Improved bamboo algorithm based on hierarchical network model

Tian, Zengming; Wen, Xiangming; Sun, Yong; Zheng, Wei; Cheng, Yinbo

doi:10.1109/cccm.2009.5270447

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Among the first DHTs suggested for MANETs were Ekta [23] which uses underlay routing information to choose links (PNS) and make overlay routing decisions (PRS), MADPatry [34] which uses landmark nodes to form location-based node clusters that share nodeID prefixes (PIS with node movements), and CHR [1] in which geographic clusters act as nodes using geographic routing as in GHT [25]. Basically, DHTs in MANETs employ some combination of cross-layer PIS [22,14,30,33], PRS [19], and PNS [7], using network layer (i.e. underlay) information to augment overlay decisions.…”

Section: Related Workmentioning

confidence: 99%

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A hierarchical approach to resource awareness in DHTs for mobile data management

Ribe-Baumann

Sattler

2014

Pervasive and Mobile Computing

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Data is increasingly distributed across networks of mobile nodes such as wireless sensor networks, distributed smartphone applications, or ad hoc recovery networks in disaster scenarios, but must still be reliably collected, stored, and retrieved. While such networks run in either ad hoc mode or use existing infrastructure, all of them must deal with node heterogeneity. Wireless nodes invariably have differing levels of power availability, and often varying connectivity and computing power. While many distributed hash tables (DHTs) have been designed for mobile ad hoc or heterogeneous networks, they do not consider differences in node strength, or resource availability, for an arbitrary number of resource availability levels. In this paper, we present a scalable, location aware, hierarchical DHT that utilizes nodes' varying resource availability levels to increase and prolong the mobile network's data storage and retrieval capabilities. Furthermore, we compare this DHT to other location aware flat and hierarchical approaches, examining their structures' suitability for nodes with varying resource availability.

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Section: Related Workmentioning

confidence: 99%

“…underlay) information to augment overlay decisions. Many of these overlays can be considered hierarchical [22,30,33,34], in part due to their clustered structures.…”

Section: Related Workmentioning

confidence: 99%