A Lightweight Model-Based Evolutionary Consensus Protocol in Blockchain as a Service for IoT
Yao Zhao, Youyang Qu, Yong Xiang, Yushu Zhang, Longxiang Gao
Abstract
Internet of Things (IoT) is experiencing fast proliferation with emerging trends in autonomy and local decision-making to avoid the explosive burden on network infrastructure between cloud and edge. Thereby, blockchain as a Service (BaaS) for IoT, as an emerging distributed services computing paradigm, has drawn intense attention due to its decentralization, auditability, and tamper-resistance. However, the primary challenge is to design a tailor-made consensus protocol that is applicable to BaaS for IoT. Existing consensus protocols generally focus on power-intensive environments, which is not feasible for power-constrained BaaS-enabled IoT systems. In this article, to fully exploit BaaS's superiority (e.g., to sharing data securely), we propose a lightweight model-based evolutionary consensus protocol called Proof of Evolutionary Model (PoEM) that can improve the quality of BaaS in IoT environments. Beyond existing rule-based consensus protocols, PoEM iteratively trains a machine learning model to achieve consensus. In this way, PoEM enhances consensus efficiency and enables low-performance IoT devices to be involved. Moreover, considering IoT environments’ dynamics, a novel mechanism is designed to manage nodes joining and exiting dynamically. Extensive analytical and experimental results show PoEM's improved consensus efficiency and applicability in dynamic BaaS-based IoT environments while providing high-level security guarantees.