SARAHAI-SERVICE_PROVIDER

A Telecom AI Agentic Resource for IoT, 5G/SD-WAN, Edge, & Anomaly Detection
1. Introduction & Overview
SARAHAI-SERVICE_PROVIDERv6.1 is an AI-driven platform designed for Telecoms/ISPs to reduce operational costs, proactively detect anomalies, and optimize resources across networks, edge sites, and IoT deployments. It leverages U.S. Patent No. 11,308,384 (Pattern-of-Life + KDE anomaly detection) exclusively licensed to Tensor Networks and includes:

IoT Device Monitoring & Security (detect compromised devices, unusual traffic/battery usage).
SD-WAN/5G Slicing Optimization (automatically allocate resources among slices).
Edge Node Monitoring (Multi-Access Edge Computing, anomalous CPU usage/traffic).
Fraud Detection (call routing anomalies, billing inconsistencies).
Capacity Planning (predict throughput usage with ARIMA).
Pattern-of-Life (PoL) + KDE advanced anomaly detection for user call data and IoT.
Structured OpenDocument (ODS) Reporting for regulatory or corporate compliance.
2. Key Features & Functions
2.1 IoT Device Monitoring & Security
IoT Telemetry: Continuously ingests device data (battery levels, firmware, traffic bytes).
KDE-Based Anomaly Detection: Pattern-of-Life modeling to identify compromised or malfunctioning IoT devices.
Malfunction Threshold: Set a fraction of anomalies for immediate operator alerts.
2.2 SD-WAN & 5G Network Slicing Optimization
Slice Data Collection: Gathers usage data (Mbps) for each slice (e.g., LowLatency, HighThroughput).
Adaptive Allocation: Automatically adjusts resource percentages based on traffic usage and anomalies.
SLA Assurance: Prioritizes “CriticalServices” or “LowLatency” slices if anomalies or usage surges occur.
2.3 Edge Node Monitoring (MEC)
Edge Telemetry: Tracks CPU usage, traffic volumes, anomaly scores at distributed sites.
Federated or Hierarchical: Aggregates local anomaly signals to detect region-wide patterns.
Proactive Security: Identifies sudden spikes or suspicious usage across multiple edge nodes.
2.4 Fraud Detection
Single-Metric KDE: Detect suspicious call durations, call routing anomalies.
Call Data Integration: Ingests call records (caller_id, duration_sec, timestamp, etc.).
Revenue Protection: Prevents subscription fraud, premium call abuse, out-of-pattern usage.
2.5 Capacity Planning
ARIMA Forecasting: Predict throughput usage, latency, or general demand across the network.
Resource Provisioning: Scale up or down virtual network functions or containers based on usage patterns.
Cost Optimization: Avoid overprovisioning while preventing SLA violations during peak.
2.6 PoL + KDE Anomaly Detection
Patent 11,308,384: Multi-dimensional Pattern-of-Life plus Kernel Density Estimation.
Time-of-Day & Usage: Model normal user/device behavior, detect outliers.
Multi-Modal: Works for call data, IoT data, slice usage, edge metrics, etc.
2.7 OpenDocument (ODS) Reporting
Data Export: Consolidates call, slice, edge, IoT data into a single ODS file.
Regulatory Compliance: Compatible with enterprise or government open document standards.
Customizable: Extend to generate ODT or CSV outputs if needed.