Page 297 - Kaleidoscope Academic Conference Proceedings 2024

Innovation and Digital Transformation for a Sustainable World

Plans. Damage Assessment & Analysis is conducted using overview of affected areas [18]. UAVs equipped with high-
multiple tools such as Visual Data, UAV Deployment, resolution cameras and AI-powered image analysis
Crowdsourced Data, and IoT Sensors, feeding into AI & capabilities offer more detailed, localized damage
Predictive Modeling. The framework also includes assessments [19]. Real-time network performance telemetry,
Resources & Infrastructure elements like Backup Power utilizing protocols such as SNMP, NETCONF, and gRPC,
Sources and Network Infrastructure. Telecom Operators provides instant insights into service degradation and
implement various Resiliency Technologies including SDN, potential failures [16]. Crowdsourced data, analyzed using
NFV, SON, NTN, and Self-Healing Networks. The entire Natural Language Processing (NLP) and sentiment analysis
system is designed to enable rapid assessment, coordinated algorithms, offers ground-level perspectives on network
response, and implementation of advanced technologies to status [15].
maintain and restore critical communications infrastructure Network Digital Twins, powered by Graph Convolutional
during and after disaster events. The pillars of this Networks (GCNs), provide real-time simulations of the
framework with their implementation mechanism are network's status and predict potential cascading failures [20].
summarized below: These digital replicas work alongside Recurrent Neural
Networks (RNNs) that forecast network performance based
5.1 Phase 1: Pre-Disaster Preparedness and Early on time-series data [21]. Ensemble learning techniques
Warning combine predictions from multiple models to provide a
The foundation of a resilient telecom network begins with holistic view of network behavior across different layers [22].
robust pre-disaster planning and advanced assessment and Immediate response actions are guided by AI-enhanced
monitoring systems, as also enumerated in ITU-T systems. SDN controllers dynamically reroute traffic around
Recommendation L.392. This phase involves implementing damaged infrastructure, while Intent-Based Networking
intelligent network infrastructure using Software-Defined (IBN) automatically enforces predefined policies to maintain
Networking (SDN) with OpenFlow and P4 programmability, critical services [23]. AI-driven Multi-Protocol Label
and Network Function Virtualization (NFV) leveraging the Switching (MPLS) optimizes label distribution for efficient
NFV MANO framework [11, 23, 24]. These technologies packet routing in the face of network disruptions [24].
enable dynamic resource allocation and rapid
reconfiguration in response to changing conditions. 5.3 Phase 3: Service Continuity and Network
A multi-layered approach to data collection and analysis is Resilience
implemented. High-resolution satellite imagery, including Maintaining service continuity during and after a disaster is
both optical and Synthetic Aperture Radar (SAR) data, paramount. This phase focuses on resource optimization and
provides broad-scale situational awareness [11]. This is the deployment of temporary network solutions. AI
complemented by Unmanned Aerial Vehicles (UAVs) algorithms dynamically allocate spectrum and computing
equipped with multispectral sensors and LiDAR for detailed resources, ensuring efficient use of available capacity [25].
infrastructure inspection [12]. A distributed network of IoT Edge computing resources, aligned with ETSI MEC
sensors, utilizing LPWAN technologies like LoRaWAN and standards, are strategically deployed to reduce latency and
NB-IoT, continuously monitors critical network components offload processing from the core network [26, 29].
and environmental conditions [14]. 5G network slicing, utilizing 3GPP-defined Network Slice
To enhance predictive capabilities, Artificial Intelligence (AI) Selection Function (NSSF) and Network Slice Management
and Machine Learning (ML) models are integrated into the Function (NSMF), creates logically separated virtual
network. These models, such as Random Forests, Support networks for critical services [30]. In the Radio Access
Vector Machines, and Long Short-Term Memory (LSTM) Network (RAN), dynamic spectrum allocation techniques
networks, analyze historical data to predict equipment and Self-Organizing Network (SON) capabilities optimize
failures and optimize maintenance schedules [13, 31]. Graph resource usage and automate parameter tuning [25, 26].
Neural Networks (GNNs) are employed to model and Where traditional infrastructure is severely damaged, Non-
optimize network topology, improving overall resilience Terrestrial Networks (NTN) including Low Earth Orbit
[14]. Early warning systems play a crucial role in disaster (LEO) satellite constellations (altitudes of 500-1200 km) and
preparedness. Edge AI, utilizing Convolutional Neural High-Altitude Platform Stations (HAPS) operating in the
Networks (CNNs) with architectures like ResNet and YOLO, stratosphere (17-22 km altitude) provide wide-area coverage
is deployed for real-time processing of seismic data and and additional redundancy [27, 32]. Drone-based temporary
automatic identification of infrastructure damage from visual networks offer rapid deployment for localized
data [15, 17]. Federated Learning techniques enable communication needs [28].
collaborative model training across multiple sensors without Network resilience is further enhanced through autonomous
compromising data privacy [16]. fault detection and self-healing mechanisms. Distributed AI
agents continuously monitor network performance, detecting
5.2 Phase 2: Disaster Impact Assessment and anomalies and initiating remediation actions [29]. Self-
Immediate Response healing capabilities, implemented through closed-loop
When a disaster strikes, rapid assessment of its impact on the automation and intent-based networking, allow the network
telecom infrastructure is critical. This phase leverages a to automatically reconfigure and optimize itself.
combination of remote sensing technologies and AI-driven
analysis. Satellite imagery, including SAR, provides a broad

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