With the widespread application of service-oriented architecture (SOA), a flood of similarly functioning services have been deployed online. How to recommend services to users to meet their individual needs becomes the key issue in service recommendation. In recent years, methods based on collaborative filtering (CF) have been widely proposed for service recommendation. However, traditional CF typically exploits only low-dimensional and linear interactions between users and services and is challenged by the problem of data sparsity in the real world. To address these issues, inspired by deep learning, this article proposes a new deep CF model for service recommendation, named location-aware deep CF (LDCF). This model offers the following innovations: 1) the location features are mapped into high-dimensional dense embedding vectors; 2) the multilayer-perceptron (MLP) captures the high-dimensional and nonlinear characteristics; and 3) the similarity adaptive corrector (AC) is first embedded in the output layer to correct the predictive quality of service. Equipped with these, LDCF can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem. Through substantial experiments conducted on a real-world Web service dataset, results indicate that LDCF's recommendation performance obviously outperforms nine state-of-the-art service recommendation methods.

In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user’s mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches.

Background: Various older adult care settings have embraced the use of the life story approach to enhance the development of comprehensive care plans. However, organizing life stories and extracting useful information is labor-intensive, primarily due to the repetitive, fragmented, and redundant nature of life stories gathered from everyday communication scenarios. Existing life story systems, while available, do not adequately fulfill the requirements of users, especially in the application of care services. Objective: The objective of this study is to design, develop, and evaluate a digital system that provides caregivers with the necessary tools to view and manage the life stories of older adults, enabling expedited access to pertinent information effectively and visually. Methods: This study used a multidisciplinary, user-centered design approach across 4 phases: initial design requirements, prototyping, prototype refinement workshops, and usability testing. During the initial phase, we conducted field research in the Hefei Tianyu Senior Living Service Nursing Home, China, to discover how caregivers currently store and use life stories and their needs, challenges, and obstacles in organizing and retrieving information. Subsequently, we designed a low-fidelity prototype according to the users’ requirements. A prototyping workshop involving 6 participants was held to collaboratively design and discuss the prototype’s function and interaction. User feedback from the workshops was used to optimize the prototype, leading to the development of the system. We then designed 2 rounds of usability testing with 7 caregivers to evaluate the system’s usability and effectiveness. Results: We identified 3 categories of functionalities that are necessary to include in the design of our initial low-fidelity prototype of life story visualizations: life story input, life story organization, and timeline generation. Subsequently, through the workshops, we identified 3 categories for functional optimization: feedback on user interface and usability, optimization suggestions for existing features, and the request for additional functionalities. Next, we designed a medium-fidelity prototype based on human-centered design. The Story Mosaic system underwent usability testing in the Hefei Tianyu Senior Living Service Nursing Home. Overall, 7 users recorded and organized 1123 life stories of 16 older adults. The usability testing results indicated that the system was accessible and easy to use for caregivers. Based on the feedback from the usability testing, we finalized the high-fidelity prototype. Conclusions: We designed, developed, and evaluated the Story Mosaic system to support the visual management of older adults’ life stories. This system empowers caregivers through digital technology and innovative design, pioneering personal narrative integration in caregiving. This system can expand to include informal caregivers and family members for continued adaptability and empathy.

To relieve the pressure of processing computation‐intensive applications on mobile devices and avoid high latency during data transmission, edge computing is proposed to solve this problem. Mobile edge computing (MEC) allows the deployment of MEC servers at the edge of the network to interact with users on the premise of low transmission delay, thereby improving the quality of service (QoS) for users. However, due to the high mobility of users, with the continuous change of geographical location, when users exceed the signal range of the MEC server, the services they request on the MEC server will also be migrated to other MEC servers. The handoff process may involve high response delays caused by service forwarding, thereby greatly degrading QoS. For the above problems, in this paper, a service migration optimization method based on transmission power control is proposed. First, according to the transmission power of the MEC server, the user’s activity range is divided into multiple subregions based on a Voronoi diagram. Therefore, there is one MEC server in each subregion, and the size of each subregion is adjusted by controlling the transmission power of the MEC server to minimize the number of wireless handoffs and the energy consumption of the MEC server. Then, the particle swarm optimization (PSO) is adopted to solve the above multiobjective optimization problem. Finally, the effectiveness of the proposed method is verified through extensive experiments.