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The complexity and isomerization of communication networks have put forth new requirements for cryptographic schemes to ensure the operation of network security protocols. Robust cryptographic schemes have been gradually favored. The robust initialization vector (RIV) instead of the synthetic initialization vector (SIV) was first introduced to support strong security and robust authenticated encryption. This paper first introduces RIV to GCM-SIV1, proposes a robust variant, GCM-RIV1, and proves that it ensures birthday-bound subtle AE (SAE) security and nonce-misuse resistance. Then, to support beyond-birthday-bound (BBB) security with graceful degradation, we introduce another, stronger security variant, GCM-RIV2, and prove that it allows gracefully degrading BBB SAE security in the faulty nonce setting. Finally, the performance of GCM-RIV1 and GCM-RIV2 is discussed and compared. [ABSTRACT FROM AUTHOR]

Computing directed Minimum Spanning Tree ( D M S T ) is a fundamental problem in graph theory. It is applied in a wide spectrum of fields from computer network and communication protocol design to revenue maximization in social networks and syntactic parsing in Natural Language Processing. State-of-the-art solutions are online algorithms that compute  D M S T  for a given graph and a root. For multi-query requirements, the online algorithm is inefficient. To overcome the drawbacks, in this paper, we propose an indexed approach that reuses the computation result to facilitate single and batch queries. We store all the potential edges of  D M S T  in a hierarchical tree in  O (n)  space complexity. Furthermore, we answer the  D M S T  query of any root in  O (n)  time complexity. Experimental results demonstrate that our approach can achieve a speedup of 2–3 orders of magnitude in query processing compared to the state-of-the-art while consuming O(n) index size. [ABSTRACT FROM AUTHOR]

Next-generation Internet-of-Things applications pose challenges for sixth-generation (6G) mobile networks, involving large bandwidth, increased network capabilities, and remarkably low latency. The possibility of using ultra-dense connectivity to address the existing problem was previously well-acknowledged. Therefore, placing base stations (BSs) is economically challenging. Drone-based stations can efficiently address Next-generation Internet-of-Things requirements while accelerating growth and expansion. Due to their versatility, they can also manage brief network development or offer on-demand connectivity in emergency scenarios. On the other hand, identifying a drone stations are a complex procedure due to the limited energy supply and rapid signal quality degradation in air-to-ground links. The proposed method uses a two-layer optimizer based on a pre-trained VGG-19 model to overcome these issues. The non-orthogonal multiple access protocol improves network performance. Initially, it uses a powerful two-layer optimizer that employs a population of micro-swarms. Next, it automatically develops a lightweight deep model with a few VGG-19 convolutional filters. Finally, non-orthogonal multiple access is used to schedule radio and power resources to devices, which improves network performance. We specifically examine how three scenarios execute when various Cuckoo Search, Grey Wolf Optimization, and Particle Swarm Optimization techniques are used. To measure the various methodologies, we also run non-parametric statistical tests, such as the Friedman and Wilcoxon tests. The proposed method also evaluates the accuracy level for network performance of DBSs using number of Devices. The proposed method achieves better performance of 98.44% compared with other methods. [ABSTRACT FROM AUTHOR]

This paper focuses on cooperative multi-task assignment and re-assignment problems when multiple unmanned aerial vehicles (UAVs) attack multiple known targets. A unified multi-objective optimization framework for UAV cooperative task assignment and re-assignment is studied in this paper. In order to simultaneously optimize the losses and benefits of the UAVs, we establish a multi-objective optimization model. The amount of tasks that each UAV can perform and the number of attacks on each target are limited according to the ammunition capacity of each UAV and the value of each target. To solve this multi-objective optimization problem, a multi-objective genetic algorithm suitable for UAV cooperative task assignment is constructed based on the NSGA-II algorithm. At the same time, a selection strategy is used to assist decision-makers in choosing one or more solutions from the Pareto-optimal front. Moreover, to deal with emergencies such as UAV damage and to detect of new targets, a task re-assignment algorithm based on the contract network protocol (CNP) is developed. It can be implemented in real-time while only slightly sacrificing the ability to seek the optimal solution. Simulation results demonstrate that the methods developed in this paper are effective. [ABSTRACT FROM AUTHOR]