Chen Yuzhong, Weng Shining, Guo Wenzhong, Xiong Naixue
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.
Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China.
Sensors (Basel). 2016 Feb 19;16(2):245. doi: 10.3390/s16020245.
Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.
车载自组织网络（VANETs）在城市管理和规划中发挥着重要作用。VANETs中车辆信息的有效整合对于交通分析、大规模车辆路线规划和智能交通调度至关重要。然而，鉴于单个传感器输出信息的精度有限，以及在高度动态的VANETs中各种传感器之间信息共享的困难，在VANETs中有效地进行数据聚合仍然是一个挑战。此外，当前的研究主要集中在大规模环境中的数据聚合，但很少讨论VANETs中簇内数据聚合的问题。在本研究中，我们通过分析传感器节点之间的竞争与合作关系，提出了一种用于VANETs中簇内数据聚合的多智能体博弈论算法。提出了几个以传感器为中心的指标来衡量簇的数据冗余度和稳定性。然后，我们研究效用函数，通过考虑数据冗余度和簇稳定性来实现高效的簇内数据聚合。特别是，我们证明了博弈模型中存在唯一的纳什均衡，并进行了大量实验来验证所提出的算法。结果表明，所提出的算法在准确性和效率方面均优于典型的数据聚合算法。
