publications

Flying Ad Hoc Networks (FANETs) provide connectivity in extreme environments such as battle zones and disaster areas. Since these networks operate without infrastructure, they must calculate routes on the fly. Unfortunately, as each e2e link selfishly chooses the shortest path for its flow, they also tend to overwhelm geographically central nodes, making these nodes the bottleneck for all flows. On top of this, these networks are quite sensitive to jamming attacks, which can shut down the network’s operation entirely. In this paper, we extend our previous work CentAir with an additional operation state that enables rapid communication by reducing interframe spaces of nodes according to their eigenvector centrality measure. Following this, we propose an intelligent heuristic to enable hybrid operation among these states. This hybrid operation state improves e2e latency by modifying interframe spacing regarding the node’s eigenvector centrality. Our exhaustive simulation results show that Hybrid CentAir achieves an 18.1 percent more throughput, a 21.1 percent lower latency while being 17.8 percent more energy efficient compared to multi-channel OLSR. Additionally, our protocol shows an 11.5 percent better packet delivery ratio against randomly placed deceptive jammers that continuously transmit false packets in the area.

Wildfires have become a global problem in recent years. Authorities are looking for various technological and auxiliary solutions to deal with this environmental crisis. One of the advances being utilized in the forest fire field and its aftermath is unmanned aerial vehicles (UAVs). UAVs play a fundamental role in wildfire-fighting solutions due to their ease of use and high accessibility. However, the energy constraints of a single UAV and the fire areas make monitoring challenging. Therefore, to address these issues, we propose a monitoring application called Phoenix. We make three main contributions with the Phoenix application. Firstly, we implement a monitoring application consisting of path planning, graph engine, and modified TSP algorithms to help the UAV’s fire tracking and shorten its route. Secondly, we develop a network architecture to transfer the tracking data we obtained to provide information to the fire brigade and other firefighting units. Thirdly, we provide energy optimization for a single UAV mission. The first part of the application uses the elliptical fire model and simulation. In addition, Phoenix utilizes fuel moisture content (fmc) data of the fire zone to analyze the critical fire regions. The simulation results show that our Phoenix application reduces energy consumption by 38 % and enhances coverage by up to 51%.

With the proliferation of affordable VTOL drones, application domain of aerial swarms expands everyday, spanning from public drone art to offensive military operations. Orchestrating these systems towards a successful mission, especially in contested environments, requires strict synchronization and reliable communication among network entities. Although using a ground controller with a long range link provides a cost-effective solution, it is extremely vulnerable to single-point of failure and/or jamming attacks. Thus, in contested environments, adapting ad-hoc policies is one of the most promising approaches, mitigating these vulnerabilities. However, existing ad-hoc approaches fail to sustain multi-hop connections, suffering from frequent topology changes and intermittent links due to dynamic environment. In this paper, we propose self-organizing TDMA (STDMA) protocol for aerial swarms in order to ensure reliable communication through efficient network management. We also partially implement our protocol on a small-scale test-bed and show that how drones are able to self-configure upon joining the network without any prior information. Our proposed STDMA protocol improves the overall channel utilization up to 55 percent with less control traffic overhead compared to the existing studies.