ICCCNet-2025 convened at Manchester Metropolitan University from August 1–3, 2025, bringing together academia, industry, and government to advance computing and communication networks. Prestigious best paper awards highlighted research focused on intelligent, sustainable, and equitable technologies with direct real-world applicability. A hierarchical AI agent system using LLM-driven strategic planning demonstrated citywide orchestration of traffic flow, reducing gridlock and lowering energy consumption. Federated learning approaches guided by explainable LLMs addressed privacy-preserving multi-agent systems for IoT-based healthcare, targeting data security challenges. Award selections emphasized translating cutting-edge research into practical solutions for urban planning, healthcare privacy, and environmental sustainability.
The 5th International Conference on Computing and Communication Networks (ICCCNet-2025) concluded on a high note at Manchester Metropolitan University, solidifying its reputation as a premier platform for global innovation. From August 1-3, 2025, the conference became a crucible for ideas, bringing together brilliant minds from academia, industry, and government to forge the future of technology. The prestigious best paper awards, announced at the close of the event, weren't just accolades; they were a roadmap to a more intelligent, sustainable, and equitable world.
Their research is a bold leap forward in urban planning, proposing a multi-layered AI system that doesn't just react to traffic; it anticipates and manages it. By leveraging the strategic reasoning capabilities of Large Language Models (LLMs), their system can orchestrate traffic flow across an entire city, leading to significant reductions in gridlock and, crucially, a sharp decrease in energy consumption. This work offers a powerful, sustainable model for cities
leading to significant reductions in gridlock and, crucially, a sharp decrease in energy consumption. This work offers a powerful, sustainable model for cities grappling with population growth and environmental demands. In healthcare, a critical field where data security is paramount, Prathap Raghavan, Rajesh Sura, Amit Taneja, and Ankur Tiwari were recognized for their groundbreaking work on "Federated Learning for Privacy-Preserving Multi-Agent Systems in IoT-Based Healthcare, Guided by Explainable LLMs." Their solution tackles one of the biggest hurdl
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