A novel federated learning approach for routing optimisation in opportunistic IoT networks

Opportunistic IoT networks are the type of wireless network that operate in challenging and dynamic environments where traditional network infrastructure is unreliable, limited, or non-existent. Due to these unreliable network conditions, traditional routing algorithms can not be applied to them. Further, in today's interconnected world, where a vast amount of personal and sensitive information is transmitted over networks, it is important to address the growing concerns over the privacy and security of users' data in communication networks. To mitigate this, a novel federated learning approach for routing optimisation in opportunistic IoT networks is proposed, where nodes opportunistically select the next-hop relay for message forwarding based on the current network state and local knowledge. Extensive simulation and analysis showcase the effectiveness and practicality of the proposed FLRouter in achieving efficient and privacy-aware routing within opportunistic IoT networks. The proposed approach outperforms existing methods in delivery probability, with gains of up to 16% and 13% as buffer size increases. Additionally, it demonstrates lower overhead ratios, with reductions of up to 42% and 34% compared to existing approaches.