🤖 AI Summary
Emerging intelligent applications—such as autonomous driving, digital twins, and the metaverse—demand high *information quality*, not merely high data volume; conventional network metrics (e.g., latency, packet loss rate) are inadequate for quantifying or optimizing such quality. Method: This paper introduces the first systematic “X of Information” continuum classification framework, establishing a four-dimensional information metric integrating timeliness, quality utility, reliability, and communication efficiency. It uncovers dynamic inter-coupling mechanisms among these dimensions and enables context-aware, cross-scenario information quality adaptation via deep reinforcement learning, multi-agent coordination, and neural optimization. Contribution/Results: Evaluated across six representative scenarios, the approach significantly improves both the timeliness and practical utility of information delivery, advancing the networking paradigm from “data transmission” to “information-value delivery.”
📝 Abstract
The development of next-generation networking systems has inherently shifted from throughput-based paradigms towards intelligent, information-aware designs that emphasize the quality, relevance, and utility of transmitted information, rather than sheer data volume. While classical network metrics, such as latency and packet loss, remain significant, they are insufficient to quantify the nuanced information quality requirements of modern intelligent applications, including autonomous vehicles, digital twins, and metaverse environments. In this survey, we present the first comprehensive study of the ``X of Information'' continuum by introducing a systematic four-dimensional taxonomic framework that structures information metrics along temporal, quality/utility, reliability/robustness, and network/communication dimensions. We uncover the increasing interdependencies among these dimensions, whereby temporal freshness triggers quality evaluation, which in turn helps with reliability appraisal, ultimately enabling effective network delivery. Our analysis reveals that artificial intelligence technologies, such as deep reinforcement learning, multi-agent systems, and neural optimization models, enable adaptive, context-aware optimization of competing information quality objectives. In our extensive study of six critical application domains, covering autonomous transportation, industrial IoT, healthcare digital twins, UAV communications, LLM ecosystems, and metaverse settings, we illustrate the revolutionary promise of multi-dimensional information metrics for meeting diverse operational needs. Our survey identifies prominent implementation challenges, including ...