Management
A. New Protocol
To address the limitations of the TR-069 protocol, its successor, TR-369/USP (User Services Platform), was introduced. This new protocol incorporates strong encryption and authentication mechanisms, significantly enhancing security. It also enables distributed management, allowing CPEs to connect to multiple ACSs, improving flexibility and scalability.
Communication in TR-369/USP is far more efficient, as it allows retrieval of specific data instead of transmitting large XML files. Unlike TR-069, which relies on a pull-based approach, USP supports sophisticated notifications through a push mechanism, reducing latency and improving responsiveness. Additionally, USP enables an always-on connection with CPEs, providing real-time monitoring and instant issue detection, making it a more advanced and future-ready solution for device management.
B. AI Opportunities
Device management solutions gain additional functionalities through the integration of AI technologies, enabling a wide range of advanced use cases. AI-powered capabilities can enhance network management by providing anomaly detection, which identifies events that negatively impact customer Quality of Experience (QoE) and pinpoints their root causes.
Through predictive analytics, AI can proactively detect hardware and software failures before they occur, allowing for preventive maintenance and minimizing downtime. In the realm of customer support, AI-driven chatbots can automate responses, streamline issue resolution, and enhance user satisfaction. Moreover, AI can predict cross-selling and up-selling opportunities, offering personalized recommendations that drive business growth. These advanced functionalities not only improve operational efficiency but also unlock new opportunities in device management and customer engagement.
Improving Efficiency with AI in Device Management
A. Real-Time Monitoring and Network Optimization
Real-Time Monitoring is a powerful functionality that provides valuable insights into network anomalies. However, when applied to large fleets of devices over extended periods, it can generate massive amounts of data, which may overwhelm storage and processing capabilities. To address this challenge, smart data collection mechanisms are required to optimize efficiency.
One effective approach is to implement a hybrid monitoring strategy. Initially, data collection and processing can occur at lower frequencies to identify network elements with irregularities or performance degradation. Once potential issues are detected, real-time monitoring can be selectively activated to gather more granular data only when necessary.
By using AI algorithms it is possible to go beyond anomaly detection and analyze root causes of detected disruptions, such as faulty wiring, overcrowded wireless channels, or interference. The ability to proactively identify issues allows for faster resolution, whether through automated configuration changes or by dispatching technicians to the field before customers experience disruptions. This approach not only enhances operational efficiency but also improves customer satisfaction by preventing service interruptions
B. Proactive Maintenance
Proactive Maintenance is crucial for ensuring optimal performance and reliability of network equipment. By leveraging Machine Learning (ML) models, operators can analyze historical data from CPE to predict potential device failures or performance degradation.
These ML models can detect patterns that often precede common issues, such as signal degradation, power supply failures, or hardware malfunctions. By anticipating failures before they occur, operators can take preemptive actions, such as sending replacement devices or deploying technicians to address issues proactively. This approach minimizes downtime, enhances network stability, and significantly reduces customer complaints, contributing to improved customer satisfaction and operational efficiency.
C. Speech Agents
AI-powered virtual assistants integrated into Home Portals can intelligently guide customers through troubleshooting steps, helping them resolve issues independently. By leveraging real-time data, these virtual assistants deliver personalized and accurate recommendations, ensuring that customers receive relevant support tailored to their specific situation.
This approach enhances customer self-service capabilities, empowering users to fix common issues on their own while maintaining a high level of assistance. As a result, it reduces the volume of calls to customer support, lowers operational costs, and improves overall customer satisfaction through faster issue resolution
Conclusion
The advanced use cases in the device management domain will be deployable in the AXESS 6 platform, which offers a comprehensive suite of device provisioning and monitoring capabilities. The platform's highly configurable data collection framework supports a variety of protocols, including TR-069, TR-369/USP, SNMP, and more, allowing seamless integration with diverse device ecosystems.
Data can be collected in flexible formats, whether through real-time or interval-based streaming or via on-demand polling, offering tailored approaches to data acquisition. The platform also allows fine-grained control over data storage, enabling users to choose whether data should be stored in databases or only displayed in dashboards, optimizing data collection efficiency and resource management.
A standout feature of the upcoming AXESS 6 is its AI-readiness. The platform allows for training and testing of AI models using data from the Expert Systems Database or a Private Database with anonymized data, ensuring data privacy while unlocking a wide range of AI-driven use cases. These capabilities empower organizations to leverage predictive analytics, anomaly detection, automated troubleshooting, and other AI-based functionalities, driving smarter device management and enhanced operational efficiency.
Written by Pavle Skocir, PhD
Pavle Skocir is working in Axiros as a Technical Account Manager. He is an IoT enthusiast and has previously worked on numerous applications in the smart home environment, as well as solutions for the IoT interoperability. Currently his focus in Axiros is on solutions for WiFi and access network monitoring and analytics.