Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Node localization in Wireless Sensor Networks (WSNs) is essential for the practical deployment of networks for a variety of applications, from environmental monitoring to military surveillance. Accurate localization is vital for ensuring data integrity and the effective execution of spatially dependent tasks. Traditional localization methods such as trilateration, which rely on geometric principles to deduce node positions, often face accuracy issues in real-world scenarios due to signal attenuation and environmental obstructions. To address these challenges, a novel approach utilizing the Bounding Box method has been developed, which estimates node positions within a defined geometric space constrained by anchor nodes. However, this method encounters difficulties in environments with sparse nodes or large areas, leading to imprecise localization. To overcome these limitations, this paper presents a hybrid localization method that integrates the Bounding Box approach with the Harmony Search optimization algorithm, resulting in the Bounding Box Harmony Search (BBHS) method. The BBHS algorithm capitalizes on the global optimization ability of Harmony Search to refine initial estimates from the Bounding Box method, demonstrating significant improvements in reducing localization errors. Through simulations using our developed WSN localization simulator, the BBHS method is evaluated and shown to provide enhanced accuracy and robustness compared to traditional trilateration and the standalone Bounding Box method, even as network conditions vary. The simulator itself serves as a critical tool for network designers to visualize and optimize the performance of localization algorithms under controlled conditions. The advancements outlined in this paper suggest that hybrid methods like BBHS are at the forefront of localization technologies in WSNs. By merging geometric constraints with optimization processes, these methods not only rectify the drawbacks of earlier techniques but also pave the way for more reliable and resilient network operations.

One of the most critical problems in a communication system is losing information between the transmitter and the receiver. WiMAX (Worldwide Interoperability of Microwave Access) technology is gaining popularity and recognition as a Broadband Wireless Access (BWA) solution. At frequencies below 11 GHz, WiMAX can operate in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The implementation of WiMAX networks are rushed worldwide. Estimating path loss is crucial in the early stages of wireless network deployment and cell design. To anticipate propagation loss, several path loss models are available (e.g., Okumura Model Hata Model), but they are all bound by particular parameters. In this paper, we propose an MLP neural network-based path loss model with a well-structured implementation network design and grid search-based hyperparameter tuning. The proposed model optimally approximates mobile and base station path losses. Therefore, neurons number, learning rate, and hidden layers number are investigated to obtain the best model in terms of prediction accuracy. Path loss data is collected based on 14 networks in different microcellular settings. Simulations under Matlab environment showed that prediction errors were lower than standard log-distance-based path loss models.

Image compression is a critical area of research aimed at optimizing data storage and transmission while maintaining image quality. This paper explores the application of clustering techniques as a means to achieve efficient and high-quality image compression. We systematically analyze nine clustering methods: K-Means, BIRCH, Divisive Clustering, DBSCAN, OPTICS, Mean Shift, GMM, BGMM, and CLIQUE. Each technique is evaluated across a variety of parameters, including block size, number of clusters, and other method-specific attributes, to assess their impact on compression ratio and structural similarity index. The experimental results reveal significant differences in performance among the techniques. K-Means, Divisive Clustering, and CLIQUE emerge as reliable methods, balancing high compression ratios and excellent image quality. In contrast, techniques like Mean Shift, DBSCAN, and OPTICS demonstrate limitations, particularly in compression efficiency. Experimental validation using benchmark images from the CID22 dataset confirms the robustness and applicability of the proposed methods in diverse scenarios.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an energy optimization approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAV). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared to EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

In this paper, we present Atomic Energy Optimization (AEO), a novel meta-heuristic optimization technique inspired by atomic energy dynamics and the process of static electricity dissipation. AEO models optimization by mimicking the energy accumulation, transfer, and dissipation behaviors observed in atoms, particularly during processes involving electrostatic charge and discharge. Each solution in AEO is represented as an atom with electrons, where its energy state corresponds to solution quality. Solutions interact, exchanging and dissipating energy in a manner analogous to static electricity buildup and release—such as when a plastic rod is rubbed to accumulate charge, then gradually neutralized upon contact with another surface. This energy-driven model allows AEO to balance exploration and exploitation dynamically, enhancing its ability to navigate com-plex, multimodal landscapes. The paper delves into key AEO mechanisms, including energy transfer, dissipation, and parameter sensitivity, exploring their impact on the algorithm’s performance across challenging benchmarks. Notable applications of AEO to problems like the Rastrigin function and the Traveling Salesman Problem (TSP) showcase its effectiveness, with experimental results demonstrating superior convergence and robustness compared to traditional methods like Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Through extensive experimentation, AEO achieved up to a 20% faster convergence rate and a 15% improvement in solution quality over peer methods, demonstrating its superiority in solving benchmark optimization problems.