###### Abstract
Hence, in this Special Issue of _Symmetry_, we invited original research investigating 5G/B5G/6G, deep learning, mobile networks, cross-layer design, wireless sensor networks, cloud computing, edge computing, Internet of Things, software-defined networks, or network security and privacy, which are relevant to Prof. [PERSON]'s research fields. After a rigorous peer-review process, we choose six high-quality research studies to be published in this Special Issue:  
1. A Survey on Resource Management for Cloud Native Mobile Computing: Opportunities and Challenges (belong to the domains of mobile networks and cloud computing). 2. A Genetic Algorithm for the Wait-able Time-Varying Multi-Depot Green Vehicle Routing Problem (belong to the domains of deep learning and Internet of Things). 3. A Secure Interoperability Management Scheme for Cross-Blockchain Transactions (belong to the domain of network security and privacy). 4. Research and Application of Improved Clustering Algorithm in Retail Customer Classification (belong to the domain of deep learning). 5. Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks (belong to the domains of deep learning and software-defined networks). 6. Applying Federated Learning in Software-Defined Networks: A Survey (belong to the domains of deep learning and software-defined networks). First, in the paper \"A Survey on Resource Management for Cloud Native Mobile Computing: Opportunities and Challenges\" [1], [PERSON] et al. investigated issues related to cloud native mobile computing, focusing on resource management issues of network slicing and containerization. The authors first reviewed the latest developments in cloud native technology, combined with mobile communication resource allocation, in which it is divided into four categories, i.e., virtualized network functions, network slicing, containers and resource management with software. The comparison and analyses of existing literatures are presented in terms of core techniques, underlying design concepts, strengths and weaknesses. The authors claimed that cloud native still has several significant challenges to overcome. First of all, as cloud native provides flexibility and scalability for deployment, the overall management and control will be more complicated than traditional cloud. In the view of microservice, it will be deleted or added over time, seriously affecting system performance and enhancing difficulty in detecting faults. Secondly, since using VMs (virtual machines) may have high energy consumption and waste computing resources due to running the same operations by various guest operating systems, the container technique will be a good solution to solve the energy consumption and computing resource-wasting problem. Thirdly, the software implementation of low-density parity-check (LDPC) decoding in the 5G physical layer is challenging due to its iterative and complex processing. Therefore, it may need to consume very high power to achieve the expected performance of 5G mobile networks. Second, in the paper \"A Genetic Algorithm for the Wait-able Time-Varying Multi-Depot Green Vehicle Routing Problem\" [2], [PERSON] et al. introduced a mixed-integer programming model to try to minimize all costs incurred in the entire transportation process, considering the impact of time-varying speed, loading, and waiting time on costs. Considering the issue that time is directional, the multi-depot problems investigated in this study are modeled based on asymmetry, making the problem solving more complex. The proposed model adopts a genetic algorithm with simulated annealing to solve the optimal waiting time and path planning problem. In addition, the mutation operator is replaced in the outer layer by a neighbor search approach using a solution acceptance mechanism similar to simulated annealing to avoid a local optimum solution.