当前位置: X-MOL 学术EURASIP J. Wirel. Commun. Netw. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Coalitional graph game for area maximization of multi-hop clustering in vehicular ad hoc networks
EURASIP Journal on Wireless Communications and Networking ( IF 2.6 ) Pub Date : 2022-08-09 , DOI: 10.1186/s13638-022-02149-9
Siwapon Charoenchai, Peerapon Siripongwutikorn

Road traffic information can be utilized in many applications of intelligent transport systems. It can be collected from vehicles and sent over a vehicular ad hoc network (VANET) to roadside units (RSUs) acting as sink nodes. Due to rapid mobility and limited channel capacity in a VANET where vehicles must compete to access the RSUs to report their data, clustering is used to create a group of vehicles to facilitate data transfer to the RSUs. Unlike previous works that focus on cluster lifetime or throughput, we formulate a coalitional graph game for multi-hop clustering (CGG-MC) model to create a multi-hop cluster with the largest possible coverage area for a given transmission delay time constraint to economize on the number of RSUs installed. Vehicles cooperatively form a proper coalition with relation directed graphs among vehicles in a multi-hop cluster to collect, aggregate, and forward data to RSUs instead of individually competing to connect directly to RSUs. Vehicles decide to join or leave the coalition based on their individual utility, which is a weighted function of the coverage area, number of members in the cluster, relative velocities, distance to sink nodes, and transmission delay toward the sink nodes. The distributed-solution approach based on probabilistic greedy merging of coalitions is used to derive the grand coalition, and the probability of grand coalition formation is analyzed by using a discrete-time Markov chain. Our results show that the proposed solution approach yields a \(95\%\) confidence interval of the average utility between 61 and \(68\%\) relative to the maximum utility in the centralized-solutions. Additionally, our CGG-MC model outperforms the non-cooperation model by approximately \(166\%\) in terms of enlarging the coverage network area under a transmission delay time constraint.



中文翻译:

车载自组织网络中多跳聚类区域最大化的联盟图博弈

道路交通信息可用于智能交通系统的许多应用中。它可以从车辆中收集并通过车载自组织网络 (VANET) 发送到充当接收节点的路边单元 (RSU)。由于车辆必须竞争访问 RSU 以报告其数据的 VANET 中的快速移动性和有限的通道容量,集群用于创建一组车辆以促进数据传输到 RSU。与以前关注集群寿命或吞吐量的工作不同,我们制定了一个多跳聚类 (CGG-MC) 模型的联合图游戏,以在给定的传输延迟时间约束下创建一个具有最大可能覆盖区域的多跳集群,以节省关于安装的 RSU 的数量。车辆与多跳集群中车辆之间的关系有向图合作形成适当的联盟,以收集、聚合和转发数据到 RSU,而不是单独竞争直接连接到 RSU。车辆根据其个人效用决定加入或离开联盟,这是覆盖区域、集群中成员数量、相对速度、到汇节点的距离以及到汇节点的传输延迟的加权函数。采用基于联合概率贪婪合并的分布式求解方法推导大联合,并利用离散时间马尔可夫链分析大联合形成的概率。我们的结果表明,所提出的解决方案方法产生了 并将数据转发到 RSU,而不是单独竞争直接连接到 RSU。车辆根据其个人效用决定加入或离开联盟,这是覆盖区域、集群中成员数量、相对速度、到汇节点的距离以及到汇节点的传输延迟的加权函数。采用基于联合概率贪婪合并的分布式求解方法推导大联合,并利用离散时间马尔可夫链分析大联合形成的概率。我们的结果表明,所提出的解决方案方法产生了 并将数据转发到 RSU,而不是单独竞争直接连接到 RSU。车辆根据其个人效用决定加入或离开联盟,这是覆盖区域、集群中成员数量、相对速度、到汇节点的距离以及到汇节点的传输延迟的加权函数。采用基于联合概率贪婪合并的分布式求解方法推导大联合,并利用离散时间马尔可夫链分析大联合形成的概率。我们的结果表明,所提出的解决方案方法产生了 到汇节点的距离,以及到汇节点的传输延迟。采用基于联合概率贪婪合并的分布式求解方法推导大联合,并利用离散时间马尔可夫链分析大联合形成的概率。我们的结果表明,所提出的解决方案方法产生了 到汇节点的距离,以及到汇节点的传输延迟。采用基于联合概率贪婪合并的分布式求解方法推导大联合,并利用离散时间马尔可夫链分析大联合形成的概率。我们的结果表明,所提出的解决方案方法产生了\(95\%\)平均效用在 61 和\(68\%\)之间相对于集中式解决方案中的最大效用的置信区间。此外,我们的 CGG-MC 模型在传输延迟时间约束下扩大覆盖网络区域方面优于非合作模型约\(166\%\)

更新日期:2022-08-10
down
wechat
bug