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The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as "BDSDT" is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets. [ABSTRACT FROM AUTHOR]

In this paper, a new distributed multi-agent framework based on the three layers' fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs), and microturbines (MT) are considered in the proposed technique. To overcome the complexity of the problem, by using advantages of fog computing, a new fast consensus-based optimization algorithm is used, which is modified based on the fuzzy adaptive leader technique. Finally, the proposed technique is simulated and tested on microgrids with 6 and 14 buses, respectively. Simulation results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate. • We developed a three-layer framework based on fog computing for MG. • We developed a modified distributed consensus-based technique to achieve the optimal dispatch solutions. • We simulated and analyzed the proposed model on two IEEE standard test system with two different scenarios. [ABSTRACT FROM AUTHOR]