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As an information carrier, the Internet of Things (IoT) based on the Internet and sensing equipment makes all physical objects form an interconnected network. The 5th generation mobile networks (5G) technology has many advantages, such as high data rates, reduced latency, energy savings, reduced costs, increased system capacity and large-scale device connectivity, realize the real-time data collection, transmission, analysis, management, and application in the era of global Internet of Everything. In order to quickly respond to people’s daily requirements and provide the smart application based on artificial intelligence technology in various scenarios, the number of IoT devices will further increase. The integration of mobile-edge computing (MEC) and IoT is imperative, especially in industries needing real-time data computing, such as smart home, public security, automobile transportation, smart health, emotion care, etc. As a new form of IoT terminal combining 5G and MEC, wearable device based on intelligent fabrics plays an important role in smart health and emotion care, which is one of the potential development directions of the next generation of intelligent medical and rehabilitation systems.

We present a Boolean algebra based algorithm to extract if-then classification rules from supervised learning feedforward neural networks to solve the black-box problem of the decision process of the neural networks. This algorithm is called the BAB-G rule extraction algorithm, which stands for Boolean Algebra Based for General inputs. According to the concept of discretizing continuous hidden neuron activation values, we present the BAB-G rule extraction algorithm, which can be applied to three-layer feedforward neural networks with discrete, continuous, or mixed inputs. The antecedent parts of the if-then rules obtained from this algorithm are slanting hyperplanes. During the rule extraction procedure, redundant hidden neurons can be removed without affecting the functionality of the neural networks. Some empirical results on the data sets from the UCI repository of machine learning database are given for comparing our rule extraction algorithm and C5.0 decision tree algorithm. For these datasets, statistical hypothesis tests show that the rules obtained from our algorithm achieve the same classification accuracy as the neural networks. Moreover, our rules are better than the C5.0 decision tree both on comprehensibility and on accuracy for these datasets.

In recent years, the rapid growth of traffic has become a serious problem of mobile network operators. For effectively mitigating this traffic explosion problem, there have been many efforts to research on offloading the traffic from cellular links to direct communications among users. In this paper, we are motivated by users’ sharing activities, and hence propose the framework of T raffic O ffloading assisted by S ocial network services (SNS) via opportunistic S haring in mobile social networks (MSNs), TOSS , to offload SNS-based cellular traffic by user-to-user sharing. First, a subset of users who are to receive the same content was selected as initial population depending on their content spreading impacts in the online SNSs and their mobility patterns in the offline MSNs. Then users move, encounter and share the content via opportunistic local connectivity with each other, the content via opportunistic local connectivity with each other, e.g., Bluetooth, Wi-Fi Direct, Device-to-Device in LTE. Individual users have distinct access patterns, which potentially allow TOSS to exploit the user-dependent access delay between the content generation time and each user’s access time for content sharing purposes. The traffic offloading and content spreading among users are analyzed by taking into account various options in linking SNS and MSN traces. Four mobility traces and online SNS trace for evaluation are analyzed. An extended evaluation over a large-scale data set are further carried out, and the effectiveness of TOSS is further proved.

The unified management of IoT devices with interoperability can be inspired by cloud computing. In addition, sinking the 5G core network to the edge brings chances for the deployment of end-to-end ultralow-latency services. However, the resource efficiency brought by heterogeneous computing devices in 5G spectrum multiplexing environments has encountered challenges. To discuss this issue from a comprehensive perspective, this article first proposes an ultralow-latency service deployment architecture in 5G heterogeneous networks, and three cognitive engines are the key components for efficient service communication across the terminal/edge/cloud computing structure. Then we give an analysis of application task model in the proposed architecture, and following the service response time models are established. In addition, it is efficient to deploy multiuser tasks with constraint resources when the differentiated user requirements are met. Finally, we conducted some experiments and the result statistics are up to our expectations. The first one is the system performance under two microcloud covered cells, and the second one is the performance comparison of the proposed solution with three single scenes of terminal computing, edge computing and cloud computing.

Currently, the integration of distributed energy generators through virtual power plants in the Internet of Energy is a mainstream method. The complex structure of virtual power plants and the characteristics of distributed energy make it difficult to solve the economic dispatch problems of virtual power plants. In addition, the load of a virtual power plant is unstable and uncertain and thus requires a robust economic dispatch strategy. Because the selection of the set of uncertain conditions is conservative, the traditional robust economic dispatch strategies cannot effectively reduce the cost of virtual power plants. In addition, the traditional methods for solving robust strategies cannot directly solve nonlinear and nonconvex problems. In this article, we propose a scenario-based robust economic dispatch strategy for virtual power plants, aiming to reduce the operational costs of virtual power plants. First, to reduce the conservatism of the strategy, scenario-based data augmentation is adopted for data generation. Through a generative adversarial network, a large amount of scene data are generated to extend the set of uncertain conditions. The scene data cannot only reduce the conservatism but also can be used in the determination of robust strategies. Second, deep reinforcement learning is adopted for historical data training, directly solving nonlinear and nonconvex problems to obtain a robust economic dispatch strategy. As experiments show, with the accurate generation of scene data, the proposed economic dispatch strategy is robust and effectively reduces the cost of virtual power plants.

With the explosive growth of multimedia contents in vehicular networks and the development of 5G technology come great challenges to the Internet of Vehicles system. By utilizing the storage capability of roadside units, popular contents can be cached in advance during nonpeak periods, which can bring better quality of experience to users. However, it is difficult for the existing content caching schemes in traditional vehicular networks to achieve global control when designing content caching mechanisms. Moreover, some content caching schemes also bring security concerns. Thus, in this article, we first discuss the content caching problem in the 5G-based cognitive Internet of Vehicles. Then we design security enhancement methods with the cognitive engine. Finally, we use a special case to study how to design a secure and delay-sensitive content caching scheme in cognitive vehicular networks. Extensive experiments show that the proposed algorithm outperforms traditional content caching methods.

Working memory allows individuals to temporally maintain and manipulate information that is no longer accessible from the sensorium. Whereas prior studies have detailed frontoparietal contributions to working memory processes, less emphasis has been placed on subcortical regions, in particular the human thalamus. The thalamus has a complex anatomy that consists of several distinct nuclei, many of which have dense anatomical connectivity with frontoparietal regions, and thus might play an important yet underspecified role for working memory. The goal of our study is to characterize the detailed functional neuroanatomy of the human thalamus and thalamocortical interactions during the n-back task. To that end, we analyzed an n-back fMRI dataset consisting of 395 subjects from the Human Connectome Project (HCP). We found that thalamic nuclei in the anterior, medial, ventral lateral, and posterior medial thalamus showed stronger evoked responses in response to higher working memory load. Activity in most thalamic nuclei were only modulated by working memory load, but not by categorical membership of the memorized stimuli, suggesting that thalamic function supports domain-general processing for working memory. To determine whether thalamocortical interactions contribute to cortical activity for working memory, we employed an activity flow mapping analysis to test whether thalamocortical interactions can predict cortical task activity patterns. In support, this data-driven thalamocortical interaction model explained a significant amount of variance in the observed cortical activity patterns modulated by working memory load. Our results suggest that the anterior, medial, and posterior medial thalamus, and their associated thalamocortical interactions, contribute to the modulations of distributed cortical activity during working memory.