Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL

In dynamic network conditions, most routing protocols periodically send additional control messages, which allow the network to quickly adjust itself to topological changes and keep the routing table up to date. In resource‐constrained devices, these communication overheads should be controlled in o...

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Main Authors: Ainaz Bahramlou, Reza Javidan
Format: Article
Language:English
Published: Wiley 2018-09-01
Series:IET Networks
Subjects:
Online Access:https://doi.org/10.1049/iet-net.2017.0192
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spelling doaj-4ca646e8e87a49529f16182edc6e5dce2021-09-08T13:49:13ZengWileyIET Networks2047-49542047-49622018-09-017530631210.1049/iet-net.2017.0192Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPLAinaz Bahramlou0Reza Javidan1Department of Computer Engineering and Information TechnologyShiraz University of TechnologyShirazIranDepartment of Computer Engineering and Information TechnologyShiraz University of TechnologyShirazIranIn dynamic network conditions, most routing protocols periodically send additional control messages, which allow the network to quickly adjust itself to topological changes and keep the routing table up to date. In resource‐constrained devices, these communication overheads should be controlled in order to conserve battery. In this study, the authors focused mainly on routing protocol for low power and lossy network (RPL), IPv6 RPL networks, which is fully compatible with limited resources and Internet of things expectations. They proposed a dynamic method in order to extract the most suitable value for the frequency of executing the objective function which are rules governing how to construct a network graph. In this method, first nodal data is collected and aggregated along the path to the sink, and then a novel metric is used to determine the degree of the environmental changes and control the signalling overhead in RPL routing protocol. They evaluated their scheme with the Contiki operating system and Cooja simulator. They aimed to decrease the packet loss ratio, routing overhead and energy consumption. The simulation results showed the effectiveness of the proposed method.https://doi.org/10.1049/iet-net.2017.0192adaptive timing modeldata aggregationInternet of things networksdynamic network conditionscontrol messagesresource‐constrained devices
collection DOAJ
language English
format Article
sources DOAJ
author Ainaz Bahramlou
Reza Javidan
spellingShingle Ainaz Bahramlou
Reza Javidan
Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL
IET Networks
adaptive timing model
data aggregation
Internet of things networks
dynamic network conditions
control messages
resource‐constrained devices
author_facet Ainaz Bahramlou
Reza Javidan
author_sort Ainaz Bahramlou
title Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL
title_short Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL
title_full Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL
title_fullStr Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL
title_full_unstemmed Adaptive timing model for improving routing and data aggregation in Internet of things networks using RPL
title_sort adaptive timing model for improving routing and data aggregation in internet of things networks using rpl
publisher Wiley
series IET Networks
issn 2047-4954
2047-4962
publishDate 2018-09-01
description In dynamic network conditions, most routing protocols periodically send additional control messages, which allow the network to quickly adjust itself to topological changes and keep the routing table up to date. In resource‐constrained devices, these communication overheads should be controlled in order to conserve battery. In this study, the authors focused mainly on routing protocol for low power and lossy network (RPL), IPv6 RPL networks, which is fully compatible with limited resources and Internet of things expectations. They proposed a dynamic method in order to extract the most suitable value for the frequency of executing the objective function which are rules governing how to construct a network graph. In this method, first nodal data is collected and aggregated along the path to the sink, and then a novel metric is used to determine the degree of the environmental changes and control the signalling overhead in RPL routing protocol. They evaluated their scheme with the Contiki operating system and Cooja simulator. They aimed to decrease the packet loss ratio, routing overhead and energy consumption. The simulation results showed the effectiveness of the proposed method.
topic adaptive timing model
data aggregation
Internet of things networks
dynamic network conditions
control messages
resource‐constrained devices
url https://doi.org/10.1049/iet-net.2017.0192
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AT rezajavidan adaptivetimingmodelforimprovingroutinganddataaggregationininternetofthingsnetworksusingrpl
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