Energy: reducing latency in IoT DLTs for AI-driven real-time solutions

Purpose Integrating Internet of Things (IoT) networks with distributed ledger technology (DLT) and artificial intelligence (AI) presents critical challenges, particularly related to latency, scalability, hardware constraints and data security. Efficient data ingestion and validation are essential to enable real-time AI processing. The main contribution of this paper is the proposal of the Energy consensus algorithm, designed to minimize both latency and energy consumption in such environments. Design/methodology/approach Energy is a consensus algorithm tailored for public directed acyclic graph-based DLTs in IoT contexts. It introduces a flexible transaction validation mechanism that reduces or bypasses Proof of Work requirements. The algorithm’s performance is experimentally compared with IOTA under varying payload conditions. Findings Results show that Energy significantly reduces latency and energy consumption, especially for small payloads, which are common in IoT applications. These findings demonstrate Energy’s ability to enhance transaction efficiency and support real-time AI model updates based on verified IoT data streams. Research limitations/implications Future work should investigate the scalability of Energy in larger and more heterogeneous IoT ecosystems, as well as its compatibility with different AI frameworks. Evaluating its performance under diverse network conditions and hardware setups would further strengthen the generalizability of the results. Practical implications The Energy algorithm enables continuous AI model updates while ensuring data integrity, traceability and low latency. Its adaptability makes it a suitable solution for large-scale IoT deployments requiring secure and efficient data processing. Originality/value This paper presents a novel consensus algorithm that bridges the requirements of IoT, DLT and AI, with a particular focus on improving latency and energy efficiency. Energy offers a robust approach for optimizing data flow and transaction processing in real-time, AI-driven IoT systems.