Guobin Xu’s research while affiliated with Morgan State University and other places

Randomness is integral to computer security, influencing fields such as cryptography and machine learning. In the context of cybersecurity, particularly for the Internet of Things (IoT), high levels of randomness are essential to secure cryptographic protocols. Quantum computing introduces significant risks to traditional encryption methods. To address these challenges, we propose investigating a quantum-safe solution for IoT-trusted computing. Specifically, we implement the first lightweight, practical integration of a quantum random number generator (QRNG) with a software-based trusted platform module (TPM) to create a deployable quantum trusted platform module (QTPM) prototype for IoT systems to improve cryptographic capabilities. The proposed quantum entropy as a service (QEaaS) framework further extends quantum entropy access to legacy and resource-constrained devices. Through the evaluation, we compare the performance of QRNG with traditional Pseudo-random Number Generators (PRNGs), demonstrating the effectiveness of the quantum TPM. Our paper highlights the transformative potential of integrating quantum technology to bolster IoT security.

Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has projected that by 2030, developing countries will expand their irrigated areas by 34%, while water consumption will only be up 14%. This discrepancy highlights the importance of accurately monitoring water flow and volume rather than people’s rough estimations. The smart irrigation systems, a key subsystem of smart agriculture known as the cyber–physical system (CPS) in the agriculture domain, automate the administration of water flow, volume, and timing via using cutting-edge technologies, especially the Internet of Things (IoT) technology, to solve the challenges. This study explores a comprehensive three-dimensional problem space to thoroughly analyze the IoT’s applications in irrigation systems. Our framework encompasses several critical domains in smart irrigation systems. These domains include soil science, sensor technology, communication protocols, data analysis techniques, and the practical implementations of automated irrigation systems, such as remote monitoring, autonomous operation, and intelligent decision-making processes. Finally, we discuss a few challenges and outline future research directions in this promising field.

The rise of deep learning and the Internet of Things (IoT) has driven a number of smart-world applications, which are mostly deployed in distributed environments. Federated learning, a privacy-preserving collaborative learning paradigm, has shown considerable potential to leverage the rich distributed data at network edges. Nonetheless, the heterogeneity of IoT devices and their connected network environment impedes federated learning applications in IoT systems. Particularly, the stale gradients updated by slower local learners impact the effectiveness of federated learning. Transmitting weight updates with a large number of users leads to network congestion at the edge of the network and incurs unaffordable communication costs. To overcome these challenges, we propose a transfer knowledge based federated learning framework under a resource-limited distributed system. We formulate a knowledge distillation based federated learning optimization problem with the consideration of dynamic local resource. The proposed approach carries out federated learning with the help of knowledge distillation to avoid occupying the expensive network bandwidth or bringing a heavy burden to the network. Theoretical analysis demonstrates convergence of the learning process. The experimental results on three public datasets illustrate that the proposed framework is capable of substantially improving the efficiency of federated learning and outperforming state-of-the-art schemes.