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Abstract:

Wireless Sensor Networks have become increasingly significant in the 21st century, enabling a wide spectrum of critical applications. However, energy efficiency remains a fundamental limitation affecting their scalability, reliability, and sustainability. This study introduces a novel framework that integrates Intelligent Dolphin Echolocation Optimization (IDEO) with Fuzzy C-Means clustering for adaptive Cluster Head (CH) selection. The proposed method employs a multi-criteria strategy considering residual energy, transmission distance to the Base Station (BS), and latency minimization, thereby ensuring efficient data aggregation in dynamic environments. Furthermore, an Energy-Conscious Cognitive Smart Routing Protocol (EC2-SRP) is developed to establish both energy-efficient and shortest routing paths, reducing communication overhead and extending network longevity. The novelty of this work lies in (i) the hybrid integration of IDEO with FCM for adaptive clustering, (ii) multi-criteria CH selection for enhanced energy efficiency, (iii) the design of a cognitive routing protocol balancing energy awareness with path optimality, and (iv) a comprehensive QoS-based evaluation. Simulation results demonstrate superiority over existing methods in terms of energy consumption, network lifetime, packet delivery ratio (PDR), End-to-End Delay, routing overhead, and Fault Tolerance, thereby achieving higher energy efficiency, reliability, and sustainability in Wireless Sensor Networks.

Keywords:

Clustering; Cross-layer routing; Fuzzy-C Means; Optimization; Wireless Sensor Network; Optimization,

References:

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Abstract:

Today's smart engineering systems are often faced with situations that are structurally uncertain, informationally incomplete, and non-probabilistically ambiguous, especially for electrical systems. ARDL models are limited in applications in complex computational environments where the uncertainty is due to vagueness, not randomness, and assume the exact parametric representation of the models and the structure of the stochastic uncertainty. This study proposes a new soft-computing paradigm using Fuzzy Autoregressive Distributed Lag (FARDL) models and compares the performance of the Linear Programming (LP) and Quadratic Programming (QP) estimation algorithms using large-scale parallel Monte Carlo simulations to overcome these drawbacks as well as fuzzy differential equations, especialy for electrical circuits and machines. In contrast to the previous works that mainly adopted the symmetric triangular fuzzy coefficients without any theoretical considerations, the proposed framework provides a mathematical foundation for fuzzy membership selection and examines the robustness of the estimators under symmetric triangular, asymmetric triangular, and trapezoidal fuzzy topologies. To evaluate the performance of the system, a Monte Carlo simulation framework is implemented under six sample sizes (T = 10, 15, 20, 30, 50, 100) and under different levels of structural complexity. The simulation results show that the QP method is always superior to the LP paradigm in terms of the estimation error of the center trajectory and the spread of uncertainty of the parameters in terms of Fuzzy Degree (FD). This is especially true in small sample situations, where the operational advantage is more pronounced, making it particularly useful for systemic modeling in data-sparse situations. Moreover, the proposed framework-based fuzzy differential equation offers a mathematically efficient tool to model mysterious engineering systems like network-based smart grids, control models, communication systems, and cyber-based frameworks. The combination of fuzzy dynamic approaches allows a reliable scheme and uncertainty quantification-based system for complex engineering environmental conditions, whereas deterministic schemes are becoming inadequate.

Keywords:

Computational Intelligence,Fuzzy ARDL,Linear Programming,Quadratic Programming,Monte Carlo Simulation,Optimization Geometry,Uncertainty Modeling,Electrical Circuits Uncertainty,Electrical Machines Uncertainty,Fuzzy Differential Equations (FDEs).,

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