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Homesick Lévy Walk and Optimal Forwarding Criterion of Utility-Based Routing under Sequential Encounters

  • Akihiro FujiharaEmail author
  • Hiroyoshi Miwa
Part of the Studies in Computational Intelligence book series (SCI, volume 460)

Abstract

The Internet of Things (IoT) is going to develop integrated and organised networks of all things and beings in the world enabling autonomous computing and information communication for the creation of new values in the future. For such networks by IoT that accept a certain level of communication delay, but that must realise highly-reliable message forwarding, Delay Tolerant Network (DTN) gives a possible solution. Recently, DTN has attracted attention as a future network under challenged network environments where communication delay, disruption, and disconnect frequently occurs. In this chapter, we review some routing protocols for efficient message forwarding in DTN. We also review some mobility models often used for simulating motions of mobile nodes to evaluate the performance of DTN. In this review, we propose our mobility model called Homesick Lévy Walk that mimics human mobility patterns of an universal scale-free property of the frequency of human contacts. After this, we also propose our utility-based routing protocol which maximises the expected number of selected relay nodes being likely to encounter a destination node under sequential encounters with nodes. We evaluate the performance of our routing protocol by comparing with some performance measures of some existing routing protocols under the condition that the Homesick Lévy Walk is adopted as mobility model. We show that our protocol is comparable to others in arrival rate of messages under a smaller number of message forwarding.We also find that the performance of our protocol is stable up to a few hundred mobile nodes and tends to be scalable with the number of nodes.

Keywords

Mobile Node Arrival Rate Destination Node Relay Node Buffer Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Graduate School of Science and TechnologyKwansei Gakuin Univ.HyogoJapan

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