AI Enhancement Service Assurance: Preventing Communication Failures in Critical Networks

In natural disasters and life-threatening emergency situations, communication failures can have catastrophic consequences. First responders depend on accurate, real-time information to save lives, and this critical data has become increasingly diverse with multi-channel voice, video, SMS, and even social media platforms. Situational awareness becomes more complex across first responders, government agencies, and local communities. Disruptions to critical communications can stem from infrastructure damage, human error, network congestion, environmental conditions, and poor maintenance or lack of power management.

This session explores the pivotal role of real-time, end-to-end service assurance for critical networks enhanced by artificial intelligence to ensure uninterrupted communication delivery. AI-based end-to-end service assurance is vital for managing multi-vendor, multi-protocol, and geographically dispersed critical networks and communications. By leveraging AI, machine learning, and automation, these systems can optimize fault, performance, configuration, and location management, ensuring seamless and secure operations across complex network environments.

1. Risk Detection and Analysis: AI algorithms can analyze communication infrastructure for single points of failure and run ‘what-if’ analyses based on real-world scenarios. This helps identify potential risks to service availability and determine where redundancy is required. This also applies to preventive maintenance for network assets.

2. Fault Management: AI-based correlation of real-time events with automated resolution of issues across multi-vendor, multi-protocol network environments. By analyzing alarm patterns and trends, AI can determine root cause analysis and predict future network degradation or outages.

3. Performance Management: AI-driven tools monitor network key performance indicators (KPIs) in real-time, analyzing in-depth metrics for service quality. This proactive analysis measures KPIs that support the critical infrastructure, including latency, packet loss, jitter, bandwidth utilization, environmental conditions, HVAC, AC/DC, and weather.

4. GIS-based Location Management: Location-based services across geographically dispersed networks rely on AI to provide situational awareness of critical network locations and response tracking.

5. Closed-Loop Automation and Self-Healing Networks: AI closes the loop from outage detection or prediction to action. AI implements preventative measures, reducing downtime and ensuring service continuity.

By attending this session, public safety officials, emergency managers, and communication professionals will gain valuable insights into optimizing their alerting systems, ultimately enhancing their ability to protect and inform the public during crises.