Yining Hu

Health Internet of Things (Health IoT) systems continuously stream sensitive physiological data, making data access governance safety-critical under conflicting objectives such as privacy risk, latency, energy/resource cost, and robustness, especially when conditions change during emergencies. This paper proposes FiB-MOBA-EAFG, a hybrid blockchain–AI framework that separates on-chain accountability from off-chain decision intelligence. Off-chain, fuzzy context inference parameterizes scenario priorities, Pareto-based multi-objective search generates candidate governance policies, an emergency-aware feasibility guard filters unsafe trade-offs, and a bargaining-based selector chooses a single deployable policy. On chain, the blockchain layer records consent commitments, access events, and hashes of the selected policy and decision trace, serving as an immutable audit and accountability substrate rather than an online decision or optimization engine, while raw health data remain off-chain. Using simulation studies of home remote monitoring, clinic telehealth, and emergency triage under stochastic network variation and adversarial device behavior, FiB-MOBA-EAFG improves robustness and yields more repeatable policy selection than rule-based control and scalarized baselines within the evaluated simulation scenarios, while maintaining latency within ranges compatible with modeled edge deployment constraints through explicit emergency-aware feasibility constraints. A budget-matched random-search ablation further indicates that structured Pareto exploration is needed to reliably obtain robust, low-risk governance policies.

The Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited computing power and storage, making it difficult to implement robust security and manage large volumes of data. Existing studies have explored integrating blockchain and Deep Neural Networks (DNNs) to address security, storage, and data dissemination in IoT networks, but they often fail to fully leverage the mutual enhancement between them. This paper proposes DeepChainIoT, a blockchain–DNN integrated framework designed to address centralization, latency, throughput, storage, and privacy challenges in generic IoT networks. It integrates smart contracts with a Long Short-Term Memory (LSTM) autoencoder for anomaly detection and secure transaction encoding, along with an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism featuring transaction prioritization and node rating. On a public pump sensor dataset, our LSTM autoencoder achieved 99.6% accuracy, 100% recall, 97.95% precision, and a 98.97% F1-score, demonstrating balanced performance, along with a 23.9× compression ratio. Overall, DeepChainIoT enhances IoT security, reduces latency, improves throughput, and optimizes storage while opening new directions for research in trustworthy computing.

The Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited computing power and storage, making it difficult to implement robust security and manage large volumes of data. Existing studies have explored integrating blockchain and Deep Neural Networks (DNNs) to address security, storage, and data dissemination in IoT networks, but they often fail to fully leverage the mutual enhancement between them. We propose DeepChainIoT, a blockchain–DNN integrated framework designed to address centralization, latency, throughput, storage, and privacy challenges in generic IoT networks. It integrates smart contracts with a Long Short-Term Memory (LSTM) autoencoder for anomaly detection and secure transaction encoding, along with an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism featuring transaction prioritization and node rating. On a public pump sensor dataset, our LSTM autoencoder achieved 99.6\% accuracy, 100% recall, 97.95% precision, and a 98.97% F1-score, demonstrating balanced performance, along with a 23.9× compression ratio. Overall, DeepChainIoT enhances IoT security, reduces latency, improves throughput, and optimizes storage, while opening new directions for research in trustworthy computing.

Digital banking as an essential service can be hard to access in remote, rural regions where the network connectivity is unavailable or intermittent. Payment operators like Visa and Mastercard often face difficulties reaching these remote, rural areas. Although micro-banking has been made possible by Short Message Service (SMS) or Unstructured Supplementary Service Data (USSD) messages in some places, their security flaws and session-based nature prevent them from a wider adoption. Global-level cryptocurrencies enable low-cost, secure and pervasive money transferring among distributed peers, but are still limited in their ability to reach people in remote communities. We propose a blockchain-based digital payment scheme that can deliver reliable services on top of unreliable networks in remote regions. We focus on a scenario where a community-run base station provides reliable local network connectivity while intermittently connects to the broader Internet. We take advantage of the distributed verification guarantees of Blockchain technology for financial transaction verification and leverage smart contracts for secure service management. In the proposed system, payment operators deploy multiple proxy nodes that are intermittently connected to remote communities where local blockchain networks, such as Ethereum are composed of miners, vendors and regular users. Through probabilistic modelling, we devise design parameters for the blockchain network to realise robust operation over the top of unreliable network. Furthermore, we show that transaction processing time will not be significantly impacted due to network unreliability through extensive emulations on a private Ethereum network. Finally, we demonstrate the practical feasibility of the proposed system by developing NFC (Near Field Communication) enabled payment gateways on Raspberry-Pis, a mobile wallet application and mining nodes on off-the-shelf computers.

Cash-less payment via a variety of credit, debit or prepaid cards is pervasive in our interconnected society, but not so ubiquitous in remote rural regions where network connectivity is intermittent. We proposed a cash-less payment scheme for remote villages based on blockchains that allow maintaining a record of verifiable transactions in a distributed manner. We overcome the limitations of intermittent network connectivity by solely relying on blockchain mining nodes in the village for transaction processing and verification. The bank joins as a peer and monitors node behaviors, rewards miners and processes currency exchanges whenever the connectivity is available. We take advantage of the Ethereum network to develop our solution and demonstrate the feasibility of the proposed system on off-the-shelf computing devices. We emulate a remote village scenario with intermittent network connectivity and show the robustness and reliability of the proposed system.

A method is proposed for 3-D reconstruction of coronary from a limited number of projections in rotational angiography. A Bayesian maximum a posteriori (MAP) estimation is applied with a Poisson distributed projection to reconstruct the 3D coronary tree at a given instant of the cardiac cycle. Several regularizers are investigated L0-norm, L1 and L2 -norm in order to take into account the sparsity of the data. Evaluations are reported on simulated data obtained from a 3D dynamic sequence acquired on a 64-slice GE LightSpeed CT scan. A performance study is conducted to evaluate the quality of the reconstruction of the structures.