Telco Network APIs for Emergency Response Systems and Other Applications Business Cases: Architecture, Implementation, Evaluation, AI

11 min read3 days ago

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Inter-connected telecom world (source: free adobe stock)

Introduction

This article presents an implementation of innovative CAMARA (Telco Global API Alliance) Network APIs (and related GSMA OpenGateway Project) — projects which are still in development, and implemented only in a few countries, by some telecoms operators, with some APIs, and which can be integrated also into an Early Warning Emergency Response System, or other private or public general applications.

This system leverages telecommunications network data through APIs for Population Density Data, Device Location Retrieval, and Geofencing capabilities, to enhance emergency services operations. The complete architecture, implementation details, integration approach using CAMARA, is described and demonstrated as a practical application in Urban Emergency Scenarios. The implemented demo system integrates four Orange Network APIs, through OAuth 2.0 authentication, processes real-time network telemetry, and provides decision support for emergency response teams. The result show the feasibility of standardized telecom APIs for Telecom based apps, with low latency and support for concurrent operations.

It was designed as a modular unified app, so that other applications and architectures could include, use, or be part of the system, and it’s open-source libraries:

• Unified Full OpenAPI CAMARA lib (unofficial);
• CAMARA SDK & MCP Server (and npmjs packages — unofficial)
• CAMARA Emergency Demo App (and github code)

Background

This project started as an application thanks to Network APIs Hackathons 2025, by ORANGE Romania (and Global Competition), currently held in some countries, where such standardized operator APIs are currently, partly, or fully implemented, and hackathons for commercial usage of those, or as in my case, of research with Demo App implementation, using Orange’s Playground APIs test environment (CAMARA compatible), and the open-source libraries, to be integrated to other systems, or other future apps, that could be transformed to business cases and implementations.

Another article about it, focused more on technical details, as an emergency application, you can read on: Authorea/TechRxiv — Integration of CAMARA TELECOM Network APIs for real time emergency response systems architecture implementation and evaluation.

Architecture, AI Implementation, Evaluation

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System Architecture (source:author)

You can read more about the architecture and implementation of the project here also.

So basically, having applied to the hackathon, I already knew what I was going to work on, that is, using the APIs for improving the Emergency Warning System I am working on for a long time (in my spare time, and mostly on architectural, design level, but also integrating multiple diverse parts from different areas in it, module by module).

This APIs challenge provided an opportunity to include real-time emergency handling, route-planning, etc., mostly for Urban Settings, based on Telecom real-time and statistical based information, to other systems.

Having a clear structure on what needs to be achieved, I concluded to a limited architecture design, until the final demo app and test, with the help of some AI tools and automations, as:

• Simple scripts and GitHub actions — project to gather and assembly a full OpenAPI spec file from all the other CAMARA repositories, for automated project syncing of files and master OpenAPI generation;
• Stainless — for generating SDKs/MCP server from the other project I created;
• Demo App (backend/frontend), for which I had help from Copilot/ChatGPT 5 Codex /Claude 4.5, and, while is fully functioning both with mock data and with connections to the real Orange Developer Account Network APIs Playground, I do not feel yet the project is 100% within the Definition of Done of Software Engineering processes (for example, it’s now missing: quality induced by tests and automated tests — not available in this version, and also some processes, management and fully compliant with all standards and requirements, end version, non-functional aspects not yet tested or satisfied, being an mvp demo, done as a side project, on limited resources, at the moment);

Using AI for some parts of this article and coding assistants, for the Demo App: Copilot for research - this time proved not too very resource-full in addition to what I had already knew and wanted to achieve, but some suggestions for the system were useful (if you guide it and give the right definitions, ask the right questions, or response exactly, to questions, with what it should be done, to guide the direction of the AI Assistant to the direction wanted, it can come up with what you need, in a basic or more complex version), and also the initial code by it (especially front-end where I am limited in experience), which further had to be refined, by using other agents or manual code edits, both in back-end and front-end.

I used my regular setup (Ubuntu, WSL, VSCode), as when I did all the coding myself, without any AI), but this time using and testing which could deliver more value, from a Product Engineering Manager / Software System Architect, point-of-view. That value, basically meant to me: to deliver concrete solutions, which are complete, bug-free, in a short period of time, aligned with other software engineering and development principles and the project’s overall structure, scope and deliverables. So, while being far from doing this on their own, yet, they, eventually (after iterations, changes, manual edits) achieve the end goal, more or less.

Cannot yet say which is my favorite one in generating the right code, or at least fixing bugs and not getting in the way more, over-complicate things or not respecting initial product definition and architecture, but at this time all seemed to have contribute to them, until the demo was up and running, with all the process that included generation, edits, manual deploying of code, accounts, pipelines, settings and things, monitoring, logging, testing.

Further more by following the repositories of code, you can find more documentation on how to use it, and on the MCP Server and how it can integrate CAMARA APIs with AI Assistants and provide a simple way for users to interact directly in plain language with the Network APIs.

Current demo app parts (these Telco Network APIs usage), could be included the other project for Early Warning Emergency System, as specific external integration, and used mostly where defined, in corroboration with the existing DB data, or as a separate module interface.

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demo app

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demo app

Use Cases & Commercial Business Applications

1. Smart City Traffic & Congestion Management

Use Case: Municipal authorities use real-time population density data to optimize traffic flow and reduce congestion.

Key Features

• Real-time density heatmaps for traffic management

• Dynamic route optimization based on crowd concentration

• Integration with existing traffic light systems

• Analytics dashboard for urban planners

Target Customers

• City governments and transportation authorities

• Traffic management agencies

• Urban planning departments

Current Gap: Existing solutions use fixed sensors and historical patterns; real-time mobile network data provides immediate visibility.

2. Crowd Safety for Event Organizers

Use Case: Event venues monitor real-time crowd density to prevent dangerous overcrowding and optimize emergency response.

Key Features

• Pre-event geofence setup for zones (entrances, exits, emergency areas)

• Real-time crowd monitoring with automatic alerts

• Integration with emergency services dispatch

• Post-event crowd flow analysis

Target Customers

• Stadium and arena operators

• Festival and concert organizers

• Convention centers and museums

• Large public gathering venues

Current Gap: Most events rely on capacity estimates; no real-time, network-based crowd monitoring at scale exists.

3. Retail & Shopping Center Intelligence

**Use Case: **Retailers understand actual foot traffic patterns and customer movement through facilities.

Key Features

• Parking lot density monitoring

• Store section heat mapping

• Customer journey tracking (entrance to checkout)

• Real-time traffic alerts for unusual patterns

Target Customers

• Shopping mall operators

• Department and hypermarket chains

• Commercial real estate managers

Current Gap: Current solutions use WiFi/Bluetooth beacons with limited range; no outdoor-to-indoor tracking exists.

4. Healthcare Facility Management

Use Case: Hospitals optimize emergency department capacity and ambulance routing using real-time population data.

Key Features

• ED occupancy prediction and forecasting

• Intelligent ambulance routing to avoid high-density areas

• Disaster response coordination

• Staff optimization based on predicted demand

Target Customers

• Hospital chains and networks

• Emergency department directors

• Ambulance services

• Public health agencies

Current Gap: Hospitals lack real-time visibility into ED congestion; ambulance routing GPS-only.

5. Campus Optimization

Use Case: Large organizations optimize facility usage and employee experience based on real-time occupancy patterns.

Key Features

• Real-time building occupancy heatmaps

• Meeting room availability prediction

• Shuttle service demand forecasting

• Energy consumption optimization

Target Customers

• Technology companies with large campuses

• Corporate headquarters

• University and research campus managers

• Co-working space operators

Current Gap: Campus operators use access cards, WiFi, counting sensors; no real-time occupancy or flow visibility between buildings.

6. Supply Chain & Logistics Optimization

Use Case: Logistics operators plan efficient routes and predict hub congestion using real-time density data.

Key Features

• Real-time route planning with crowd avoidance

• Hub capacity forecasting

• Driver safety optimization

• Integration with transport management systems

Target Customers

• Logistics companies and freight forwarders

• Port and terminal operators

• Distribution center managers

• Last-mile delivery networks

Current Gap: Current routing uses static traffic models; no integration with real-time population density.

7. Insurance Risk Assessment

Use Case: Insurers adjust pricing and assess risk based on real-time area density patterns and movement data.

Key Features

• Real-time risk scoring for policies

• Anomaly detection against historical data

• Event-based claims prediction

• Integration with driver telematics

Target Customers

• Auto insurance companies

• General liability insurers

• Event liability specialists

• Business interruption insurers

Current Gap: Current models use historical data; no real-time risk assessment based on population concentration exists.

8. Real Estate & Neighborhood Analytics

Use Case: Property developers understand actual resident usage patterns and amenity utilization.

Key Features

• Amenity usage analytics

• Safety monitoring for underutilized areas

• Resident movement patterns

• Historical trend analysis

Target Customers

• Real estate developers

• Property management companies

• City planners

• Real estate investment firms

Current Gap: Developers lack insight into actual resident movement and amenity adoption patterns.

9. Disaster Response & Crisis Management

Use Case: Emergency agencies monitor population movement and coordinate multi-agency response during disasters.

Key Features

• Real-time evacuation monitoring

• Resource routing to population concentrations

• Shelter capacity forecasting

• Multi-agency coordination platform

Target Customers

• National disaster response agencies

• Emergency management departments

• Humanitarian organizations

• NGOs and crisis relief organizations

Current Gap: Emergency managers rely on outdated data; no real-time visibility into population movement during crises.

10. Tourism & Visitor Experience

Use Case: Tourism destinations monitor visitor flow and prevent overcrowding at attractions.

Key Features

• Real-time attraction crowding monitoring

• Queue management and wait time estimation

• Dynamic visitor recommendations

• Historical visitor pattern analysis

Target Customers

• National tourism boards

• Destination management organizations

• UNESCO World Heritage sites

• National parks and protected areas

Current Gap: Destinations identify overcrowding after incidents occur; no preventive monitoring exists.

11. Public Health Surveillance

Use Case: Health agencies monitor population concentration during outbreaks or vaccination campaigns.

Key Features

• Outbreak hotspot identification

• Vaccination site optimization

• Hospital surge capacity planning

• Movement restriction monitoring

• HIPAA/GDPR-compliant data aggregation

Target Customers

• Public health agencies and ministries

• Disease control centers

• Hospital networks

• Healthcare research organizations

Current Gap: Health agencies lack real-time visibility into population concentration; planning is based on historical models.

12. Financial Services Risk Management

Use Case: Financial institutions assess collateral risk and branch security based on neighborhood activity patterns.

Key Features

• Property risk scoring dashboard

• Branch activity monitoring

• Loan portfolio risk assessment

• Asset security planning

Target Customers

• Banks and financial institutions

• Asset management companies

• Real estate investment firms

• Corporate risk management departments

Current Gap: Lenders rely on demographic models; no real-time data on physical asset security and area risk exists.

13. EV Charging & EV Drivers — Localization and Matching

Use Case: EV Charging Networks CPMS, planning routes, predict congestions, calendars and reservations of stations, balancing charger networks and drivers, using real-time density data, backup method for different operations.

Key Features

• Real-time route planning
• Chargers capacity forecasting
• Driver safety optimization
• Integration with transport management systems
• Integration with energy grids and chargers networks
• Backup APIs methods to complete existing methods

Target Customers

• EV Chargers CPMS Systems
• CPOs
• Roaming Hubs
• EV Drivers

Current Gap: No integration with real-time population density and other SIM based telecom Network APIs live data and features, applied to EV Chargers and EV Drivers mobile apps

Implementation Approaches

Phase 1: Foundation

• Stabilize CAMARA SDK integration

• Build core platform infrastructure

• Develop horizontal dashboard framework

Phase 2: Vertical Development

• Develop industry-specific features

• Build partnerships with stakeholders

• Launch pilot deployments

Phase 3: Commercialization

• Transition pilots to production

• Scale operations and support

• Develop go-to-market strategies

Phase 4: Expansion

• Profitability in lead verticals

• Expand to secondary markets

• Build ecosystem integrations

Key Success Factors

1. Regulatory Compliance — Navigate telecom regulations and data privacy laws

2. Operator Partnerships — Secure agreements with major telecom providers

3. Data Privacy — Implement strict anonymization and aggregation protocols

4. **Reliability **— Maintain high availability for production deployments

5. Integration Expertise — Deep knowledge of enterprise systems

6. Customer Support — 24/7 support for mission-critical applications

Risk Considerations

Regulatory Risks

• Telecom regulations vary by country/region

• Data privacy compliance (GDPR, HIPAA, etc.)

• Lawful intercept and location request policies

Competitive Risks

• New market entrants with similar capabilities

• Existing analytics platforms expanding features

• Telecom operators developing proprietary solutions

Technical Risks

• API availability and reliability dependencies

• Data quality and accuracy limitations

• Performance under high load scenarios

• Different distributed systems failures

Market Risks

• Adoption speed varies by vertical

• Customer acquisition costs may be higher than expected

• Integration complexity with legacy systems

Conclusions

The CAMARA Emergency Response Demo, with the related APIs, Libraries, SDKs, MCP Server, provide a foundation for multiple applications, across diverse industries and domains. Each use case solves specific problems by providing real-time, network-based visibility into population density, location, and movement patterns — capabilities that do not exist in other current solutions, along with usage of innovative AI protocols, as MCP.

Success depends on identifying the highest-value customer segments in each vertical, building robust partnerships with telecom operators and integrators, innovative usage implementations, and maintaining strict compliance with data privacy regulations.