开花:在机会主义网络中保护隐私的基于集群的路由

Benedikt Kluss, Samaneh Rashidibajgan, Thomas Hupperich
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引用次数: 0

摘要

机会网络是一种没有集中式基础设施的类型学的使能技术。便携式设备,如可穿戴和嵌入式移动系统,向通信范围设备发送中继消息。最关键的挑战之一是在这些网络中找到最优路由,同时保护网络参与者的隐私。针对这一挑战,我们提出了一种基于设备集群的新颖路由算法,减少了总体消息负载并提高了网络性能。同时,通过隐身消除了网络节点可能被识别的信息,以满足隐私要求。我们通过将我们的方法与PRoPHET、First Contact和Epidemic路由算法进行比较,从传统和结构化城市(即威尼斯和旧金山)的机会主义网络的效率和隐私性方面评估了我们的路由算法。在旧金山和威尼斯场景中,Blossom提高了信息传递概率,比PRoPHET、First Contact和Epidemic分别高出46%、100%和160%,分别高出67%、78%和204%。此外,与旧金山的《PRoPHET》和《Epidemic》相比,《Blossom》的掉落信息概率降低了83%,与威尼斯的《PRoPHET》和《Epidemic》相比,这一概率降低了91%。由于生成的消息数量很少,因此该算法的网络开销接近于零。通过集群可以显著降低网络开销,同时保持可靠的消息传递。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blossom: Cluster-Based Routing for Preserving Privacy in Opportunistic Networks
Opportunistic networks are an enabler technology for typologies without centralized infrastructure. Portable devices, such as wearable and embedded mobile systems, send relay messages to the communication range devices. One of the most critical challenges is to find the optimal route in these networks while at the same time preserving privacy for the participants of the network. Addressing this challenge, we presented a novel routing algorithm based on device clusters, reducing the overall message load and increasing network performance. At the same time, possibly identifying information of network nodes is eliminated by cloaking to meet privacy requirements. We evaluated our routing algorithm in terms of efficiency and privacy in opportunistic networks of traditional and structured cities, i.e., Venice and San Francisco by comparing our approach against the PRoPHET, First Contact, and Epidemic routing algorithms. In the San Francisco and Venice scenarios, Blossom improves messages delivery probability and outperforms PRoPHET, First Contact, and Epidemic by 46%, 100%, and 160% and by 67%, 78%, and 204%, respectively. In addition, the dropped messages probability in Blossom decreased 83% compared to PRoPHET and Epidemic in San Francisco and 91% compared to PRoPHET and Epidemic in Venice. Due to the small number of messages generated, the network overhead in this algorithm is close to zero. The network overhead can be significantly reduced by clustering while maintaining a reliable message delivery.
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