A paper recently published in Scientific Reports proposed QPSOFL, a novel clustering and routing protocol (CRP) that combines a fuzzy logic (FL) system and quantum particle swarm optimization (QPSO) for simultaneously extending network lifetime and improving the energy efficiency of wireless sensor networks (WSNs).
2-D representation of benchmark functions. Image Credit: https://www.nature.com/articles/s41598-024-69360-0
Background
CRPs have a critical role in prolonging the lifespan and reducing the energy consumption of WSNs. However, maximizing the network longevity through energy efficiency optimization is a major challenge for these CRPs. Particle swarm optimization (PSO) is preferred for solving NP-hard problems due to its rapid convergence and ease of implementation. Thus, PSO is applied to different tasks in WSNs, such as combined clustering routing and target tracking.
Yet, PSO has significant limitations, including parameter tuning difficulties and premature convergence. Several novel variants of PSO have been developed to improve the optimization performance. Among them, QPSO has emerged as one of the most effective variants.
Importance of QPSO
QPSO demonstrates greater exploratory tendencies for avoiding local optima and superior convergence behavior, resulting in highly optimal solutions. Its update mechanism is simpler and adaptable to different problems. Thus, QPSO is extensively applicable in multi-task allocation and suspension optimization.
Yet, limited search capabilities and low diversity hinder QPSO's efficiency in realizing global optimum solutions. Thus, several QPSO improvements aim to ensure a balance between global and local search capabilities while addressing the challenges of premature convergence.
Despite its advantages, QPSO has not been applied extensively to CRPs in WSNs. PSO can be hybridized with other methods, such as integrating with FL, to improve clustering and routing. This approach leverages the strengths of various algorithms by integrating them to overcome individual limitations and realize superior results in complex problem-solving.
The Proposed Approach
In this work, researchers introduced QPSOFL, a novel CRP that integrates QPSO and an FL system to extend network lifespan and improve the energy efficiency of WSNs. QPSOFL employed an improved QPSO algorithm to select optimal cluster heads (CHs) for cluster formation, using Sobol sequences during initialization for population diversification and an FL system to identify the best/optimal relay CHs for transmission of data.
Additionally, this novel CRP incorporated Lévy flight and Gaussian perturbation-based position updates to avoid trapping in local optima. N homogeneous nodes with distinct IDs were grouped into m clusters in QPSOFL.
Every cluster had only one CH, and every cluster member (CM) belonged to only one cluster. The special node base station (BS) could be deployed outside or within the network, and neither the nodes nor the BS could move once they were deployed. The QPSOFL protocol nominated the most suitable nodes as CHs using the QPSO algorithm. The QPSOFL work primarily consisted of two phases, including CH selection and relay finding.
Benchmark experiments were performed using QPSO, PSO, grey wolf optimization (GWO), and Harris Hawks optimization (HHO), focusing on the convergence speed, search capability, and accuracy of all methods to validate the proposed QPSOFL's efficacy.
In QPSOFL, an FL system determined the best next-hop CH based on descriptors, including relay distance, energy deviation, and residual energy. Extensive simulations were performed to compare QPSOFL's performance against existing protocols, including FL and PSO (FLPSOC), F-GWO, a fuzzy-based improved Harris’s hawk optimization algorithm (IHHO-F), and an enhanced fuzzy unequal clustering and routing protocol (E-FUCA), based on scalability, energy consumption, throughput, and network lifetime.
Importance of this Work
Energy-efficient clusters were effectively determined using a QPSO algorithm, employing Lévy flights, Gaussian perturbation, and Sobol sequences to expand the search space, mitigate local optima trapping, and enhance convergence speed. Additionally, balanced and energy-efficient relay CHs were realized using a Mamdani fuzzy inference system.
Results from the function test bench and simulations confirmed the effectiveness of the proposed protocol. The comparative analysis of QPSOFL, HHO, GWO, PSO, and QPSO validated QPSOFL's efficacy. Simulations displayed that QPSOFL substantially outperforms current and classical CRPs such as FLPSOC, F-GWO, IHHO-F, and E-FUCA.
Most importantly, the QPSOFL protocol significantly improved the key performance metrics, including scalability, network energy consumption, and network survival period. QPSOFL effectively reduced energy consumption, prolonged network lifetime, balanced energy distribution, and achieved higher throughput for data transmission to the BS.
The initial enhanced QPSO algorithm’s application for the optimal set of CH selection based on a carefully designed fitness function resulted in CHs with closer proximity to the BS and higher residual energy, which led to a substantial decrease in energy consumption. This factor, coupled with the selection of optimal relay nodes during the routing phase, collectively contributed to performance improvements in the QPSOFL protocol.
To summarize, the findings of this study demonstrated the feasibility of using the proposed energy-efficient CRP for WSNs.
Journal Reference
Hu, H., Fan, X., Wang, C. (2024). Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks. Scientific Reports, 14(1), 1-19. DOI: 10.1038/s41598-024-69360-0, https://www.nature.com/articles/s41598-024-69360-0
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