Neural Nectar: A Slime-inspired Boost

RUDNImage Source: RUDNU


RUDN University mathematicians, alongside Chinese and Saudi Arabian counterparts, harnessed the wisdom of slime mold to enhance the energy efficiency of drone router-based wireless networks. Their neural network model, influenced by slime mold behavior, optimized resource distribution, tackling the challenge of power limitations in drone networks. This innovation, combining deep learning and nature-inspired algorithms, achieved 5–20% superior computational and energy efficiency in transmitting data.


In the pursuit of advancing wireless communication, a collaboration between mathematicians from RUDN University, China, and Saudi Arabia has birthed an innovative solution. Focused on the energy efficiency of drone router-based wireless networks, the team turned to an unexpected source of inspiration – slime mold. The goal was to overcome the inherent challenge of resource distribution in drone networks, particularly the need for optimal power allocation.

The application of drone routers in scenarios demanding swift and widespread signal coverage, such as natural disasters or public events, presents a crucial need for efficient resource utilization. The Achilles heel, however, lies in the limited power capacity of the drones due to size and weight constraints. Dr. Ammar Muthanna, the Ph.D. Director of the Scientific Center for Modeling Wireless 5G Networks at RUDN University, emphasizes the potential of unmanned aerial vehicles (UAVs) like drones for ubiquitous network access.

The breakthrough came in the form of a neural network model intricately designed using a combination of deep learning models and the "slime mold" method. Drawing inspiration from the behavior of this single-celled organism in search of food, the algorithm paved the way for optimized parameters in the neural network. Much like the evaporating trail left by the slime mold, the model aimed to discover the path leading to maximum neural network efficiency.

Results published in Sensors reveal the success of this slime-inspired approach. The neural network, guided by the slime mold's optimization, showcased remarkable computational and energy efficiency. In fact, the new model demonstrated a 5–20% improvement over its predecessors in terms of the number of bits transmitted per joule.

Dr. Ammar Muthanna sees this approach as a stride toward making energy-efficient and computationally sound decisions. Looking ahead, the team is eager to explore additional avenues for resource distribution, adapting dynamically to real-time network conditions.


In conclusion, the collaboration between mathematicians from RUDN University, China, and Saudi Arabia has yielded a groundbreaking approach to enhance the energy efficiency of drone router-based wireless networks. By incorporating insights from the behavior of slime mold, the team crafted a neural network model that outperformed previous models by 5–20% in terms of computational and energy efficiency. Driven by the quest for energy-efficient decisions, the researchers anticipate further exploration of resource distribution methods, aiming to adapt seamlessly to evolving network conditions in the future.

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