A Novel Approach for RPL Assessment Based on the Objective Function and Trickle Optimizations
The ROLL working group proposed the RPL (IPv6 routing protocol for low-power and lossy networks) to respond to the requirements of low-power and lossy networks (LLNs). In this paper, we propose a new approach to assess the RPL performances based on its main components, namely, the objective function...
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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2019/4605095 |
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doaj-7a5e745eafb94c04af85e6e4bedd61352020-11-25T01:33:48ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772019-01-01201910.1155/2019/46050954605095A Novel Approach for RPL Assessment Based on the Objective Function and Trickle OptimizationsHanane Lamaazi0Nabil Benamar1Faculty of Sciences, University of Moulay Ismail, Meknes, MoroccoSchool of Technology, University of Moulay Ismail, Meknes, MoroccoThe ROLL working group proposed the RPL (IPv6 routing protocol for low-power and lossy networks) to respond to the requirements of low-power and lossy networks (LLNs). In this paper, we propose a new approach to assess the RPL performances based on its main components, namely, the objective function (OF) and the trickle algorithm. To this end, we compare between the RPL-EC (RPL based combined ETX and energy consumption) and the RPL-FL (RPL based on the flexible trickle algorithm). This paper compares the two implementations to assess the weight of each proposed improvement against the standard RPL. The results show that RPL performances are greatly influenced by the change of both the objective function and trickle algorithm. RPL-FL provides best values in terms of overhead, packet delivery ratio (PDR), and energy consumption, while RPL-EC acts better with the convergence time and the network lifetime compared to the standard RPL.http://dx.doi.org/10.1155/2019/4605095 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hanane Lamaazi Nabil Benamar |
spellingShingle |
Hanane Lamaazi Nabil Benamar A Novel Approach for RPL Assessment Based on the Objective Function and Trickle Optimizations Wireless Communications and Mobile Computing |
author_facet |
Hanane Lamaazi Nabil Benamar |
author_sort |
Hanane Lamaazi |
title |
A Novel Approach for RPL Assessment Based on the Objective Function and Trickle Optimizations |
title_short |
A Novel Approach for RPL Assessment Based on the Objective Function and Trickle Optimizations |
title_full |
A Novel Approach for RPL Assessment Based on the Objective Function and Trickle Optimizations |
title_fullStr |
A Novel Approach for RPL Assessment Based on the Objective Function and Trickle Optimizations |
title_full_unstemmed |
A Novel Approach for RPL Assessment Based on the Objective Function and Trickle Optimizations |
title_sort |
novel approach for rpl assessment based on the objective function and trickle optimizations |
publisher |
Hindawi-Wiley |
series |
Wireless Communications and Mobile Computing |
issn |
1530-8669 1530-8677 |
publishDate |
2019-01-01 |
description |
The ROLL working group proposed the RPL (IPv6 routing protocol for low-power and lossy networks) to respond to the requirements of low-power and lossy networks (LLNs). In this paper, we propose a new approach to assess the RPL performances based on its main components, namely, the objective function (OF) and the trickle algorithm. To this end, we compare between the RPL-EC (RPL based combined ETX and energy consumption) and the RPL-FL (RPL based on the flexible trickle algorithm). This paper compares the two implementations to assess the weight of each proposed improvement against the standard RPL. The results show that RPL performances are greatly influenced by the change of both the objective function and trickle algorithm. RPL-FL provides best values in terms of overhead, packet delivery ratio (PDR), and energy consumption, while RPL-EC acts better with the convergence time and the network lifetime compared to the standard RPL. |
url |
http://dx.doi.org/10.1155/2019/4605095 |
work_keys_str_mv |
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1725075737928531968 |