Prediction-based routing with packet scheduling under temporal constraint in delay tolerant networks

Liu, Guoliang; Krishnamani, Janani; Sunderraman, Rajshekhar; Li, Yingshu

doi:10.1109/pccc.2013.6742793

Cited by 3 publications

(1 citation statement)

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“…Hence, existing research in mobile opportunistic networks focuses on either protocol design to make effective forwarding decisions [5]- [8], or fundamental data dissemination properties [9]- [15] because they provide an answer to what one can expect for optimal performance (throughput, data delivery, etc.). Nevertheless, such investigations can not answer the question regarding whether mobile opportunistic networks are able to support emerging applications such as mobile commerce and disaster relief, which may involve time and location sensitive data disseminations.…”

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confidence: 99%

The Potential of Mobile Opportunistic Networks for Data Disseminations

Wang

Huang

et al. 2016

IEEE Trans. Veh. Technol.

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Mobile opportunistic networks make use of node mobility to provide occasional contact opportunities for mobile devices to deliver data. In this paper, we investigate the inherent properties of data disseminations in mobile opportunistic networks by answering the question regarding how far and how fast a packet can be disseminated in mobile opportunistic networks. This problem is overlooked by the recent research; but it is very important because it reveals the potential of mobile opportunistic networks to support emerging applications such as mobile commerce and emergency services that may involve time and location sensitive information dissemination. Our investigations are taken from the perspectives of small-and large-scale. From the perspective of small-scale, the probability distributions of the minimum time needed by the data to spread to a given region, namely T , are deduced for both the one-copy case and the multiple-copy case. From the perspective of the large-scale, by using the large deviation theory, the probability distributions of T are deduced for both the one-copy case and the multiple-copy case when the destination region is far enough from the data origin.

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confidence: 99%