Underwater acoustic backscatter communication technology brings a new perspective on addressing the energy dilemmas of Internet of Underwater Things (IoUT). However, time asynchronism in underwater acoustic backscatter devices (UABDs) can significantly degrade the performance of the UABD-based IoUT system. Existing time synchronization algorithms lose practicability leading to high energy consumption in scenarios where charging delays vary. To address these challenges, we propose UAB-Sync, a time synchronization algorithm specifically designed for the system. UAB-Sync introduces a novel three-stage architecture that uses dual constraints of time and energy to dynamically optimize the duration of energy signal, achieving adaptive approximation of optimal results. Besides, a closed-form solution for clock parameter estimation that incorporates Doppler factor estimation and accounts for multi-source measurement errors is developed, ensuring effective synchronous correction. Simulation results demonstrate that UAB-Sync significantly outperforms existing synchronization schemes in terms of both accuracy and energy efficiency for the UABD-based IoUT system.

Underwater backscatter is an emerging passive communication technology powered by underwater acoustic energy, which has emerged as a promising solution to the underwater energy problem. However, the backscatter mechanism causes the reflected signal strength to undergo periodic ups and downs due to the phase cancellation effect when the receiving end is in motion. Additionally, during the signal retro-reflective process, the reflected array signals deviate from the optimal beam direction when there is movement at the receiving end, introducing nonlinear attenuation into the signal strength. This paper proposes GKCformer signal strength forecasting method for underwater backscatter systems. It is based on the original sequence and Gram angular field image modal design to add period information. It utilizes Transformer to rearrange and improve channel feature information, and Kolmogorov-Arnold Networks to make the fitting better. Experimental results show that the proposed model outperforms the classical model in mean squared error (MSE) and mean absolute error (MAE) metrics, which demonstrates its better performance in prediction accuracy.

The rapid adoption of microservice architectures has spurred research into addressing various challenges in microservice system evolution, such as service deployment and task offloading. While numerous solutions have been proposed by researchers, validating these approaches requires extensive experimentation, including both algorithmic testing and simulations across different Microservice-Based Applications (MBAs). However, current research lacks sufficient physical experimentation, which is essential for evaluating how these solutions perform when deployed on actual computing infrastructure with running microservice instances. This gap exists mainly because conducting physical experiments is complex and costly, primarily due to two barriers: the lack of standardized problem modeling approaches and the absence of suitable experimental platforms. To address these challenges, the Universal Evolution Model (UEM) was proposed that unifies different microservice research problems within a common evolution framework. This model frames research problems as approaches to evolve microservice systems under specific constraints to achieve desired objectives. Based on UEM and the widely-adopted Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K) model, a general Microservice System Evolution Architecture was designed that standardizes experimental processes across diverse research scenarios. Building on these foundations, MicroForge was presented, an open-source experimental platform equipped with tools for accelerating experiments. The platform incorporates RescueService, a carefully curated MBA. Through comprehensive evaluation across various MBAs, the functional effectiveness of MicroForge was validated. Additionally, a practical user guide was provided to facilitate the platform's adoption and utilization.

Personal data cloud, as an emerging personal data management mode in recent years, enables to reduce the risk of privacy disclosure and protect the rights and interests of individuals given by privacy protection laws and regulations. Personal data that is generated during the interaction between individual users and various services contains a lot of useful personalized but private information and plays a crucial part in personalized service recommendation. In traditional service recommendation scenario, personal data of massive users is centralized owned/managed by service providers, which is easy to lead to privacy disclosure and personal data abuse. In the personal data cloud based service recommendation scenario, personal data of individual users is distributed stored and controlled by users themselves. To address the challenges of privacy protection and distributed storage of personalized data in this new recommendation scenario, we propose HyFL, a deep learning based recommendation algorithm with hybrid federated learning. HyFL can conduct recommendation based on the personal data from multiple services. The security of HyFL is theoretically proved, and experiments on real-world datasets demonstrate that HyFL performs better on the basis of privacy preservation than that of some traditional recommendation approaches.

Privacy preservation is drawing growing concern. Due to personal data of massive users is centralizedly owned/managed by service providers at present, it is very easy to lead to privacy disclosure and personal data abuse, therefore many countries and regions put forward privacy protection laws and regulations and researchers also propose some de-centralized personal data management systems. Nevertheless, the implementation of decentralized data management model means a radical change in current service development and service model. For service providers, it is too difficult and costly to realize these personal data management systems, and there are also no additional economic benefits. These personal data management systems are too ideal to popularize from a business model perspective, so the development of decentralized models proceeds slowly. In response to this situation, we propose a new personal data management service which realizes the transition stage between reality and ideal where users’ data can exist both in users’ personal data cloud and service providers. To address the challenges of data collection and data semantic uniformity caused by personal data cloud at this transition stage, we propose a personal data management architecture based on SoLiD (Social Linked Data) and DTP (Data Transfer Project). The proposed architecture could transfer users’ data from service providers to personal data cloud and fuse heterogeneous data through data fusion technology so as to provide users with the ability to manage their own data and a unified view of data. The feasibility and efficiency of the proposed service are practically proved through conducting a case study.

From the point of design methodology, change quickly development computer technology of computer supported cooperative work (CSCW) has influence on the actions of landscape planning and design, such as renovation of design tradition conception, new ways for shape originality, and testing method for landscape planning and design. It has practicality value of determining computer technology values in landscape architecture design and thinking.