Web service-oriented transactions have become an integral part of the Internet economy, with the mainstream transaction patterns relying primarily on cloud service markets. However, traditional service transaction methods have deficiencies in terms of both trust and scalability. Distrust between service provider (SP) and consumer (SC), particularly around online payments and data security, impedes the further growth of service transactions. Although blockchain-based transaction mechanisms have made notable progress in addressing trust issues, they still face performance bottlenecks. To tackle these challenges, this paper introduces TrustPay, a service transaction framework that leverages a dual-layer blockchain structure consisting of a parent chain and multiple subchains. The framework partitions the subchain network based on business, with each subchain dedicated to storing service invocation records generated within a specific business unit. The smart contract deployed on the parent chain will settle the invocation records in all subchain networks as transaction records and facilitate automatic transfers among blockchain accounts. This design leverages blockchain's inherent reliability while improving its scalability in large-scale scenarios. Additionally, a novel consensus protocol, REFEREE, is introduced and applied to the subchain network, ensuring efficient recording of invocation data and trusted verification among participants, further enhancing both trust and performance. Comparative experiments and analysis show that TrustPay's dual-layer blockchain structure and REFEREE protocol are not only reliable but also outperform baseline methods in terms of efficiency.

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles (CAVs) through comprehensive information gathering, sensing, device control, and data processing. Presently, ITS predominantly relies on sensors embedded in fixed infrastructure, notably Roadside Units (RSUs). However, RSUs are confined by coverage limitations and may encounter challenges in prompt emergency responses. On-demand resources, such as drones, present a viable option to supplement these deficiencies effectively. This paper introduces an approach where Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are integrated to formulate a high-availability drone swarm control and communication infrastructure framework, comprising the cloud layer, edge layer, and device layer. Drones confront limitations in flight duration attributed to battery limitations, posing a challenge in sustaining continuous monitoring of road conditions over extended periods. Effective drone scheduling stands as a promising solution to overcome these constraints. To tackle this issue, we initially utilized Graph WaveNet, a specialized graph neural network structure tailored for spatial-temporal graph modeling, for training a congestion prediction model using real-world dataset inputs. Building upon this, we further propose an algorithm for drone scheduling based on congestion prediction. Our simulation experiments using real-world data demonstrate that, compared to the baseline method, the proposed scheduling algorithm not only yielded superior scheduling gains but also mitigated drone idle rates.

Innovation and prosperity of the Modern Service Industry bring convenience and efficiency to our society. However, the service governance capability lags behind the development of the industry, causing problems such as service violations and poor service quality. Existing works towards service regulation for service design time such as business process compliance checking are restricted to certain types of rules. When services or policies undergo evolution, rapid iteration becomes challenging. Motivated by this, we propose a service regulation analysis framework for service design time. It includes three phases: the service regulation modeling phase, which realizes modeling of regulation requirements; the service violation recognition phase, proposing an automatic detection algorithm based on process semantics; and the violation trace-back phase, which supports rapid localization of violations. Based on previous work, we construct the Enhanced-LPD4VR, a dataset with a broader range of processes and more nuanced annotations. Furthermore, we introduce an Internet healthcare service case study that illustrates the effectiveness of our framework through comparative experiments

Cyber-Physical-Social System (CPSS) is a pioneering solution in Crowd Computing, which integrates heterogeneous resources from cyber, physical, and social spaces, possessing collaborative capabilities in perception, computation, and control. However, existing CPSSs usually confine their functionality to rigid scenarios or tasks, and often oversimplify human resource modeling that fails to dynamically recognize human capabilities. In this work, we propose a Scenario-Driven CPSS to enable an adaptive resource choreography across scenarios. More concretely, we leverage temporal environments to identify events and disassemble these events into workflows, triggering the execution of corresponding capability units, where the capability units abstract the shared functionality of heterogeneous resource groups. Meanwhile, we improve the pre-assuming human capabilities to construct the relationship between human and their capabilities during the execution of workflows, sufficiently promoting human intelligence. Our real-world demo on fire rescue demonstrates the effectiveness of the solution.