Massive devices have become a bottleneck restricting the improvement of computing networks. In this paper, we investigate massive multiple access (MA) schemes in low earth orbit satellite-unmanned aerial vehicle (UAV) integrated multi-tier computing networks to achieve higher connectivity. A game-theoretic MA approach is proposed for sum data minimization at UAVs. This approach is divided into two parts: data node (DN)-UAV assignment and UAV-satellite matching. In the first part, the DNs’ orthogonal MA process is formulated as a coalition formation game (CFG) with the derived optimal bandwidth allocation. The CFG with the proposed Max-Satisfaction order is proofed as an exact potential game, which can maximize the satisfaction between the collected data and UAVs’ real computing capacities. Besides, the overlapping CFG (OCFG) is utilized for higher satisfaction. In UAV-satellite matching, non-orthogonal MA-based sum rate maximization with imperfect successive interference cancellation is obtained by transmit power optimization firstly. The UAV-satellite matching process is formulated as a many-to-one matching game (MTOMG). A swap-based MTOMG algorithm is proposed for UAVs grouping. Finally, the simulation results show the proposed schemes have better performance than other schemes, and the proposed approach remains the lowest data at UAVs compared to other approaches.

We discuss how resilient satellite constellation networks are against attacks. Two types of robustness are focused in this paper. One is the topology-related network robustness, which mainly assesses the effect of attacks or faults on satellites and links. The other is the network function robustness related to routing mechanisms, which mainly assesses how resource allocation mechanisms affect network robustness. To this purpose, two satellite constellation network models based on physical network topology and traffic are proposed, along with a new satellite importance index and the robustness metrics. Based on the newly proposed satellite importance index, three different types of attacking strategies are implemented in this paper, i.e., random attacks, selective attacks based on the initial state of the network, and selective attacks based on the current state of the network. The routing methods involved in function robustness mainly include different local state routing algorithms, which we summarize into a brand-new routing model with tunable parameters. Simulation results show that the removals by the selective attacks based on the current state of the network are often more harmful than the other attack strategies. Constellation networks almost collapse for the above attack strategies as the attack ratio are nearly 0.6, 0.4, and 0.2 respectively. Meanwhile, the larger constellation is more robust than the smaller one against the attacks before collapses. However, the larger constellation collapses earlier for the selective attack strategies. In addition, local-state routing algorithms with a certain level of state awareness capability can be used to improve the network function robustness.

Satellite-enabled Internet of Things (IoT) (SatIoT) has drawn increasing attentions due to the ubiquitous coverage, high capacity and massive connectivity. The inherent openness and broadcast nature of the SatIoT are vulnerable to security threats, particularly the jamming attacks for interrupting transmissions. Nonorthogonal multiple access (NOMA) scheme has the potential to be applied in anti-jamming communication for SatIoT due to the characteristic of resource sharing. The severely jammed users can get more allocated power by forming NOMA groups with other users, and both parties can improve the spectrum efficiency by frequency sharing. In this article, we aim to improve the performance of sum rate for SatIoT under the jamming environments. An anti-jamming transmission scheme is developed by jointly considering the NOMA-based user grouping and the power allocation (PA) for each NOMA group. Specifically, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as an anti-jamming coalition formation game, and the equilibrium solution is proved by the exact potential game theory. Moreover, in order to further improve NOMA performance, we derive the PA solution for multiuser NOMA by considering the imperfect successive interference cancellation. Finally, simulation results briefly highlight some details of the proposed approaches.

As an emerging technology, mobile edge computing (MEC) network paradigm provides great computing potential for edge services, which has been widely applied in friendly city environment. However, there are still many challenges to deploy MEC technology in harsh tactical communication environment due to poor communication conditions, limited computational resources, and hostile malicious interference. Thus, this article investigates the computational resource pricing and task offloading strategy in tactical MEC ad-hoc network, which consists of multiple tactical edge nodes, ground MEC servers, unmanned aerial vehicle-MEC (UAV-MEC) servers and a low-Earth orbit-MEC (LEO-MEC) satellite server. Each edge node can offload its partial computation-intensive task to the MEC servers to reduce computational delay and energy consumption. First, a multileader and multifollower Stackelberg game (MLMF-SG) which includes leader subgame for MEC servers and follower subgame for edge nodes, is proposed to formulate the interaction between servers and edge nodes. It has been proved that there exists a Stackelberg equilibrium (SE) in the proposed MLMF-SG. In order to decrease the delay, energy consumption, and resource overhead, the follower subgame is further formulated as a multimode computation task offloading game. With the help of the exact potential game (EPG), we prove that the follower subgame can converge to the Nash equilibrium (NE). To achieve the SE, a hierarchical distributed iterative algorithm is designed to maximize the utilities of the leaders and followers. Finally, the simulation results demonstrate that the proposed scheme can achieve better performance compared with the existing schemes.

In this article, we investigate max completion time optimization for Internet of Things (IoT) in LEO satellite-terrestrial integrated networks (STINs), in which IoT devices use non-orthogonal multiple access (NOMA) scheme to transmit data to central earth stations (CESs), and orthogonal multiple access (OMA) scheme is used for data transmission from CESs to LEO satellite. We decouple this problem into two subproblems: 1) max completion time optimization in terrestrial networks and 2) max completion time optimization among satellite beams. Different from the existing works about NOMA data transmission in terrestrial networks, we propose a cooperative NOMA scheme, and derive the closed expressions of the optimal cooperative data and the optimal transmit power of IoT devices. Based on the closed-form expressions, a joint subcarrier assignment and cooperative NOMA pairing (JSACNP) approach is proposed to minimize the max completion time in terrestrial networks by utilizing matching theory. Then, to minimize the max completion time among satellite beams, the optimal linear receiver expression is derived with fixed transmit power. Convex optimization is utilized to solve transmit power optimization, we propose an algorithm to solve it by CVX tool. An iterative algorithm is proposed for improved performance. Finally, numerical results are provided to evaluate our proposed algorithms, compared with some other proposed approaches or algorithms.