Milena Radenkovic

Adam Graham, Milena Radenkovic

On the 28th of September, 2018, a tsunami hit the city of Palu in Indonesia, killing 4,340 people. The earthquake preceding the tsunami crippled communication lines and may have rendered the transmission of tsunami warning messages using traditional end-to-end approaches impossible. This paper proposes an alternative approach using Delay Tolerant Networks (DTNs) for tsunami warning message routing given their resilience to disruptions and sparse connections. Both Epidemic and Spray and Wait routing protocols were simulated in a pseudo-realistic environment to evaluate their effectiveness for transmitting tsunami warning messages in Palu. Results indicated that these protocols are not suitable for the tight time constraints of post-earthquake tsunami warnings with the currently available technology. However, they may have promising applications for the earthquakes that precede tsunamis.

Yihang Cao, Milena Radenkovic

Delay Tolerant Networks (DTNs) can provide emergency communication support when conventional infrastructure is disrupted during disasters. This paper evaluates the performance of opportunistic routing protocols in a realistic disaster scenario based on the 2016 Central Italy earthquake, modelled as an emergency occurring during a football match at Stadio Adriatico in Pescara. We identify multiple suitable groups of mobile and static nodes, such as audiences, a range of different emergency responders, stage sensors, and vehicles, to design and build evacuation and rescue activities in a partially connected environment. Two representative DTN routing protocols, Epidemic and Spray and Wait, are tested under identical simulation settings and compared using delivery probability, latency, overhead ratio, hop count and dropped messages. The results highlight that Spray and Wait provides a better balance between reliability and efficiency in this scenario, achieving higher delivery probability while reducing overhead and using network resources more efficiently. The study shows the usefulness of DTN simulation for analysing disaster communication performance in emergency response scenarios.

Xiaofei Liu, Milena Radenkovic

In the fields of disaster rescue and communication in extreme environments, Delay Tolerant Network (DTN) has become an important technology due to its "store-carry-forward" mechanism. Selecting the appropriate routing strategy is of crucial significance for improving the success rate of distress message transmission and reducing delays in material dispatch. We design a pseudo realistic use case of Nepal Kathmandu earthquake rescue based on dynamically changing population distribution model and characteristics of rescue activities in the initial rescue efforts in Nepal Kathmandu earthquakes to conducted the multi criteria two benchmark routing protocols performance analysis in the face of different buffer sizes of the rescue team nodes. We identify multiple real world node groups, including affected residents, rescue teams, drones and ground vehicles and communication models are established according to the movement behaviors of these groups. We analyze the communication of distress messages between edge nodes to obtain performance metrics such as delivered probability, average delay, hop count, and buffer time. By analyzing the multi layer complex data and protocols differences, the research results show the effectiveness of distributed DTN communication methods in the Nepal earthquake rescue use case, reveal existence of trade-offs between transmission reliability and resource utilization of different routing protocols in disaster communication environment and provide a basis for the design of next-generation emergency communication services based on edge nodes.

He Gao, Baoxiang Huang, Milena Radenkovic et al.

Synthetic Aperture Radar (SAR), with its all- weather and wide-area observation capabilities, serves as a crucial tool for wake detection. However, due to its complex imaging mechanism, wake features in SAR images often appear abstract and noisy, posing challenges for accurate annotation. In contrast, optical images provide more distinct visual cues, but models trained on optical data suffer from performance degradation when applied to SAR images due to domain shift. To address this cross-modal domain adaptation challenge, we propose a Similarity-Guided and Memory-Guided Domain Adap- tation (termed SimMemDA) framework for unsupervised domain adaptive ship wake detection via instance-level feature similarity filtering and feature memory guidance. Specifically, to alleviate the visual discrepancy between optical and SAR images, we first utilize WakeGAN to perform style transfer on optical images, generating pseudo-images close to the SAR style. Then, instance-level feature similarity filtering mechanism is designed to identify and prioritize source samples with target-like dis- tributions, minimizing negative transfer. Meanwhile, a Feature- Confidence Memory Bank combined with a K-nearest neighbor confidence-weighted fusion strategy is introduced to dynamically calibrate pseudo-labels in the target domain, improving the reliability and stability of pseudo-labels. Finally, the framework further enhances generalization through region-mixed training, strategically combining source annotations with calibrated tar- get pseudo-labels. Experimental results demonstrate that the proposed SimMemDA method can improve the accuracy and robustness of cross-modal ship wake detection tasks, validating the effectiveness and feasibility of the proposed method.

Li Kun, Milena Radenkovic

Currently, deep learning has been utilised to tackle several difficulties in our everyday lives. It not only exhibits progress in computer vision but also constitutes the foundation for several revolutionary technologies. Nonetheless, similar to all phenomena, the use of deep learning in diverse domains has produced a multifaceted interaction of advantages and disadvantages for human society. Deepfake technology has advanced, significantly impacting social life. However, developments in this technology can affect privacy, the reputations of prominent personalities, and national security via software development. It can produce indistinguishable counterfeit photographs and films, potentially impairing the functionality of facial recognition systems, so presenting a significant risk. The improper application of deepfake technology produces several detrimental effects on society. Face-swapping programs mislead users by altering persons' appearances or expressions to fulfil particular aims or to appropriate personal information. Deepfake technology permeates daily life through such techniques. Certain individuals endeavour to sabotage election campaigns or subvert prominent political figures by creating deceptive pictures to influence public perception, causing significant harm to a nation's political and economic structure.

Parth Patel, Milena Radenkovic

Space Communication poses challenges such as severe delays, hard-to-predict routes and communication disruptions. The Delay Tolerant Network architecture, having been specifically designed keeping such scenarios in mind, is suitable to address some challenges. The traditional DTN routing protocols fall short of delivering optimal performance, due to the inherent complexities of space communication. Researchers have aimed at using recent advancements in AI to mitigate some routing challenges [9]. We propose utilising a feedforward neural network to develop a novel protocol NeuraLunaDTNet, which enhances the efficiency of the PRoPHET routing protocol for lunar communication, by learning contact plans in dynamically changing spatio-temporal graph.