As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing the trustworthiness of IoT-based services. These architectural limitations hinder the IoT’s Sensor-as-a-Service (SEaaS) model, which enables the commercial transmission of sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into the IoT architectural design, specifically enhancing SEaaS efficiency. This research contributes to an optimized IoT architecture with decentralized blockchain operations and simplified public key encryption. Furthermore, this study introduces an advanced SEaaS model featuring innovative trading operations for sensed data among diverse stakeholders. At its core, this model presents a unique blockchain-based data-sharing mechanism that manages multiple aspects, from enrollment to validation. Evaluations conducted in a standard Python environment indicate that the proposed SEaaS model outperforms existing blockchain-based data-sharing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, and a 25% reduction in algorithm processing time. Ultimately, integrating a lightweight authentication mechanism using simplified public key cryptography within the blockchain establishes the model’s potential for efficient and secure data-sharing in IoT.

With the rapid growth of China’s new energy vehicle industry, the quality of crimped wire connectors directly impacts the performance of wiring harnesses, which are critical to the overall vehicle quality. At present, reliable methods for inspecting crimped wire connectors are still primarily based on image recognition evaluations. To address this, we propose an Improved Lightweight Network based on YOLOv8 (ILN-YOLOv8) to achieve high-precision and high-efficiency detection of crimped wire connectors. Taking the original YOLOv8 model as a baseline, the new model enhances the ability to extract shallow features from small targets by increasing the P2 detection layer and improving the Feature Pyramid Network(FPN) and Path Aggregation Network(PAN) structures. Next, the improved Selective Boundary Aggregation(SBA) module replaces the Concat module in the Neck, enhancing the fusion of deep and shallow features. Additionally, the Efficient Local Attention(ELA) attention mechanism is introduced into the Cryptographic Service Provider(CSP) bottleneck with 2 convolutions(C2F) module in the Backbone, improving feature localization accuracy without increasing network complexity. The Minimum Point Distance based IoU(MPDIoU) loss function is used to calculate localization loss, improving detection accuracy while preventing gradient explosion. Finally, lightweighting of the ILN-YOLOv8 model is achieved using the slim-neck network, the backbone with Depthwise Separable Convolution (DWConv), and Lightweight Convolution (LightConv) modules. After pruning and knowledge distillation, the model’s complexity and computational load significantly decreased while accuracy improved, meeting the industry’s requirements for crimped wire connectors detection and achieves superior performance. Experimental results show that, compared to the original YOLOv8 model, the proposed method achieved 96.2% accuracy on a real-world crimped wire connectors dataset, with mAP@0.5 and mAP@.5:.95 improving by 6.1% and 7.4%, respectively, while Parameters and FLOPs decreased by 66.7% and 34.6%.

Multi-party quantum key agreement (MQKA) enables a group of mutually untrusted users to jointly establish a secret key with equal influence, a capability not provided by two-party quantum key distribution (QKD) and increasingly relevant for securing resilient infrastructure such as quantum-enabled smart grid networks and distributed cyber-physical systems. We present a high-dimensional Greenberger-Horne-Zeilinger (GHZ)-based MQKA that simultaneously targets rigorous security, provable fairness, and practical loss tolerance. The protocol distributes an N -partite d -level GHZ state in a star (with an optional star-of-stars hierarchy for large N ) and maps each user’s contribution to local $X_{d}^{a} Z_{d}^{b}$ X d a Z d b operations. A commit-then-reveal layer makes inputs binding and hiding, yielding equal influence fairness over $\mathbb{Z}_{d}$ Z d with a strict fair-abort guarantee. Composable secrecy is established via entropy bounds and a Devetak-Winter-style key rate analysis, strengthened by test-round statistics; we connect Mermin/Svetlichny-type correlators to min-entropy lower bounds and incorporate full leakage accounting and finite-key penalties. We model depolarizing, shift/phase (Weyl), amplitude-damping, erasures, detector efficiency, and dark counts, and derive closed-form mappings from device parameters to symbol/phase errors. Monte Carlo studies confirm that $d>2$ d > 2 expands the positive-rate region and reduces the global survival required for a given throughput compared with qubit GHZ MQKA under identical conditions. We discuss potential photonic realizations (time-bin or frequency-bin encoding, discrete Fourier transform networks, and superconducting nanowire detectors) and architectural pathways toward larger N via hierarchical clustering, while acknowledging the significant technological challenges that would need to be overcome to realize high-dimensional multipartite entanglement experimentally. Overall, the scheme delivers higher efficiency per entangled state, rigorous fairness, and composable security with realistic noise and loss, addressing key gaps in current MQKA designs for resilient infrastructure.