An AI–Blockchain‐Based Network Optimization Framework for Energy‐Efficient Computing Systems
Udit Mamodiya, Divyanshu Sinha, Indra Kishor, Amjad Ali Syed, Abhinandan Routray · IET Networks · 2026
ABSTRACT In modern computing and blockchain systems, with the growth of networks, classical consensus mechanisms, which tend to be hard and rule‐based, cannot dynamically adapt to changing workloads and, therefore, consume unnecessary energy and have uneven performance. The proposed AI–blockchain convergence framework is aimed at accomplishing energy‐efficient and self‐optimising computing networks based on reinforcement‐based consensus adaptation. The proposed model combines a multi‐agent reinforcement learning controller with blockchain telemetry which allows the real‐time adjustment of the block size, committee composition, and timeouts. Compared to the traditional consensus system, AIBLOCK adapts to both throughput and stability based on feedback of the live states on a network, inherently turning the blockchain into a self‐regulating digital organism. Large scale node deployments have been evaluated through experimentation and have a 34% lower energy use as well as a 22% increase in consensus stability when compared to HiCoOB and Layer‐2 baselines. The framework reported sublinear scaling of energy per node, and it converges quickly when subjected to dynamic loads in terms of transactions, which validate its adaptive efficiency. AIBLOCK, therefore, represents a transition to cognitive blockchain ecosystems that do not necessarily have to be manually tuned but learn instead.