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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Online Access: | https://doi.org/10.1049/iet-net.2017.0192 |
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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 |
work_keys_str_mv |
AT ainazbahramlou adaptivetimingmodelforimprovingroutinganddataaggregationininternetofthingsnetworksusingrpl AT rezajavidan adaptivetimingmodelforimprovingroutinganddataaggregationininternetofthingsnetworksusingrpl |
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1717762361365889024 |