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A social-based watchdog system to detect selfish nodes in opportunistic mobile networks

机译:基于社会的看门狗系统,用于检测机会移动网络中的自私节点

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Detecting selfish nodes in opportunistic mobile networks can reduce the loss of network resources, thus improve the data delivery performance. Most of existing detection schemes primarily rely on the nodes’ contact records and do not consider their individual and social preferences in their data relaying behavior, which result in long detection time and high communication overhead. In addition, they cannot distinguish the nodes’ selfishness type and degree, which is important because the charge and rewarding mechanisms applied to stimulate different nodes may not be the same. In this paper, we propose a Social-based Watchdog system (SoWatch) in which watchdog nodes analyze messages received from their encountered nodes with respect to their social tie information to identify the nodes’ selfish behavior in message relaying. Meanwhile, the watchdog nodes apply the second-hand watchdog information received from other nodes to improve the detection time and accuracy. Next, we design a reputation system in which watchdog nodes identify selfish nodes based on their direct and indirect watchdog information and distinguish individually and socially selfish nodes. Furthermore, we design a watchdog evaluation module to protect SoWatch against wrong watchdogs disseminated by malicious nodes in which a watchdog node investigates the truthfulness of the indirect watchdogs before applying them. Our experiments using real-world datasets illustrate that SoWatch outperforms a benchmark contact-based watchdog system in terms of detection time by 45% and detection ratio by 10% with less communication overhead.
机译:在机会移动网络中检测自私节点可以减少网络资源的损失,从而提高数据传递性能。大多数现有的检测方案主要依靠节点的联系记录,并且在数据中继行为中不考虑其个人和社会偏好,这导致检测时间长和通信开销高。此外,它们不能区分节点的自私类型和程度,这很重要,因为用于刺激不同节点的收费和奖励机制可能不相同。在本文中,我们提出了一种基于社交的看门狗系统(SoWatch),其中看门狗节点分析从其遇到的节点收到的有关其社交关系信息的消息,以识别该节点在消息中继中的自私行为。同时,看门狗节点应用从其他节点接收的二手看门狗信息,以提高检测时间和准确性。接下来,我们设计一种信誉系统,其中看门狗节点根据其直接和间接看门狗信息识别自私节点,并区分个体和社会自私节点。此外,我们设计了一个看门狗评估模块,以保护SoWatch免受由恶意节点传播的错误看门狗的侵害,其中看门狗节点会在应用间接看门狗之前调查其真实性。我们使用实际数据集进行的实验表明,SoWatch在检测时间缩短了45%,检测率降低了10%的同时,其通信开销也比基于基准的基于接触的看门狗系统优越。

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