The computer-aided diagnosis (CAD) method based on few-shot learning (FSL) effectively reduces the dependence on labelled medical images. However, the catastrophic forgetting defect of neural networks seriously decreases the ability of CAD methods to meet real diagnosis needs. Moreover, the low classification accuracy and limited categories make FSL classification difficult. A few-shot class incremental learning (FSCIL) method for skin disease diagnosis (SDD) based on the consumer Internet of Things (CIoT) is designed to solve these problems in this paper. First, dermoscopic images and clinical images obtained from CIoT nodes are used to cover more skin disease categories, and a dual-flow modal alignment module is designed to mitigate the modal misalignment of different modal images. Second, a queue of gradient episodic memory (Q-GEM) method is designed to solve the catastrophic forgetting problem. Third, a differential diagnosis method (DDM), which can effectively improve the low classification accuracy of the few-shot learning (FSL) classification network, is designed. Experiments show that the top-3 diagnostic accuracy of the proposed method can match the accuracy level of dermatologists, the accuracy is 11.2% improvement over the SOTA method.

Social media have become increasingly popular for the sharing and spreading of usergenerated content due to their easy access, fast dissemination, and low cost. Meanwhile, social media also enable the wide propagation of cyber frauds, which leverage fake information sources to reach an ulterior goal. The prevalence of untrustworthy information sources on social media can have significant negative societal effects. In a trustworthy social media system, trust calculation technology has become a key demand for identification of information sources. Trust, as one of the most complex concepts in network communities, has multi-criteria properties. However, existing work only focuses on single trust factor, and does not fully consider the complexity of trust relationships in social computing. In this paper, a multi-criteria trustworthiness calculation mechanism called Info-Trust is proposed for information sources, in which identity-based trust, behavior-based trust, relation-based trust, and feedback-based trust factors are incorporated to present an accuracy-enhanced full view of trustworthiness evaluation of information sources. More importantly, the weights of these factors are dynamically assigned by the ordered weighted averaging and weighted moving average (OWA-WMA) combination algorithm. This mechanism surpasses the limitations of existing approaches in which weights are assigned subjectively. Experimental results based on real-world datasets from SinaWeibo demonstrate that the proposed mechanism achieves greater accuracy and adaptability in trustworthiness identification of network information.

In mobile ad hoc networks (MANETs), if mobile devices frequently leave or join the overlay of MANETs, the communication links between mobile devices or between mobile devices and cloud will lose or reestablish; for searching and routing the needed cloud services again, more energy will be consumed. 5G is the fifth generation mobile communication technology, and fog computing is defined as a distributed computing infrastructure that is able to handle billions of Internet-connected devices. Therefore, combining fog computing with 5G, we present a novel and effective dynamic cloudlet-assisted routing mechanism (DCRM) for MANETs to solve the energy-saving problem of link breakages. First, for every mobile device in MANETs, we build a temporary file to record its identity and route information in a certain time. Moreover, as a key promising technology of 5G, device-to-device is used as the communication way between mobile devices, because it can enhance the communication ability and the information sharing ability between mobile devices. Second, cloudlets can be considered as small data centers, and we set the sharing relation table and the cooperation mechanism between cloudlets. Then, relying on these, mobile devices can quickly route and search the requested services regardless of the frequent movement of mobile devices in MANETs. The experimental results show that DCRM can save more time and several times more energy, and display more advantages than the network model without the proposed mechanism in many ways, consequently enable cloud to provide services that are more realistic for the future mobile network applications.

Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

Data aggregation is a critical operation in wireless sensor networks(WSNs). Many applications have strict requirements for the latency of data aggregation. This paper focuses on the latency problem of data aggregation. Two factors determine the latency of data aggregation. First, because of the existence of interference, efficient collision-free scheduling is crucial for reducing data aggregation latency. Second, the tree structure has an important impact on data aggregation latency. In this paper, we propose a novel approach called distributed and efficient data aggregation scheduling over multi-channel links (DEDAS-MC). DEDAS-MC minimizes the latency in routing the aggregated data to the sink over multi-channel links. In DEDAS-MC, we first present a scheduling algorithm to schedule sensors to avoid interference and minimize the latency of data aggregation on a given tree. Then, a distributed algorithm for constructing minimum-latency data aggregation trees is proposed by employing the Markov approximation method. In DEDAS-MC, the value of β is adaptive. The Markov approximation method based adaptive-β is more flexible and efficient than the single β approximation. Experiments show that DEDAS-MC outperforms the existing competing schemes.

The interaction between mobile Internet of Things (IoT) devices is based on a hybrid communication architecture. To improve packet delivery ratio, reduce end-to-end delay and protect data privacy, designing an efficient data forwarding scheme is crucial in guaranteeing the quality of data transmission. Fog computing, a novel distributed computing framework, can decrease the amount of data transmission on the Internet and improve quality of services. In this paper, we propose a fog computing-assisted trustworthy forwarding (FCTF) scheme. To the best of our knowledge, this work is the first study that investigates the role of fog infrastructure nodes (FINs) in forwarding scheme design. FCTF firstly selects the contact probability and the service degree as the basic trustworthy metrics between node pairs, and combines high-performance optimization algorithms to design a dynamic detection model of overlapping trustworthy communities (DOTC). Then, we construct logical joint edge community structures on the basis of the results of DOTC and the distribution of FINs at each timestamp. Finally, based on the logical joint edge community, we define the forwarding utilities for FINs and mobile devices respectively, and design self-adaptive forwarding rules. The experimental results prove that our FCTF scheme can enhance the delivery ratio, and decrease the latency and average hop-count more effectively, and has better routing quality compared with some popular forwarding models in mobile IoT applications.