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国家自然科学基金(61373040)

作品数:6 被引量:23H指数:2
相关作者:黄传河更多>>
相关机构:武汉大学工业和信息化部计算机与微电子发展研究中心(中国软件评测中心)更多>>
发文基金:国家自然科学基金湖北省自然科学基金更多>>
相关领域:自动化与计算机技术电子电信更多>>

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Quantitative Assessment Model on Path Assignment for SDNs
2016年
Software defined networking(SDN) has emerged as an efficient network technology for lowering operating cost through simplified hardware, software and management. Specific research focus has been placed to achieve a successful carrier grade network with SDN, in terms of scalability, reliability, Qo S and service management. In this paper, graph theory and traffic engineering are used to model the network state transitions and path assignment problem at first. Next, we present a quantitative assessment model on path assignment for a centralized controller to assess different kinds of path assignments, considering optimal path utilization, network load balance, network load volatility, and resource utilization simultaneously. In the end, an example forquantitatively assessing existing different path assignments is detailed to illustrate our proposed model.
WANG XiaomaoHUANG ChuanheSHI JiaoliFAN XiyinZHOU Hao
车载自组网中基于时延的路由策略被引量:2
2019年
有效的数据分发机制是实现车载自组网中各类应用的基础,针对车载自组网中非对称无线传输场景提出一种基于期望传输时延(expected transmission delay,ETD)的路由协议ETD-GPSR。转发节点候选集的建立由确认(ACK)机制和中间节点辅助完成,解决不准确转发节点候选集问题。ETD综合考虑链路质量、链路时延和传输半径,从候选集中选取ETD最小的车辆作为转发节点。仿真结果表明,使用基于ETD的路由策略可以增大数据包投递率,减少平均端对端时延。
王莎黄传河
关键词:车载自组网路由
无人机辅助车联网环境下干扰感知的节点接入机制被引量:15
2019年
在车联网中,链路频繁切换和信道干扰会导致传输时延增加及网络吞吐量下降。引入无人机与地面车辆进行协同通信,构建无人机辅助的空地一体化车联网,采用发送端自主决策的分布式策略,提出了一种干扰感知的节点接入机制。将节点接入问题形式化为一个与链路传输速率、链路切换次数和节点发射功率相关的多目标优化问题,利用对偶分解法将该问题分解为2个凸优化子问题:联合优化链路切换次数与链路传输速率和在保证链路可靠性的前提下优化节点发射功率。仿真实验表明了所提出节点接入机制可有效地改善数据传递率、平均端到端时延以及网络吞吐量。
范茜莹黄传河朱钧宇文少杰
关键词:车联网无人机
Context-Aware Adaptive Routing for Opportunistic Network被引量:1
2015年
Opportunistic networking-forwarding messages in a disconnected mobile ad hoc network via any encountered nodes offers a new mechanism for exploiting the mobile devices that many users already carry. However, forwarding messages in such a network is trapped by many particular challenges, and some protocols have contributed to solve them partly. In this paper, we propose a Context-Aware Adaptive opportunistic Routing algorithm(CAAR). The algorithm firstly predicts the approximate location and orientation of the destination node by using its movement key positions and historical communication records, and then calculates the best neighbor for the next hop by using location and velocity of neighbors. In the unpredictable cases, forwarding messages will be delivered to the more capable forwarding nodes or wait for another transmission while the capable node does not exist in the neighborhood. The proposed algorithm takes the movement pattern into consideration and can adapt different network topologies and movements. The experiment results show that the proposed routing algorithm outperforms the epidemic forwarding(EF) and the prophet forwarding(PF) in packet delivery ratio while ensuring low bandwidth overhead.
WANG XiaomaoHUANG ChuanheZHOU HaoSHI JiaoliHE KaiDAN Feng
Public Auditing for Encrypted Data with Client-Side Deduplication in Cloud Storage被引量:4
2015年
Storage auditing and client-side deduplication techniques have been proposed to assure data integrity and improve storage efficiency, respectively. Recently, a few schemes start to consider these two different aspects together. However, these schemes either only support plaintext data file or have been proved insecure. In this paper, we propose a public auditing scheme for cloud storage systems, in which deduplication of encrypted data and data integrity checking can be achieved within the same framework. The cloud server can correctly check the ownership for new owners and the auditor can correctly check the integrity of deduplicated data. Our scheme supports deduplication of encrypted data by using the method of proxy re-encryption and also achieves deduplication of data tags by aggregating the tags from different owners. The analysis and experiment results show that our scheme is provably secure and efficient.
HE KaiHUANG ChuanheZHOU HaoSHI JiaoliWANG XiaomaoDAN Feng
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