Comparative study between metaheuristic algorithms for internet of things wireless nodes localization

Mohammed, Rana Jassim; Abed, Enas Abbas; Elgayar, ostafa Mahmoud

doi:10.11591/ijece.v12i1.pp660-668

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The efficiency of the routing approach is the most important determinant of overall network success in all forms of networks [2,3]. In a WSN, dynamic topology characteristics turn routing into an effective optimization challenge [4].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

SHANTHI¹,

PRASANNA²

2023

Preprint

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The primary goal of wireless sensor networks from a design point of view is to enhance the network's lifetime. Among the different options for reducing operational energy consumption, energy invested in routing and cluster head selection is considered to be very effective mechanisms. Both tasks have been considered as very challenging and difficult to obtain the efficient solution. Since it is difficult for traditional approaches to satisfy the requirements and difficulties, a heuristic solution focused on natural computation methods has provided a lot of naivety. The proposed work efforts to address these challenges using computational intelligence especially differential evolution and genetic algorithm. An energy efficient route discovery for dynamic network is designed with variations in DE, quick and adaptable routes were discovered for networks undergoing changes. A knowledge based DE has been designed depending on prior knowldge to redefine new routes for changing network. A hybrid mutation strategy under standard DE is designed for cluster head selection providing faster convergence characteristics. The proposed solutions were implemented under MATlab environment and the results have shown that the proposed solutions are performing better for different network configurations. Dynamic route discovery using KDE has achieved energy saving of 9.83 to 49.2 percentage as compared to RDE and 6.7 to 29.5 percentage as compared to PDE. Also energy saving attained in cluster head selection using proposed HMDE is 10 to 33 percentage better compared to dyPSO and 5 to 10 percentage better compared to SDE.

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Section: Introductionmentioning

confidence: 99%

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

SHANTHI¹,

PRASANNA²

2023

Preprint

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“…However, 5G may struggle to satisfy the increasing demands for data traffic, spectral efficiency, massive connectivity, and capacity while maintaining fairness [1][2][3][4][5][6]. Additionally, the widespread adoption of the Internet of Things (IoT) presents significant challenges to wireless networks [7]. The exponential rise in data transmission driven by emerging technologies has imposed immense strains on ground-based communication infrastructure, resulting in increased delays and reduced network efficiency.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation in UAV-Enabled NOMA Networks for Enhanced Six-G Communications Systems

El-Gayar,

Ajour

2023

Electronics

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Enhancing energy efficiency, content distribution, latency, and transmission speeds are vital components of communication systems. Multiple access methods hold great promise for boosting these performance indicators. This manuscript evaluates the effectiveness of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) systems within a single cell, where users are scattered randomly and rely on relays for dependability. This paper presents a model for improving energy efficiency, content distribution, latency, and transmission speeds in communication systems using NOMA and OMA systems within a single cell. Additionally, this paper also proposes a caching strategy using unmanned aerial vehicles (UAVs) as aerial base stations for ground users. These UAVs distribute cached content to minimize the overall latency of content demands from ground users while modifying their positions. We carried out simulations using various cache capacities and user counts linked to their respective UAVs. Furthermore, we evaluated OMA and NOMA in terms of the achievable rate and energy efficiency. The proposed model has achieved noteworthy enhancement across various scenarios including different sum rates, numbers of mobility users, diverse cache sizes, and amounts of power allocation.

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“…Radio-frequency circular integrated inductors sizing optimization using … (Imad El Hajjami) 6321 such as artificial bee colony (ABC) [8], [9], genetic algorithms (GA) [10], [11], tabu search (TS) [12], simulated annealing (SA) [13], differential evolution (DE) [14], [15], ant colony optimization (ACO) [16], [17], Jaya algorithm (JA) [18], particle swarm optimization (PSO) [19], [20], salp swarm algorithm (SSA) [21], firefly algorithm (FA) [22], and the gravitational search algorithm (GSA) [23].…”

Section: Introductionmentioning

confidence: 99%

Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques

Hajjami

Benhala

2022

IJECE

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<span lang="EN-US">In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (</span><span lang="EN-US">RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.</span>

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Comparative study between metaheuristic algorithms for internet of things wireless nodes localization

Cited by 6 publications

References 10 publications

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

Energy Efficient Routing and Cluster Head Selection in Wsn Using Different Variant of Differential Evolution

Resource Allocation in UAV-Enabled NOMA Networks for Enhanced Six-G Communications Systems

Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques

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