Virtual links (VLs) are isolated data tunnels used in Avionics Full-Duplex Switched Ethernet (AFDX) to route Ethernet frames. Each VL is allocated dedicated bandwidth, ensuring reliable communication under adverse conditions. Proper VL configuration can reduce bandwidth consumption by assigning appropriate message flows and setting parameters like maximum transfer unit (MTU) and bandwidth allocation gap (BAG). Previous studies often assume messages with the same source and the destination share the same VL, leading to bandwidth waste. This paper presents a dynamic programming (DP)-based grouping algorithm (DPGA) for low-bandwidth systems and a traversal top-down algorithm (TTDA) for systems with a large number of messages, to enhance bandwidth utilization. Validated on a real dataset, DPGA reduces bandwidth by 19.9% for small-scale messages, and TTDA by 24.7% for large-scale messages compared with a single VL approach. Our methods handle larger message scales and significantly reduce network bandwidth usage compared with previous work.

Large-quantity and high-quality data is critical to the success of machine learning in diverse applications. Faced with the dilemma of data silos where data is difficult to circulate, emerging data markets attempt to break the dilemma by facilitating data exchange on the Internet. Crowdsourcing, on the other hand, is one of the important methods to efficiently collect large amounts of data with high-value in data markets. In this paper, we investigate the joint problem of efficient data acquisition and fair budget distribution across the crowdsourcing and data markets. We propose a new metric of data value as the uncertainty reduction of a Bayesian machine learning model by integrating the data into model training. Guided by this data value metric, we design a mechanism called Shapley Value Mechanism with Individual Rationality (SV-IR), in which we design a greedy algorithm with a constant approximation ratio to greedily select the most cost-efficient data brokers, and a fair compensation determination rule based on the Shapley value, respecting the individual rationality constraints. We further propose a fair reward distribution method for the data holders with various effort levels under the charge of a data broker. We demonstrate the fairness of the compensation determination rule and reward distribution rule by evaluating our mechanisms on two real-world datasets. The evaluation results also show that the selection algorithm in SV-IR could approach the optimal solution, and outperforms state-of-the-art methods.