Multi-access Edge Computing (MEC) technology has been widely considered as a paradigm for offloading computation-intensive and latency-sensitive tasks from ground devices (GDs). However, the deployment of ground edge servers to provide ubiquitous computing services for GDs may incur extremely high costs, primarily due to the uneven distribution of devices and the complexity of geographical environments. As a promising solution, deploying edge servers on Low Earth Orbit (LEO) satellites can offer superior coverage and reduced latency for processing remote terrestrial computational tasks. Nevertheless, considering the limitations in computing resources and energy supply on LEO satellites, it is crucial to enhance task processing efficiency while taking energy consumption into account. In this paper, we consider a multi-satellite cooperative edge computing network (MSCECN) where time-sensitive tasks can be offloaded to an Access Satellite (AS) and multiple Edge Satellites (ESs) through Inter-Satellite Links (ISLs). We formulate a delay-minimization offloading problem by jointly optimizing satellite selection, computing resource allocation, task scheduling, and transmit power control. To solve this problem effectively, we propose a novel joint iterative algorithm called Multi-Satellite Cooperative Resource Allocation (MSCRA), which integrates the relaxation method, the Lagrangian multiplier framework, and the CVX toolbox. Simulation results demonstrate that the proposed optimization scheme achieves a 35.5% reduction in total delay compared to benchmark algorithms and effectively minimizes offloading latency across various resource constraints.

Community engagement plays a critical role in anti-poaching efforts, yet existing mathematical models aimed at enhancing this engagement often overlook direct participation by community members as alternative patrollers. Unlike professional rangers, community members typically lack flexibility and experience, resulting in new challenges in optimizing patrol resource allocation. To address this gap, we propose a novel game-theoretic model for community-participated patrol, where a conservation agency strategically deploys both professional rangers and community members to safeguard wildlife against a best-responding poacher. In addition to a mixed-integer linear program formulation, we introduce a Two-Dimensional Binary Search algorithm and a novel Hybrid Waterfilling algorithm to efficiently solve the game in polynomial time. Through extensive experiments and a detailed case study focused on a protected tiger habitat in Northeast China, we demonstrate the effectiveness of our algorithms and the practical applicability of our model.

Ensuring massive access for a large number of devices is essential to realize the full potential of satellite Internet of Things (IoT), promising to bridge connectivity gaps and provide services in previously unreachable regions. However, traditional terrestrial access methods have been found inadequate for satellite-terrestrial communications, primarily due to their inability to account for the unique characteristics of space-ground links, such as signal propagation delays and the dynamic nature of satellite orbits. To tackle this issue, we propose an innovative access scheme based on grant-free random access. In order to meet the demand for simultaneous access of multiple terminals, joint detection and parameter estimation are carried out by collaborative satellites, through the linear minimum mean square error (LMMSE) algorithm and Newton's algorithm. Then, this scheme empowers users to select several short sequences to form their own preamble signal. The new scheme can meet the access requirements of a large number of users while reducing computational complexity and the impact of carrier frequency offset (CFO). Additionally, choosing multiple short sequences can effectively lower the probability of preamble conflict. Simulation results demonstrate that the collision probability of the proposed strategy can be reduced by 70 percent and the detection probability can be improved by 38 percent with an equal number of users and preambles.

Low-earth orbit (LEO) satellite Internet of Things (IoT) has emerged as a promising solution to address the limitations of terrestrial IoT by providing global coverage and seamless connectivity. Among the various techniques enhancing LEO satellite IoT, beam hopping (BH) stands out as an efficient approach that dynamically adjusts beam illumination to match the varying traffic demands of diverse IoT devices. This flexibility enables optimal utilization of limited on-board resources. However, while BH allows adaptive beam illumination planning, it can also introduce severe inter-beam interference, particularly when adjacent beams are simultaneously activated. To address this challenge, we propose a novel rate-splitting multiple access (RSMA)-enabled cluster-based beam hopping (CBH) LEO satellite IoT system. By leveraging RSMA, the proposed framework supports large-scale IoT devices access, and mitigates inter-beam interference introduced by CBH. Within this framework, we introduce a metric—the ratio of offered capacity to traffic demand (ROCD)—to quantify how well the required traffic sum rate aligns with the achievable sum rate for each beam. We then focus on jointly optimizing the precoding vector, common rate allocation, and CBH pattern design to maximize the worst-case ROCD among beams. To solve this problem efficiently, we decompose the original problem into three sub-problems and propose a two-stage algorithm. Numerical results demonstrate that our proposed scheme improves the minimum satisfaction rate by 14.10% and 39.59% compared to the non-orthogonal multiple access and space-division multiple access baselines, achieving effective interference mitigation.

Aiming at the privacy leakage problem of traditional differential privacy function in correlated datasets, a novel improved method based on machine learning and maximum information coefficient (MIC) was proposed, which improved the difference privacy of correlated datasets in big data. On this basis, the r-CBDP (r-Correlated Block Differential Privacy) protection model was proposed. Firstly r-CBDP used MIC and machine learning to determine the dependencies between correlated datasets, accurately calculated the correlated sensitivity of the query function, and then used clustering to divide big data into independent blocks, and implemented r-CBDP protection of data blocks to achieve the whole big data correlated differentiated privacy protection.