Trusted third parties (TTPs) are frequently used for serving as an authority to issue and verify transactions in applications. Although the TTP-based paradigm provides customers with convenience, it causes a whole set of inevitable problems such as security threats, privacy vulnerabilities, and censorship. The TTP-based paradigm is not suitable for all modern networks, e.g., 5G and beyond networks, which are been evolving to support ubiquitous, decentralized, and autonomous services. Driven by the vision of blockchain technologies, there has been a paradigm shift in applications, from TTP-based to decentralized-trust-based. Decentralized applications (DApps) with blockchains promise no trust on authorities, tackling the key challenges of security and privacy problems. A main thrust of blockchain research is to explore frameworks and paradigms for decentralizing applications, fostering a number of new designs ranging from network architectures to business models. Therefore, this paper provides a compact and concise survey on the state-of-the-art research of decentralizing applications with blockchain in the 5G and beyond perspective. We provide four burning 5G and beyond challenges and discuss five aspects of motivation for decentralizing applications with blockchain. Then, we define nine fundamental modules of blockchains and explain the potential influence of these modules on decentralization in depth. We also discuss the interrelation between decentralization and some desired blockchain properties. Particularly, we present the capabilities of blockchain for decentralizing applications through reviewing DApps for 5G and beyond. We clearly distinguish three blockchain paradigms and discuss how developers to make right choices for 5G and beyond. Finally, we highlight important learned lessons and open issues in applying blockchain for decentralizing applications. Lessons learned and open issues from this survey will facilitate the transformation of centralized applications to DApps.

Mobile-edge computing (MEC) has been garnering considerable level of interests by processing computation tasks nearby mobile devices (MDs). With limited computation and communication resources and strict task deadline, balancing the energy consumption and time delay of computational tasks will be highly focused. MDs deployed energy harvesting (EH) modules can always provide service to continuous task requests, and finer-grained offloading schemes of the MEC system will significantly affect the time delay of computation tasks. However, when combined them together, the energy causal constraint and the coupling between offloading ratios and resources allocation will cause new challenges for the computation offloading problem. To address these issues, we investigate the partial computation offloading schemes for multiple MDs enabled by harvested energy in MEC. Specifically, we build models for two computing modes and EH process. Subsequently, we formulate a nonconvex optimization problem by minimizing the energy consumption of all the MDs while satisfying the constraint of time delay. Furthermore, we propose and design a novel algorithm based on the Lyapunov optimization to achieve optimal solution, that is, Lyapunov-optimization-based partial computation offloading for multiuser (LOMUCO). Then, we take the long-term average energy consumption and the discarding ratio of computation tasks as the quantitative metrics and conduct extended simulation experiments to confirm the performance of LOMUCO. Finally, compared to several baseline or state-of-the-art algorithms, including local computing all (LCA), offloading computing all (OCA), randomly partial computation offloading (RPCO), and Lyapunov-optimization-based dynamic computation offloading (LODCO), we can demonstrate the superiority of LOMUCO.

Background: An intracranial aneurysm is a cerebrovascular disorder that can result in various diseases. Clinically, diagnosis of an intracranial aneurysm utilizes digital subtraction angiography (DSA) modality as gold standard. The existing automatic computer-aided diagnosis (CAD) research studies with DSA modality were based on classical digital image processing (DIP) methods. However, the classical feature extraction methods were badly hampered by complex vascular distribution, and the sliding window methods were time-consuming during searching and feature extraction. Therefore, developing an accurate and efficient CAD method to detect intracranial aneurysms on DSA images is a meaningful task. Methods: In this study, we proposed a two-stage convolutional neural network (CNN) architecture to automatically detect intracranial aneurysms on 2D-DSA images. In region localization stage (RLS), our detection system can locate a specific region to reduce the interference of the other regions. Then, in aneurysm detection stage (ADS), the detector could combine the information of frontal and lateral angiographic view to identify intracranial aneurysms, with a false-positive suppression algorithm. Results: Our study was experimented on posterior communicating artery (PCoA) region of internal carotid artery (ICA). The data set contained 241 subjects for model training, and 40 prospectively collected subjects for testing. Compared with the classical DIP method which had an accuracy of 62.5% and an area under curve (AUC) of 0.69, the proposed architecture could achieve accuracy of 93.5% and the AUC of 0.942. In addition, the detection time cost of our method was about 0.569 s, which was one hundred times faster than the classical DIP method of 62.546 s. Conclusion: The results illustrated that our proposed two-stage CNN-based architecture was more accurate and faster compared with the existing research studies of classical DIP methods. Overall, our study is a demonstration that it is feasible to assist physicians to detect intracranial aneurysm on DSA images using CNN.

Advanced metering infrastructure (AMI) is a vital component of the smart grid (SG) for real-time data access and bidirectional communication. Authentication and Key Agreement (AKA) protocol is needed for AMI systems to ensure the confidentiality and integrity of communication data. Since the devices are connected to open network and generally deployed outdoors with limited computation, communication and storage, designing a suitable AKA protocol is a challenging task. Researchers are still looking for good ways to make SG secure and efficient simultaneously. To remedy the situation, we advance a security-enhanced ECC-based AKA protocol, and the security has been proven rigorously under the random oracle model and verified with ProVerif tool. Further, performance comparison validates the proposed protocol in affording improved security features with lower computation and communication cost. In additional, the proposed scheme is implemented practically on a testbed, which deployed using Raspberry Pi 3 Model B+ for smart meters.

With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data. First, we integrate the incentives based on reputation and payment and introduce “credibility coins” as a cryptocurrency for data-sharing transactions, to encourage users to participate honestly in the data-sharing process based on federated learning. Second, we propose a dynamic incentive model based on the evolutionary game theory to model the game process of users in data sharing and analyze the stability of their strategies. Finally, based on the results of this analysis, we use the blockchain-based smart contract technology to dynamically adjust the participation benefits of users under different conditions in order to promote users to join consortium blockchains more often and steadily to participate in model training for federated learning and obtain better model accuracy. Our work is the first to apply the evolutionary game theory to the study of incentives in federated learning, and plays a leading role in the study of incentives in federated learning. Experimental simulation validation shows that our DIM-DS model can adequately motivate users to participate in the collaborative task of data sharing and maintain stability. The model can maximize the effectiveness of the federated learning model.