In this paper, we propose an innovative architecture for sharing intelligence in low earth orbit (LEO) satellite edge computing networks. Specifically, we adopt the sharing of intel-ligence to satellites via ground stations to improve the response speed of satellites in processing intelligent services. Considering the burden of frequent transmission of intelligent models over unstable ground-satellite links, the satellites share intelligence with each other in the coalition, which greatly reduces the service response delay. In addition, considering the poor generalization of pre-trained intelligent models transmitted by ground stations, we design a model aggregation scheme with differentiated weights. Each coalition appoints a coalition center satellite, tasked with aggregating models and re-sharing them to individual satellites, thereby enhancing model performance. Then, we propose two low-time complexity algorithms to solve the above problems. Finally, the effectiveness and superiority of the proposed schemes are verified through extensive simulations.

Service Customized Networking (SCN) is emerging as an escalating technological trend to address the personalized requirements of services, which are plagued in traditional “best-effort” transmission networks. Organically coordinating heterogeneous domains in Wide Area Networks (WANs) is essential for establishing end-to-end customized service delivery. However, the peer-to-peer centralized communication mode between Au-tonomous Domains (ADs) hinders their connectivity and makes it challenging to support diverse intra-domain routing protocols for the desired Quality of Service (QoS). To achieve customized service scheduling for predictable QoS in WANs, we propose a contract theory-based incentive mechanism. In specific, we first select trusted ADs with high service qualities by computing their reputations through a subjective logic model. The Service Provider (SP) decomposes the overall QoS requirements by domains logically from a global perspective. These decomposed QoS metrics will splice differentiated service capabilities from ADs to obtain the expected end-to-end connection. To address information asymmetry between the SP and ADs, we formulate contribution-reward contract items and devise an optimization problem of maximizing the whole system utility. The optimal contract problem is solved through constraints of individual rationality and incentive compatibility. Simulation results indi-cate the feasibility and effectiveness of our scheme on service customization and economic benefits.

Distributed clouds decentralize cloud resources, moving from a single high-level point in the network to multiple low-level points, allowing for the dynamic distribution of services across the “cloud-edge-end”. Nevertheless, the “best-effort” traditional networks suffer from unpredictable service quality and limited collaboration between services and networks. To address these shortcomings, we present a novel solution named “Network-as-a-Service (NaaS) Tickets,” inspired by traffic tickets in transportation systems to empower distributed clouds with customized service capabilities. Specifically, we first propose NaaS Tickets-enabled service-customized distributed clouds (NT-SCDC) to realize on-demand and service-oriented interconnection in a wide area. To establish a solid connection between services and networks, we introduce an auction-driven matching mechanism for NaaS Tickets. Then, the matching problem is formulated via an online framework MatOnline, which translates the long-term market problem into a series of one-shot auctions for NaaS Tickets. Based on the Vickrey-Clarke-Groves (VCG) mechanism, we develop MatVCG algorithm to handle one-shot matching problems, guaranteeing truthfulness, individual rationality, and social welfare. Moreover, we improve the performance of MatOnline to find the minimum feasible scale-down ratio with reduced budget expenditure. Experimental results demonstrate our algorithm achieves a stable competitive ratio on social welfare, effectively meeting customized demands

The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an - - minimization objective with an norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.