To enable global coverage and massive connectivity in the 6G era, space-air-ground integrated networks (SAGINs) rely heavily on low Earth orbit (LEO) satellite constellations, which offer wide-area coverage and autonomous inter-satellite links (ISLs). However, the high mobility, dynamic topology, and uneven resource distribution in LEO networks pose significant challenges to service continuity, efficient scheduling, and resource utilization. This paper proposes an innovative multi-layer spatiotemporal cost graph model that captures the time-varying network dynamics through discretized time windows and layered node representations, transforming complex ISL scheduling into a constrained flow optimization problem. Based on this model, we design a priority-based routing (PBR) algorithm, where mission data volume serves as a priority metric for flow allocation. By periodically updating the spatiotemporal graph and re-executing PBR, the system achieves adaptive scheduling through a system-level feedback mechanism. Evaluations on the Globalstar-2, Iridium Next, and Telesat constellations show that the proposed approach improves throughput by 300%, reduces latency by 50%, and increases mission completion rates by 20%, demonstrating significant gains in inter-satellite resource efficiency.

The massive growth of data has brought vigorous vitality to Internet-of-Things (IoT). It has also brought new challenges, such as confidentiality privacy protection and redundant data transmission. Concerning this regard, data aggregation serves as an efficient technique to minimize the transmission frequency among massive objects in smart grid(SG). By aggregating a large amount of the same type of data while satisfying the protection of user privacy. With the advent of the post-quantum era, a good aggregation scheme must provide quantum resistance while ensuring the secure aggregation of ciphertext power data. However, the excessive overhead limits anti-quantum algorithms from being widely used in SG where resource devices are limited. Therefore, it is an important part of the current private data security aggregation technology to find a low cost and lightweight inverse quantum algorithm to achieve user data security aggregation. In this paper, we propose an improved NTRU-based cryptosystem with multidimensional coding, referred to as multidimensional coding NTRU (MC-NTRU). and use the lattice batch signature technique, which improves the efficiency of the scheme while satisfying the anti-quantum attack. Based on these, we design the multidimensional auditable lattice-based privacy-preserving data aggregation scheme(MA-PPDA) for privacy data on resource-limited IoT devices such as smart grids. In addition to this, the scheme achieves fault tolerance of the scheme by adding zeros and random numbers to the user data. The comparative study against existing approaches demonstrates that the proposed scheme not only adheres to critical security aspects including user privacy, data confidentiality, integrity, and authenticity, but also decreases both communication and computational burdens on the system. This makes our scheme particularly apt for IoT environments characterized by constrained device resources.

A K-hedral tiling of a 2D finite domain is a covering of the domain with tiles without gaps or overlaps, where each tile is congruent to one of the K distinct shapes called prototiles. K, the number of prototiles, is preferred to be as small as possible for congruent tiling appearance and reducing fabrication cost, e.g., by molding. Typically, a forward approach is adopted to produce K-hedral tilings by prescribing a set of prototiles and placing prototile instances (i.e., tiles) to cover the input domain. However, the prescribed prototile set may not be sufficient to tile the domain (for small K) or may lead to tiling results with excessive prototiles more than needed (for large K).

Federated learning (FL) has received considerable attention because it allows multiple devices to train models locally without revealing sensitive data. Well-trained local models are transmitted to a parameter server for further aggregation. The dependence on a trusted central server makes FL vulnerable to the single point of failure or attack. Blockchain is regarded as a state-of-the-art solution to decentralize the central server and provide attractive features simultaneously, such as immutability, traceability, and accountability. However, current popular blockchain systems cannot be combined with FL seamlessly. Since all local models should be collected before aggregation, the latency of FL is determined by the slowest device. The consensus process required by blockchain will increase the latency further, especially, when a large block is required for including the model. Moreover, forever-growing blockchain together with models will take up a lot of storage space, making it impractical to be deployed on lightweight devices. To address these problems, we propose a lightweight blockchain TORR for FL. A novel consensus protocol Proof of Reliability is designed to achieve fast consensus while mitigating the impact of stragglers. A storage protocol is designed based on erasure coding and periodic storage refreshing policy. With erasure coding, we take full advantage of the limited storage space of devices. With the periodic storage refreshing policy, we reduce the requirement for storage. Compared to the common blockchain-based FL system, TORR reduces the system latency, overall storage overhead, and peak storage overhead by up to 62%, 75.44%, and 51.77%, respectively.

Nowadays blockchain systems are widely used in many different fields, not only as a kind of payment method, i.e. cryptocurrency, but also as the infrastructure of decentralized applications. A smart contract, which is a program running on the blockchain, e.g. Ethereum, enables decentralized applications without the need for any trusted third party. To deploy a smart contract every miner of the blockchain needs to perform the same function of the smart contract such that they reach a consensus on its final state. Therefore, current blockchain systems are deterministic and non-probabilistic, disallowing any randomness in smart contracts, which is a significant limitation for the applicability of blockchains. A wide range of real-world applications depend on random functions, most obvious examples are games and lottery applications. Various methods have been proposed To address the random number generation problem, such as using a trusted oracle or the block hash. All of those have different disadvantages and advantages. Noticing the lack of concrete guidance for the inclusion of randomness in smart contracts on blockchains, we investigate the state-of-the-art random number generation methods and compare them in several critical aspects including availability, unpredictability, unbiasability, verifiability, scalability, execution time and cost.

To avoid the inconvenience of the deployment of large-scale blockchains, blockchain simulators are used to facilitate blockchain design and implementation. We evaluate state-of-the-art simulators and find that they suffer from low performance and scalability. To build a more general and faster blockchain simulator, we extend an existing blockchain simulator. We add a network module integrated with a network topology generation algorithm and a block propagation algorithm to simulate the block propagation efficiently. We design a binary transaction pool structure and adopt bitwise operations to accelerate the simulation and reduce memory usage. Moreover, we modularize the simulator based on five primary blockchain processes. Significant blockchain elements are implemented in individual modules and can be combined flexibly to simulate different types of blockchains. Experiments demonstrate that the new simulator reduces the simulation time by an order of magnitude and improves scalability, enabling us to simulate more than ten thousand nodes.