Resource constraints and data heterogeneity pose significant hurdles for malicious traffic detection in satellite networks. To address this, we propose STELLAR, a similarity-based federated learning framework tailored for efficient space computing. STELLAR introduces a multi-dimensional similarity metric to dynamically select representative nodes, effectively eliminating computational redundancy. Furthermore, it ensures system robustness and trust through a time-window asynchronous protocol with staleness compensation and a lightweight proxy-based authentication scheme. Evaluations demonstrate that STELLAR outperforms specialized baselines, reducing network-wide energy consumption by 77%–80% while achieving 99.72% detection accuracy in heterogeneous Non-IID environments. These results validate STELLAR as a sustainable and robust solution for distributed security in resource-constrained satellite networks.

In recent years, the 3GPP Cellular Vehicle to Everything (C-V2X) communication protocol has been widely discussed as a standard for vehicle communications. The 3GPP C-V2X defines a mode 4 operation mode, which allows vehicles to directly communicate with each other without relying on base stations or road-side units (RSUs). According to the mode 4 specification, vehicles adopt a semi-persistent scheduling method to select subchannels (i.e., radio resources) by themselves. However, the SPS method will induce a subchannel collision problem due to location changes of vehicles. When subchannel collisions occur, vehicles may not correctly receive broadcast data from their neighbor, and thus transmission reliability cannot be preserved. In this paper, we propose a localized subchannel selection method. The basic idea is that each vehicle infers its moving status by the received information from other vehicles, and then refers to its moving status to select subchannels. According to our simulation results, the proposed scheme can effectively reduce subchannel collision ratio and increase packet reception ratio.

Massive Machine Type Communication (mMTC) is one application scenario of 5G system to provide cellular Internet of Things (IoT) service. Some work is needed to provide efficient IoT service to meet the requirement of low power consumption and coverage extension, since IoT devices are mainly battery powered and installed in a coverage hole. Especially, the power consumption and radio resources are very essential when data packets are repeatedly transmitted to compensate the coverage gap. Considering the available energy in a real system is restricted, this paper proposes a distributed energy-restricted cluster based transmission scheme for mMTC/IoT data transmission, according to the geo-locations of IoT devices to optimize the transmission efficiency. The simulation results show that the proposed transmission scheme with a cluster size limitation can reduce the power consumption at IoT devices and improve the radio resource efficiency of the system, compared with the conventional centralized scheduling algorithm.