Public defence, Networking Technology, MSc Oussama Bekkouche

Title of the thesis: Mobile Edge Computing for Next-Generation UAV Services and Operations

Thesis defender: Oussama Bekkouche
Opponent: Prof. Dusit Niyato, Nanyang Technological University, Singapore
Custos: Prof. Yu Xiao, Aalto University School of Electrical Engineering  

Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used in applications such as package delivery, infrastructure inspection, agriculture, and emergency response. As their use expands, drones are expected to operate beyond the visual line of sight of their pilots. Such operations require reliable communication and effective airspace coordination to ensure safety.

UAV Traffic Management (UTM) systems are being developed to coordinate drone operations and support their safe integration into shared airspace. However, their successful implementation also depends on communication networks and computing services that can respond quickly and remain available throughout a flight. Even small communication delays can affect the precision and stability of remotely controlled drones.

Mobile edge computing (MEC) offers a promising solution by bringing computing power closer to drones. Instead of sending data to distant cloud data centres, MEC processes it on servers located near mobile network base stations. This shorter communication path reduces delays, allowing drones to receive faster responses during flight.

In his doctoral thesis at Aalto University, Oussama Bekkouche investigated how MEC can support safe and efficient drone operations. The research examined how communication delays and network reliability affect remote drone control and whether MEC can improve flight stability. It also investigated the challenges of managing limited computing resources and maintaining services as drones move across network areas.

The thesis developed frameworks for integrating MEC with UTM systems and proposed methods for planning drone flight paths based on available computing resources. It also introduced optimization strategies for maintaining computing services as drones move between network areas.

The results demonstrate that MEC can improve the responsiveness and reliability of drone control. However, these benefits depend on effective management of computing resources and service continuity. The proposed solutions show how coordinating flight planning and computing resources can improve the reliability and efficiency of drone operations.

The findings contribute to research on mobile networks, edge computing, and drone traffic management. They can support the development of future drone services requiring reliable, low-delay communication. The thesis concludes that integrating MEC with UTM systems is a promising approach to enabling safer and more widespread drone operations.

Key words: Unmanned Aerial Vehicles (UAVs), Mobile Edge Computing (MEC), Flight Planning, Cellular Networks, Optimization.

Thesis available for public display 7 days prior to the defence at Aalto University's public display page.