...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on wireless communications >Deep Reinforcement Learning (DRL)-Based Device-to-Device (D2D) Caching With Blockchain and Mobile Edge Computing
【24h】

Deep Reinforcement Learning (DRL)-Based Device-to-Device (D2D) Caching With Blockchain and Mobile Edge Computing

机译：基于BlockChain和移动边缘计算的基于设备到设备（D2D）缓存的深增强学习（DRL）

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Device-to-Device (D2D) caching assists Mobile Edge Computing (MEC) based caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes' sharing willingness. In this paper, a Blockchain-based Cache and Delivery Market (CDM) is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and MEC caching nodes' willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on transactions and prevent frauds, a consensus protocol among the smart contract execution nodes (SCENE) is necessary. To minimize the latency of reaching consensus while guaranteeing its confidence level, we propose partial Practical Byzantine Fault Tolerance (pPBFT) protocol. Further, the model of cache sharing and transaction execution consensus is proposed, and we further formulate caching placement and SCENE selection as Markov Decision Process problems. Due to the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problem. The simulation results show that the proposed schemes outperform conventional solutions in terms of traffic offloading, content retrieval latency, and consensus latency.
机译：设备到设备（D2D）缓存通过与附近用户共享缓存的项目，帮助基于移动边缘计算（MEC）缓存卸载域间流量，而其性能严重依赖于缓存节点的共享意愿。在本文中，提出了一种基于区块的高速缓存和传送市场（CDM）作为分布式缓存系统的激励机制。根据给定的激励机制，通过满足其对缓存共享的预期奖励来保证D2D和MEC缓存节点的意愿。此外，对于分布式CDM，内容交付相关交易由智能合同执行。为实现交易共识并防止欺诈，必要智能合同执行节点（现场）之间的共识议定书。尽量减少达成共识的延迟，同时保证其置信水平，我们提出了部分实际的拜占庭容错（PPBFT）协议。此外，提出了缓存共享和交易执行共识的模型，我们进一步制定了高速缓存的放置和场景选择，作为马尔可夫决策过程问题。由于问题的复杂性和动态，采用了深度加强学习方法来解决问题。仿真结果表明，该建议的方案在交通卸载，内容检索延迟和共识延迟方面优于传统解决方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号