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  5. TrickleTree: A Gossiping Approach To Fast Staggered Scheduling For Data Gathering Wireless Sensor Networks
 
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TrickleTree: A Gossiping Approach To Fast Staggered Scheduling For Data Gathering Wireless Sensor Networks

Author(s)
Bober, Wojciech  
Li, Xiaoyun  
Bleakley, Chris J.  
Uri
http://hdl.handle.net/10197/7079
Date Issued
2010-07-25
Date Available
2015-09-22T13:38:25Z
Abstract
In recent years data gathering has received significant attention as an application of Wireless Sensor Networks (WSNs). Staggered data tree based protocols have been shown to be successful in reducing energy consumption in data gathering scenarios. An important part of staggered protocols is the process of schedule construction. In order to minimize energy consumption, this process must be fast. In this paper we present TrickleTree, a fast distributed protocol for establishing staggered communication schedule. TrickleTree has three functions: to establish routes, i.e. construct a data gathering tree, to establish a staggered communication schedule, i.e. assign time slots to links, and to disseminate the maximal tree depth in the network. To minimize network setup time, TrickleTree combines the neighborhood discovery and scheduling steps. To reduce message overhead, TrickleTree uses adaptive gossiping. The behavior of the proposed approach is evaluated in simulation. The results show up to 90% reduction in schedule setup time and a 50% reduction of duty cycle compared to a flooding approach.
Other Sponsorship
Enterprise Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2010 IEEE
Subjects

Wireless sensor netwo...

Staggered schedule

Schedule construction...

Schedule disseminatio...

DOI
10.1109/SENSORCOMM.2010.40
Language
English
Status of Item
Peer reviewed
Conference Details
4th International Conference on Sensor Technologies and Applications (SENSORCOMM), Venice/Mestre, Italy, 18 - 25 July, 2010
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Owning collection
Computer Science Research Collection
Mapped collections
CASL Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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