— Enhancing Post-Earthquake Communication Resilience

Communication resilience when it matters most!

Integrating unmanned aerial vehicles (UAVs), long-range LoRa communications, and privacy-conscious cloud and edge systems to support emergency response when terrestrial networks are disrupted.

Explore the Framework

Our Research Objectives

05

Core research objectives

03

Layers in the proposed hybrid communication framework

06

UAVs modelled in the reported simulation

340

Drone images described in existing project reporting

— 01 / Project Overview

Keeping people connected after an earthquake.

Earthquakes can damage cellular towers, fibre-optic links and power supplies in an instant, leaving first responders and affected communities without reliable ways to communicate. Even infrastructure that remains operational can face severe congestion when communication is most urgently needed.

Post-Earthquake Ccommunication Resilience proposes a resilient communication framework combining UAV-based aerial coverage, low-power LoRa links, and secure cloud/edge processing. The research seeks to enable timely information exchange, strengthen situational awareness, and support coordinated emergency decisions while protecting personal data.

Why conventional communications struggle

Infrastructure damage

Ground networks, electricity and backhaul links can become unavailable or unreliable.

Limited access and coverage

Blocked roads, remote communities and difficult terrain complicate restoration efforts.

Rising information demand

Emergency messages, sensors, maps and video streams require different levels of bandwidth and priority.

Security and privacy risks

Location and identity information need appropriate safeguards throughout the response process.

Earthquake Casualties & Communication Impact

Recent earthquakes where communication infrastructure collapsed. Türkiye’s February 2023 events caused roughly 52,000 deaths. Hover any bar for its figure. No casualty figure was recorded for Sulawesi in the project source.

UAV Mission Priority Distribution

Indicative split of message traffic across the four QoS priority levels used in the OMNET++ simulation. CRITICAL messages bypass standard traffic under congestion. Hover any segment for its share.

Supported through the British Council’s International Science Partnerships Fund (ISPF)

A collaborative research effort involving institutions in Türkiye, the United Kingdom and the wider research network.

RESEARCH PARTNERSHIP

— 02 / Proposed Solution

One resilient network. Three connected layers.

The project uses a hybrid approach linking available backhaul services, an aerial UAV relay network, and communication with devices and sensors on the ground.

LAYER 01 · NETWORK BACKHAUL

Backhaul connectivity

Satellite and available cellular infrastructure connect the field network to ground stations, control centres and emergency coordination systems.

Satellite

Cellular

Ground control

LAYER 02 · AERIAL NETWORK

UAV relay network

Deployable UAVs extend coverage, relay data through LoRa-based links, and support adaptive energy management, mobility and local processing.

UAV relays

LoRa

Edge logic

LAYER 03 · LAST MILE

People, responders & sensors

Wi-Fi-enabled devices and low-power IoT nodes exchange appropriate types of emergency messages, status updates and environmental data.

Wi-Fi

IoT sensors

Emergency updates

Hybrid network architecture

Satellite and available cellular infrastructure connect the field network to ground stations, control centres and emergency coordination systems.

Conceptual research framework

How technology, data governance and expected benefits relate.

— 03 / Research Objectives

Five research priorities, one shared goal.

The research combines reliable communications, energy-aware operation, network interoperability, privacy-aware data management and field-based validation.

Ensure critical information exchange

Study UAV-supported LoRa communication pathways for emergency messages, IoT data, responders and affected communities. Explore UAV-based edge inference and mobile communication interfaces.

EMERGENCY MESSAGING

UAV RELAY

EDGE AI

Extend network lifetime

Investigate energy-aware UAV operations, LoRa node clustering, load balancing, sleep–wake schedules and adaptive deployment based on demand.

ADAPTIVE POWER

NODE CLUSTERING

DUTY CYCLING

Integrate heterogeneous networks

Examine switching and handover across available cellular, satellite, Wi-Fi and LoRa communications to improve reliability when parts of the infrastructure fail.

INTEROPERABILITY

HANDOVER

BACKHAUL

Apply privacy and security by design

Design data-handling practices aligned with GDPR principles, including minimisation, purpose limitation, access control, encryption, retention and accountable logging. Technical safeguards require validation before deployment.

GDPR PRINCIPLES

DATA MINIMISATION

AUDITABILITY

Develop and validate functional prototypes

Combine network simulation, UAV-based research, prototypes and scenario-based testing to evaluate communication performance, operational resilience and usability in disaster-response contexts.

OMNeT++

FIELD TESTING

PROTOTYPING

— 04 / Technology & Data Governance

Technology built around real disaster needs.

Each technology contributes a different capability. The proposed system combines them into a communication approach designed to remain useful under disruption.

UAVs and aerial networking

Temporary airborne relays expand coverage and support adaptable flight paths, positioning and network recovery.

LoRa and IoT sensing

Long-range, low-power links can connect sensors and other suitable devices where low-bandwidth connectivity is valuable.

Hybrid access and backhaul

Wi-Fi, cellular and satellite connectivity can complement low-power links for different message types and operational conditions.

Cloud and edge computing

Local processing can support time-sensitive analysis, while cloud platforms can assist coordination when suitable connections exist.

Prioritised communication

Quality-of-service designs aim to give urgent rescue messages priority over routine traffic during network congestion.

Simulation and validation

Network models, UAV imagery and technical testing help research teams examine performance before broader field use.

Privacy should be built in from the start.

The research proposes GDPR-aligned data management rather than assuming compliance has already been certified. Production security and legal compliance must be assessed against the actual deployment.

✓ Purpose limitation & data minimisation

✓ Role-based access & accountability

✓ Appropriate encryption & secure interfaces

✓ Defined retention & deletion practices

— 05 / Research Work & Evidence

From modelling to practical investigation.

There are two technical research strands. They are presented separately here from the framework’s broader intended capabilities.

REPORTED SIMULATION WORK

UAV communication network modelling

OMNeT++-based exploration of traffic prioritisation, UAV topology, message reliability and network stress.

6 UAVs

Modelled in a star-type simulation

34,000

Messages in reported stress scenario

Normal scenario described as approximately 7,500 messages.
Priority levels: Low, Routine, High and Critical.
Stop-and-Wait ARQ and acknowledgements discussed as reliability mechanisms.
Simulation-specific settings should not be presented as LoRa physical-layer performance.

REPORTED DATA COLLECTION

UAV-based road condition assessment

Drone imagery described for pavement-crack and post-disaster infrastructure assessment research.

85

Data collection points described

340

High-resolution images reported

Images reportedly captured at four heights: 3m, 5m, 7m and 9m.
GPS information used to support location-aware mapping.
Dataset preparation addresses shadows, puddles and other visual interference.
Proposed edge deployment aims to accelerate damage assessment.

Simulation parameters

Technical details of the project.

SIMULATOR

OMNeT++

TOPOLOGY

Centralized star, up to 6 UAVs

MODEL LINK RATE

1 Mbps*

BIT ERROR RATE

1 × 10⁻⁶*

RELIABILITY

Stop-and-Wait ARQ

MESSAGE CLASSES

Low · Routine · High · Critical

BASE SCENARIO

7,500 messages

STRESS SCENARIO

34,000 messages

* The model’s 1 Mbps rate must not be interpreted as a measured LoRaWAN data rate. Existing XOR-based simulation encryption references are not evidence of production-grade encryption or GDPR compliance.

— 06 / OUTCOMEs and Impact

Better connections. Better coordination. Greater resilience.

These are the intended benefits of the research framework; their level of achievement will depend on prototype development, testing and field validation.

Connectivity Recovery

Restore useful communication pathways where terrestrial infrastructure is damaged.

Interoperability

Bridge suitable networks and devices to improve information exchange.

Faster Response

Improve the delivery of emergency information and situational updates.

Data Security

Support responsible treatment of sensitive identities and location information.

Community Resilience

Help people, responders and agencies stay better connected in crises.

— 07 / Research Activities & Knowledge Exchange

Collaboration beyond the laboratory.

The project combines capacity building, partnership development, research dissemination and prototype testing.

01 · CAPACITY BUILDING

Training workshops

Technical learning sessions in IoT, AI, UAV interactions and related research methods.

02 · JOINT RESEARCH

Collaborative research groups

Interdisciplinary collaboration between academic partners on communication resilience challenges.

03 · KNOWLEDGE EXCHANGE

Industry–academia networking

Events connecting researchers and technical practitioners around disaster communications.

04 · DISSEMINATION

Public engagement

Seminars, public lectures, and outreach activities for wider research and policy audiences.

05 · TECHNICAL VALIDATION

Implementation and testing

Development of functional prototypes and investigations of real-world deployment scenarios.

06 · CONTINUOUS LEARNING

Monitoring and feedback

Review processes to inform iteration, usability and longer-term technical improvements.

Research outputs & publications

Find related research papers and knowledge products in the Hub’s publication collection.

Explore Publications

— 08 / Research Consortium

Connecting expertise across institutions.

Institutional Partners & Collaborators in Türkiye and the United Kingdom.

INSTITUTIONS

RESEARCH TEAM

Prof. Altan Koçyiğit

Principal Investigator – Turkey
METU-NCC, Northern Cyprus

Prof. Enver Ever

Co Principal Investigator – Turkey
METU-NCC, Northern Cyprus

Dr. Muhammad Toaha Raza Khan

Co Principal Investigator – Turkey
METU-NCC, Northern Cyprus

Prof. Adnan Yazici

Co Principal Investigator – Turkey
Nazarbayev University

Dr. Kamran Ali

Principal Investigator – UK
Middlesex University, London

Dr. Anum Tanveer

Co Principal Investigator – UK
Middlesex University, London

Dr. Syed Sardar Muhammad

Co Principal Investigator – UK
Brunel University, London

Dr. Masood Ur-Rehman

Co Principal Investigator – UK
University of Glasgow, London

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