Novel Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks

Journal article

Mir, F. and Meziane, F. 2024. Novel Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks. Expert Systems with Applications. 247, pp. 1-23. https://doi.org/10.1016/j.eswa.2024.123212
AuthorsMir, F. and Meziane, F.
Abstract

Clustering in the Internet of Things (IoT) involves organizing devices into groups to streamline network management and optimize resource utilization, including
Internet connections, energy usage, coverage, quality of service, and connectivity. DCOPA (A Distributed Clustering Based on Objects Performances Aggregation for Hierarchical Communications in IoT Applications) is a recent distributed clustering protocol based on a timer for cluster formation where the election of Cluster Heads (CHs) is modeled as a multicriteria problem. In this paper, three contributions are presented. Firstly, the DCOPA protocol is analyzed with a focus on its multi-criteria aggregation function T(i) which directly contributes to the election of the CHs and the formation of the network’s clusters. This is then followed by an in-depth analysis of the impact and the variation of the weights assigned to the two aggregated criteria which are the
energy and the distance from the base station. A verification of the scalability, load balancing and distribution of the clusters and CHs will follow. Secondly, a new formal notation for the performance analysis, specifically focusing on the
mortality and lifetime based on the Vector of Performance Indicators (VPI ), will be introduced for IoT. As a third contribution, a revised version of DCOPA is introduced called ADCOPA (Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks). ADCOPA is based on a new property which is the dynamicity or the variability of the weights of the criteria used in the election function of CHs. The simulation results show that the ADCOPA algorithm, which dynamically adjusts the weights of the criteria during the network’s lifetime, outperforms the DCOPA algorithm. The latter uses static weights for the criteria that remain unchanged for the entire lifetime of the network. This confirms that the ability to dynamically adjust the weighting of the criteria is an important factor in achieving better performance.

KeywordsIoT; DCOPA; Clustering; Data Communication; Energy Efficiency; Multi-criteria Analysis; Dynamic Weights of Criteria; VPI
Year2024
JournalExpert Systems with Applications
Journal citation247, pp. 1-23
PublisherElsevier
ISSN1873-6793
0957-4174
Digital Object Identifier (DOI)https://doi.org/10.1016/j.eswa.2024.123212
Web address (URL)https://www.sciencedirect.com/science/article/abs/pii/S0957417424000770
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Output statusPublished
Publication dates
Online19 Jan 2024
Publication process dates
Accepted09 Jan 2024
Deposited16 Jan 2024
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Mir, F, Bounceur, A and Meziane, F. 2014. Regression analysis for energy and lifetime prediction in large wireless sensor networks. 2014 International Conference on Advanced Networking Distributed Systems and Applications. Bejaia, Algeria 17 - 19 Jun 2014 IEEE. https://doi.org/10.1109/inds.2014.8
The effect of a different environment on providing personal information when engaging in E-commerce : the case of Saudis in the UK
Alshehri, H and Meziane, F. 2014. The effect of a different environment on providing personal information when engaging in E-commerce : the case of Saudis in the UK. 22nd IBIMA Conference. Rome, Italy 13 - 14 Nov 2013 IBIMA.
The Effect of recommendations from family members and friends and third party endorsement in B2C E-Commerce: The case of Saudi Arabia
Alshehri, H. and Meziane, F. 2014. The Effect of recommendations from family members and friends and third party endorsement in B2C E-Commerce: The case of Saudi Arabia. 24th International Business Information Management Association Conference - Crafting Global Competitive Economies: 2020 Vision Strategic Planning and Smart Implementation. Milan, Italy 06 - 07 Nov 2014 IBIMA.