随着物理信息系统的蓬勃发展，高时效性信息的传输机理受到了广泛关注。一般来说，越新鲜的信息越有利于系统做出正确的控制决策，然而受到频谱资源的制约，盲目频繁的更新会导致网络的拥塞。因此，信息新鲜度与网络负载之间存在矛盾关系，需要设计合理的更新策略及传输策略找到性能折中点。然而，基于传统网络性能指标(时延，吞吐量)的优化设计无法捕捉信息的这一特性。近年来，信息年龄作为一个新的性能指标被用来衡量信息的新鲜度。迄今，对信息年龄的研究大多集中于单跳网络中的理论分析。本项目以车联网为应用场景，创建多跳场景下的信息新鲜度时空演变模型。基于该模型，利用排队论及概率论，研究更新策略与传输策略的联动机制。针对传输策略，通过设计基于空间自由度的协作传输方案，探索用户间不同程度协作下可达信息新鲜度。最后针对此类模型，探索分布式智能优化算法，以实现具备可扩展性的决策方案，为下一代高实时性网络的发展提供理论依据。

As the emergence of cyber-physical systems, real-time status updates have become an important research area. Information freshness is vital to the overall system performance. However, as constrained by limited spectrum resources, frequent updates may result in network congestion. The tension had led to the introduction of a new performance metric called Age of Information, which measures how timely is one’s knowledge of the system. AoI based optimization involves joint consideration of the data generation rate and transmission rate. Most existing works focus on the mathematical understanding of AoI in single-hop networks. In this project, taking VANET as the application scenario, we first derive the general expression of AoI in multi-hop networks and construct the temporal evolution model of AoI. Based on the model, we further analysis the relationship between update policy and transmission policy. Then we aim to explore the achievable AoI under different transmission scheduling schemes by employing DoF-based modeling for MIMO. At last, we will develop fast converging distributed optimization algorithms that can offer performance approaching the theoretical limits. All in all, the results of this project can provide some theoretical insights for next generation time-critical networks.