Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users' QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price.

With the flourishing of the credit card business and Internet technology, the risk of fraudulent credit card transactions is ever-increasing due to the complex information involved in the credit card business. Since the high redundancy of feature information and imbalance of class distribution in transaction data, the performance of the existing machine learning-based models for detecting credit card fraudulent transactions still needs to be improved. Therefore, it is crucial to build fraud detection models for effective feature engineering and sampling techniques. This article proposes a credit card fraud detection model incorporating a fraud feature-boosting mechanism with a spiral oversampling balancing technique (SOBT). Specifically, we present a compound grouping elimination strategy to exclude highly redundant and correlated features from the credit card transaction dataset and improve the data quality. Furthermore, we design a multifactor synchronous embedding mechanism, which combines the performance evaluation metrics of the embedding model for each feature and improves the decision-making ability of each feature for the target domain. Moreover, we propose an SOBT to balance the ratio of legitimate to fraudulent transactions, which improves the ability of the fraud detection model to distinguish legitimate from fraudulent transactions. Extensive experimental results based on two real-world datasets demonstrate that our methods can facilitate efficient credit card fraud detection and achieve better performance than state-of-the-art methods.

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP‐hard problem. In this paper, we propose a three‐layer scheduling model based on whale‐Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition‐based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale‐cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

The commercialization of 5G has greatly promoted the development of medical Internet of Things (IoT). More medical devices connected to the Internet may further increase the communication power consumption. Meanwhile, privacy protection technique in cloud computing cannot match the rapid development of medical applications. Therefore, exploring secure, balanced and energy-efficient data transmission between medical devices and cloud servers is extremely challenging. This paper focuses on the security and energy consumption of medical electronic health record (EHR) data transmission and storage between cloud server and IoT device users. We build a secure energy-saving communication and encrypted storage model by adding secure energy-saving communication scheme and encryption algorithm to the traditional medical cloud model. Specifically, we propose a communication authentication algorithm MedGreen based on elliptic curve and bilinear pair. In the algorithm, the two communication parties can complete the key establishment and identity authentication only after one communication, which effectively balances the resource overhead of the key center and the user, and resists the Man-in-the-middle attack. Aiming at the characteristics of large repetition and high sensitivity of medical data, we present a secure data storage algorithm MedSecrecy based on Huffman compression and RC4. The algorithm not only maintains the RC4 encryption efficiency, reduces the amount of cipher text data, but also improves confidentiality, randomness and security of the key stream. Comprehensive analysis and simulations show that our system is secure, energy-saving and highly efficient for EHR.

The popularization of mobile communication devices and location technology has spurred the increasing demand for location-based services (LBSs). While enjoying the convenience provided by LBS, users may be confronted with the risk of privacy leakage. It is very crucial to devise a secure scheme to protect the location privacy of users. In this paper, we propose an anonymous entropy-based location privacy protection scheme in mobile social networks (MSN), which includes two algorithms K-DDCA in a densely populated region and K-SDCA in a sparsely populated region to tackle the problem of location privacy leakage. The K-DDCA algorithm employs anonymous entropy method to select user groups and construct anonymous regions which can guarantee the area of the anonymous region formed be moderate and the diversity of the request content. The K-SDCA algorithm generates a set of similar dummy locations which can resist the attack of adversaries with background information. Particularly, we present the anonymous entropy method based on the location distance and request contents. The effectiveness of our scheme is validated through extensive simulations, which show that our scheme can achieve enhanced privacy preservation and better efficiency.

With the proliferation of intelligent services and applications authorized by artificial intelligence, the Internet of Things has penetrated into many aspects of our daily lives, and the medical field is no exception. The medical Internet of Things (MIoT) can be applied to wearable devices, remote diagnosis, mobile medical treatment, and remote monitoring. There is a large amount of medical information in the databases of various medical institutions. Nevertheless, due to the particularity of medical data, it is extremely related to personal privacy, and the data cannot be shared, resulting in data islands. Federated learning (FL), as a distributed collaborative artificial intelligence method, provides a solution. However, FL also involves multiple security and privacy issues. This paper proposes an adaptive Differential Privacy Federated Learning Medical IoT (DPFL‐MIoT) model. Specifically, when the user updates the model locally, we propose a differential privacy federated learning deep neural network with adaptive gradient descent (DPFLAGD‐DNN) algorithm, which can adaptively add noise to the model parameters according to the characteristics and gradient of the training data. Since privacy leaks often occur in downlink, we present differential privacy federated learning (DP‐FL) algorithm where adaptive noise is added to the parameters when the server distributes the parameters. Our method effectively reduces the addition of unnecessary noise, and at the same time, the model has a good effect. Experimental results on real‐world data show that our proposed algorithm can effectively protect data privacy.