Blockchain-Based Privacy-Aware Contextual Online Learning for Collaborative Edge-Cloud-Enabled Nursing System in Internet of Things

Abstract

With the rapid growth of Internet of Things (IoT), smart home develops rapidly in these years, which could assist people who need family medical support. It could integrate health care with ambient assisted living (AAL) technologies and provide activities of daily life (ADLs) to the people who need care. This paper proposes a smart home and cross-cloud-and-edge computing based nursing system (NS). In general, a good NS requires low latency, high stability, and the real-time analysis and response, where the conventional centralized cloud computing based approaches cannot meet those requirements very well. To this end, we introduce a novel distributed joint edge-cloud structure to better satisfy these requirements. Moreover, to deal with the security and privacy issues, we introduce the blockchain to verify the identity of data exchanging and differential-privacy (DP) in the NS to protect the healthcare takers’ data privacy. In a word, we propose a privacy-preserving context-aware multi-armed bandit based online learning approach for edge-cloud-enabled NS via blockchain in IoTs. Additionally, our system with a novel top-down expanding tree based structure can support dynamically increasing health care datasets. Extensive experimental and numerical results demonstrate our solution can achieve accurate recommendation results with sublinear regret performance.