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In recent years, the use of edge computing to solve the problem of limited resources in the IoV has attracted more and more attention. Vehicles can upload tasks to the edge servers within their radio range for computing cooperation and offloading. However, due to shared resources among edge servers, the uneven distribution of vehicles may lead to many problems such as uneven tasks distribution, unbalanced load, and low computing efficiency on different edge servers. On the other hand, most of the existing works are proposed to address the load unbalance issue of edge servers with the help of remote cloud or vehicle cloud, but still leading to resource wasting problems especially for edge servers with a light load. To address this issue, a task transfer scheme has been proposed among different edge servers in this paper. First, we designed a partitioned and hierarchical software-defined network architecture for IoV. After that, a load balancing model is proposed under this network architecture, in which the global controller can manage and schedule the local tasks from each edge server. Next, we proved that the task allocation problem for our load balancing model under the proposed network architecture is an NP-hard problem. To solve this problem, a Deep Q-Network (DQN) model is proposed to minimize the mean square deviation of loads among different edge servers. Numerical results show that our proposed load balancing model can significantly improve the resource utilization of edge servers as well as reduce the computational latency of vehicular tasks. [ABSTRACT FROM AUTHOR]

With the widespread application of mobile edge computing (MEC), MEC is serving as a bridge to narrow the gaps between medical staff and patients. Relatedly, MEC is also moving toward supervising individual health in an automatic and intelligent manner. One of the main MEC technologies in healthcare monitoring systems is human activity recognition (HAR). Built-in multifunctional sensors make smartphones a ubiquitous platform for acquiring and analyzing data, thus making it possible for smartphones to perform HAR. The task of recognizing human activity using a smartphone's built-in accelerometer has been well resolved, but in practice, with the multimodal and high-dimensional sensor data, these traditional methods fail to identify complicated and real-time human activities. This paper designs a smartphone inertial accelerometer-based architecture for HAR. When the participants perform typical daily activities, the smartphone collects the sensory data sequence, extracts the high-efficiency features from the original data, and then obtains the user's physical behavior data through multiple three-axis accelerometers. The data are preprocessed by denoising, normalization and segmentation to extract valuable feature vectors. In addition, a real-time human activity classification method based on a convolutional neural network (CNN) is proposed, which uses a CNN for local feature extraction. Finally, CNN, LSTM, BLSTM, MLP and SVM models are utilized on the UCI and Pamap2 datasets. We explore how to train deep learning methods and demonstrate how the proposed method outperforms the others on two large public datasets: UCI and Pamap2. [ABSTRACT FROM AUTHOR]

Nowadays, due to the advances in mobile and wireless communication, mobile devices are widely used in our daily life. Meanwhile, in the mobile devices, there exits diverse applications which are developed to satisfy the various requirements of mobile users. Correspondingly, a large number of services are produced by the mobile devices. Since the mobile devices have limitations on the battery capacity, physical size, etc., they can hardly complete all the services. To relieve this problem, driven by edge computing, the central units (CUs) in fifth-generation wireless systems (5G) could be enhanced into edge nodes (ENs) for processing. However, during the transmission of edge services, the privacy leakage may occur, and the overall performance of the networks needs to be taken into consideration. In this paper, an optimization problem is defined to improve the resource utilization and load balance for all the ENs while protecting the privacy information and satisfying the time requirement. Then, a balanced service offloading method, abbreviated BSOM, is proposed. Finally, abundant experiments and evaluations are conducted to validate our proposed method is both effective and feasible. [ABSTRACT FROM AUTHOR]

With the explosion of social data comes a great challenge called information overloading. To overcome this challenge, recommender systems are expected to support users in quickly accessing the appropriate content. However, cold-start users are a formidable challenge in the design of recommender systems because the conventional recommendation services are based on a single data source, namely, a single field. Considering the advantages of social-based and cross-domain approaches involving further additional data, we propose a cross-domain recommender system, including three approaches, based on multi-source social big data. The proposed approach is expected to effectively alleviate the issues of cold-start users by transferring user preferences from a related auxiliary domain to a target domain. Moreover, the transferred preferences are able to improve the diversity of recommendations. Through adequate evaluations based on an actual dataset in the book and music domains, it is shown that the accuracies of the three proposed approaches are significantly improved compared with the conventional recommender approaches, such as collaborative filtering and matrix factorization. In particular, the proposed approaches are available to provide cold-start users with highly effective recommendations. [ABSTRACT FROM AUTHOR]

B Correction: Mobile Networks and Applications b https://doi.org/10.1007/s11036-022-01972-0 Authors have found out that the affiliation of the first author Pu Li is not correct, which should be "The College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang China 310027". The original article can be found online at https://doi.org/10.1007/s11036-022-01972-0. [Extracted from the article]