Internet of Things (IoT) devices are widely used and pervasive, commonly collecting daily life data and often involving personal information. The sensitivity of the data brings security concerns when handling it. Blockchain ensures decentralization and data security through immutable service records and tamper-proof query execution. The single-chain architecture is constrained in supporting multi-agency IoT environments for isolated data structures and interoperability bottlenecks. On the other hand, cross-chain service queries enable secure interoperability and dynamic resource integration across heterogeneous blockchain systems, but are amenable to privacy breaches, query latency, and consistency in multi-chain environments. To address these challenges, we propose a verifiable and low-latency cross-chain IoT service query scheme with the collaborative indexing. To encapsulate IoT service attributes, we design a service metadata model based on partial homomorphic encryption, allowing service providers to upload services without revealing the private service information. To achieve fine-grained metadata queries, the collaborative indexing mechanism filters irrelevant chains through a global index to narrow the query scope and then utilizes localized sub-indexes, optimizing search efficiency. Finally, we construct data structures based on fingerprint verification to ensure consistency between the query layer and blockchain data. We provide a security analysis of the robustness and privacy, and develop a supporting tool named ServChain. Experimental results demonstrate that, even with consistency verification, our scheme outperforms state-of-the-art solutions by significantly reducing query time (by at least 68.18%), communication overhead (by 45.9%), and computational cost (by 46.5% ).

Service composition has become a crucial mechanism for integrating heterogeneous Internet of Things (IoT) services to meet users' complex and dynamic requirements. In decentralized IoT environments, the absence of a trusted third party presents significant challenges in coordinating service providers. Blockchain offers a promising solution by maintaining tamperproof records to address the inherent multi-party trust problem. However, as IoT services are deployed across diverse platforms, cross-chain service composition introduces new challenges, particularly in establishing trust among multiple stakeholders and ensuring adaptive Quality of Service (QoS) optimization. To address these challenges, we propose a cross-chain IoT service composition approach named CSCIoT, which constructs adaptive QoS constraints while ensuring trustworthiness throughout the entire composition process. Our scheme establishes a Trustaware Practical Byzantine Fault Tolerance (T-PBFT) mechanism to evaluate IoT services using a QoS reputation model. A constraint graph is then constructed to capture dependencies among services, devices, and user requirements, enabling adaptive service selection in response to environmental changes. Smart contracts are further employed for continuous QoS monitoring. We demonstrate the adaptability and effectiveness of CSCIoT through a case study on autonomous vehicle leasing, and show its efficiency and QoS performance on real-world datasets compared to state-of-the-art approaches.

Water-fertilizer integrated regulation system aims to to improve crop yield, nutrient use efficiency (NUE), and water use efficiency (WUE). This study develops an intelligent control system with a cloud server network to centralize data management and process. The system contains of the monitoring module, the intelligent cloud platform module, and the control terminal. In the monitoring module, the sensor network and phenotypic monitoring method were utilized to collect real-time crop and environment data. In the intelligent cloud platform module, a perception-computing-control integrated computing optimization framework was constructed to achieve the localization of control tasks by analyzing perceived data, training predictive control models, and optimizing deep reinforcement learning algorithms. The control terminal contains of task scheduling, equipment control, and data collection drive, as well as a reasonable deployment pipeline in the cloud. Our developed control model has increased crop productivity nearly 9% and achieved water resource savings nearly 15.6%. Multi-modal large models will be applied to adjust model parameters in different environments.

Person search is a challenging task in computer vision and multimedia understanding, which aims at localizing and identifying target individuals in realistic scenes. State-of-the-art models achieve remarkable success but suffer from overloaded computation and inefficient inference, making them impractical in most real-world applications. A promising approach to tackle this dilemma is to compress person search models with knowledge distillation (KD). Previous KD-based person search methods typically distill the knowledge from the re-identification (re-id) branch, completely overlooking the useful knowledge from the detection branch. In addition, we elucidate that the imbalance between person and background regions in feature maps has a negative impact on the distillation process. To this end, we propose a novel KD-based approach, namely Disaggregation Distillation for Person Search (DDPS), which disaggregates the distillation process and feature maps, respectively. Firstly, the distillation process is disaggregated into two task-oriented sub-processes,i.e., detection distillation and re-id distillation, to help the student learn both accurate localization capability and discriminative person embeddings. Secondly, we disaggregate each feature map into person and background regions, and distill these two regions independently to alleviate the imbalance problem. More concretely, three types of distillation modules,i.e., logit distillation (LD), correlation distillation (CD), and disaggregation feature distillation (DFD), are particularly designed to transfer comprehensive information from the teacher to the student. Note that such a simple yet effective distillation scheme can be readily applied to both homogeneous and heterogeneous teacher-student combinations. We conduct extensive experiments on two person search benchmarks, where the results demonstrate that, surprisingly, our DDPS enables the student model to surpass the performance of the corresponding teacher model, even achieving comparable results with general person search models.

There are long-term concerns about the risk of privacy leakage when data is acquired and transmitted via Internet of Things (IoT) devices, which too often hinders the development of data-driven applications and services. To tackle such obstacle, we present a novel blockchain-based data service provisioning architecture based on federated learning, named BP-DSP. The system enables data owners to contribute to data-driven services without exposing their private data. To enhance the model accuracy and reduce the communication overhead, we build a data preference and data summary model, by which the model selects data providers whose data preferences align with those of the customers, serving as participating nodes in federated learning training. By leveraging lightweight homomorphic encryption algorithms, BP-DSP encrypts the model gradients to prevent the potential privacy leakage of data owners’ sensitive information from the inference attack. We construct a reputation-based incentive mechanism to motivate computational nodes to perform model parameter validation and punish participating nodes that upload false parameters or low-quality models in order to constrain their self-interest. Through a detailed security analysis, we show that parameter and model privacy are well preserved during the whole process. Finally, we conduct extensive experiments on real-world datasets to demonstrate the efficiency and reliability of BP-DSP, that the BP-DSP speeds up operational efficiency by over 40%.

As the number of web services proliferates, service discovery has emerged as a burgeoning area of research. Conventional service discovery methods hinge on index creation to enhance the efficiency of service identification. Moreover, service discovery and composition techniques can be distributed across various edge services. Owing to the multi-level indexing mechanism, determining the hash range for distinct services is crucial. Additionally, heterogeneity between service compositions results in differing indexing structures. In this study, we present a novel thread-based indexing method explicitly devised for service discovery in web service environments. Our approach commences with employing indexing to distinguish partitioning intervals of web service repositories. Subsequently, we assign a corresponding index hash value to each web service. Each index is then threaded and registered within a thread-based index pool. By allocating threads to a central service discovery server, the pertinent search thread undertakes the service retrieval task initiated by the service consumer, enabling asynchronous processing and result delivery to the client. To corroborate our method, we performed comprehensive experiments in real-world web service discovery scenarios. The outcomes underscore the effectiveness of our approach, emphasizing its potential to augment service discovery in web service environments.

Abstract Deoxys‐BC is the internal tweakable block cipher of the authenticated encryption (AE) Deoxys family, in which Deoxys‐II is the primary choice for the use case of ‘Defence in depth’ among the portfolio of CAESAR competition. Improvements of the related‐tweakey rectangle attacks on round‐reduced Deoxys‐BC using the known distinguishers is focussed in this study. Under the new related‐key rectangle attack framework proposed by Dong et al. in EUROCRYPT 2022, we present three kinds of precomputed tables to further reduce the time complexity in the key‐recovery phase. In the related‐tweakey rectangle attack, the invalid quartets are filtered or the subtweakey candidates are obtained by lookup the precomputed tables without more computation. Based on the precomputed table technique, we improved the related‐tweakey rectangle attacks on 11‐round Deoxys‐BC‐256, 13‐round and 14‐round Deoxys‐BC‐384. Furthermore, we reduce the time complexity of the 13‐round related‐tweakey rectangle attack on Deoxys AE scheme Deoxys‐I‐256‐128 by a factor of 2 24 compared with the best previous attack.

Rapid development of ultrafast ultrasound imaging has led to novel medical ultrasound applications, including shear wave elastography and super-resolution vascular imaging. However, these have yet to incorporate endoscopic ultrasonography (EUS) with a circular array, which provides a wider view in the alimentary canal than traditional linear and convex arrays. A coherent diverging wave compounding (CDWC) imaging method was proposed for ultrafast EUS imaging and implemented on a custom circular array. In CDWC, virtual acoustic point sources are allocated and virtually insonified diverging waves from each source are achieved by adjusting all circular array elements' emission time delays. Diverging waves emitted from different virtual sources are coherently compounded, generating synthetic transmit focusing at every location in the image plane. As the field of view of the circular array is centrally symmetric, all virtual sources are equidistantly distributed on a concentric circle of radius r . To achieve the highest frame rate possible with image quality comparable to that obtained with the traditional multi-focus imaging method, the effects of various radii r and virtual source quantities on the compounded image quality were theoretically analyzed and experimentally verified. Simulation, phantom, and ex-vivo experiments were conducted with an 8 MHz, 124-element circular array, with a 5.35 mm radius. When 16 virtual sources were used with r=1.605 mm, image quality comparable to that obtained with the multi-focus approach was achieved at a frame rate of 1000 frames/s. This demonstrates the feasibility of the proposed ultrafast EUS imaging method and promotes further development of multi-functional EUS devices.