With the explosive growth of Internet of Things devices and massive data produced at the edge of the network, the traditional centralized cloud computing model has come to a bottleneck due to the bandwidth limitation and resources constraint. Therefore, edge computing, which enables storing and processing data at the edge of the network, has emerged as a promising technology in recent years. However, the unique features of edge computing, such as content perception, real-time computing, and parallel processing, has also introduced several new challenges in the field of data security and privacy-preserving, which are also the key concerns of the other prevailing computing paradigms, such as cloud computing, mobile cloud computing, and fog computing. Despites its importance, there still lacks a survey on the recent research advance of data security and privacy-preserving in the field of edge computing. In this paper, we present a comprehensive analysis of the data security and privacy threats, protection technologies, and countermeasures inherent in edge computing. Specifically, we first make an overview of edge computing, including forming factors, definition, architecture, and several essential applications. Next, a detailed analysis of data security and privacy requirements, challenges, and mechanisms in edge computing are presented. Then, the cryptography-based technologies for solving data security and privacy issues are summarized. The state-of-the-art data security and privacy solutions in edge-related paradigms are also surveyed. Finally, we propose several open research directions of data security in the field of edge computing.

With the development of wireless communication technology, the need for bandwidth is increasing continuously, and the growing need makes wireless spectrum resources more and more scarce. Cognitive radio (CR) has been identified as a promising solution for the spectrum scarcity, and its core idea is the dynamic spectrum access. It can dynamically utilize the idle spectrum without affecting the rights of primary users, so that multiple services or users can share a part of the spectrum, thus achieving the goal of avoiding the high cost of spectrum resetting and improving the utilization of spectrum resources. In order to meet the critical requirements of the fifth generation (5G) mobile network, especially the Wider-Coverage, Massive-Capacity, Massive-Connectivity, and Low-Latency four application scenarios, the spectrum range used in 5G will be further expanded into the full spectrum era, possibly from 1 GHz to 100 GHz. In this paper, we conduct a comprehensive survey of CR technology and focus on the current significant research progress in the full spectrum sharing towards the four scenarios. In addition, the key enabling technologies that may be closely related to the study of 5G in the near future are presented in terms of full-duplex spectrum sensing, spectrum-database based spectrum sensing, auction based spectrum allocation, carrier aggregation based spectrum access. Subsequently, other issues that play a positive role for the development research and practical application of CR, such as common control channel, energy harvesting, non-orthogonal multiple access, and CR based aeronautical communication are discussed. The comprehensive overview provided by this survey is expected to help researchers develop CR technology in the field of 5G further.

Industrial Internet of Things (IIoT) systems are key enabling infrastructures that sustain the functioning of production and manufacturing. To satisfy the intelligence demands, federated learning has been envisioned as a promising technique for IIoT applications with privacy training requirements. However, research works have shown that, by training the local model on crafted poisoning samples malicious participants can jeopardize the functionalities of the global model. In this article, we propose a robust federated learning method, named RobustFL, in IIoT systems to defend against poisoning attacks. The main idea is that we conduct an adversarial training framework, in which an extra logits-based predictive model is built at the server-side to predict which participant a given logit belongs to. Meanwhile, the federated model is adversarially trained to prevent this predictive behavior, thus mitigating the poisoning attack influences. We evaluate the poisoning attack and our defense method on three benchmark datasets. Experimental results demonstrate the superiority of our proposed method in terms of high accuracy and efficiency in defending against poisoning attacks.

Under complex conditions, the collaborative control capability of UAV swarms is considered to be the key to ensuring the stability and safety of swarm flights. However, in complex environments such as forest firefighting, traditional swarm control methods struggle to meet the differentiated needs of UAVs with differences in behavior characteristics and mutually coupled constraints, which gives rise to the problem that adjustments and feedback to the control policy during training are prone to erroneous judgments, leading to decision-making dissonance. This study proposed a swarm control method for complementary collaboration of UAVs under complex conditions. The method first generates training data through the interaction between UAV swarms and the environment; then it captures the potential patterns of UAV behaviors, extracts their differentiated behavior characteristics, and explores diversified behavior combination scenarios with complementary advantages; accordingly, dynamic behavior allocations are made according to the differences in perception accuracy and action capability to achieve collaborative cooperation; and finally, it optimizes the neural network parameters through behavior learning to improve the decision-making policy. According to the experimental results, the UAV swarm control method proposed in this study demonstrates high formation stability and integrity when dealing with the collaborative missions of multiple types of UAVs.

Mission-oriented UAV networks operate in nonsecure, complex environments with time-varying network partitioning and node trustworthiness. UAV networks are thus essentially asynchronous distributed systems with the Byzantine General problem, whose availability depends on the tolerance of progressively more erroneous nodes in the course of a mission. To address the resource-limited nature of UAV networks, this paper proposes a lightweight asynchronous provable Byzantine fault-tolerant consensus method. The consensus method reduces the communication overhead by splitting the set of local trusted state transactions and then dispersing the reliable broadcast control transmission (DRBC), introduces vector commitments to achieve multivalue Byzantine consensus (PMVBA) for identity and data in a provable manner and reduces the computational complexity, and the data stored on the chain is only the consensus result (global trustworthiness information of the drone nodes), avoiding the blockchain’s “storage inflation” problem. This makes the consensus process lighter in terms of bandwidth, computation and storage, ensuring the longevity and overall performance of the UAV network during the mission. Through QualNet simulation platform, existing practical asynchronous consensus algorithms are compared, and the proposed method performs better in terms of throughput, consensus latency and energy consumption rate.

Edge computing extends the service resources of cloud computing to the edge of the network, providing less latency and higher bandwidth utilization. The relay edge computing wireless network (RECWN) with intelligent edge device can effectively solve the problem of wireless signal transmission distance and attenuation. However, the relay node’s forwarding brings greater risks to the leakage of private information. In this paper, we propose a homomorphic encryption and non-orthogonal multiple access (NOMA) based channel coding scheme to address the problem of stealing private information from users by incompletely trusted relay node or potential malicious nodes. Specifically, the homomorphic encryption algorithm is adopted to protect the privacy of users’ private information. Furthermore, the code domain NOMA and LDPC algorithm are introduced to compensate for the problem of drastic drop in communication performance caused by poor sub-channel conditions, thereby improving the efficiency of data transmission, and guaranteeing the users’ QoS requirements and fairness. The simulation results demonstrate that the proposed channel coding scheme has better performance than the existing approaches in terms of privacy preservation and data transmission efficiency.